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Enabling organizational use of artificial intelligence: an employee perspective
Abstract
Purpose
As artificial intelligence (AI) has become increasingly popular and accessible, most companies have recognized its far-reaching potential. However, despite numerous research papers on organizational adoption of new technologies including AI, little is known about individual employees’ intentions to use them. Given that organizational innovations are of limited value if they are not adopted by employees, the purpose of this study is to understand the underlying factors that push employees to make use of these new technologies in the workplace.
Design/methodology/approach
This study builds on previously developed technology acceptance models to provide a new theoretical model. The model is then tested using data collected from a survey of 203 employees and analyzed through structural equation modeling.
Findings
Findings show that five factors affect employees’ intention to use AI either directly or as mediators. Organizational culture and habit exert a positive impact on employees’ intention to use AI, whereas job insecurity has a negative impact. Perceived self-image and perceived usefulness fully mediate the relation between job insecurity and intention to use. Moreover, perceived self-image and perceived usefulness partially mediate the relationship between habit and intention to use.
Originality/value
To the best of the authors’ knowledge, this study is among the first to determine the factors that influence employees’ intention to use AI in general and more particularly chatbots within the workplace.
... The TOE framework's applicability to the research of AI adoption is also supported by another study (Radhakrishnan et al. 2022) that discovered organizational elements affecting the adoption of AI, including corporate culture, strategic roadmaps, senior management support, and the availability of trained personnel. Other findings show that organizational culture, personal habits, and job insecurity influence employees' intention to use AI (Dabbous et al. 2022). They found that the relationship between job insecurity and intention to use is fully mediated by perceived self-image and perceived usefulness, and the relationship between habit and intention to use is partially mediated by them. ...
... GAI-experienced interviewee's preference to use it in private life emphasizes compatibility issues, mirroring the extension of UTAUT (Blunt et al. 2022). They felt that while GAI cannot currently replace humans at work, its rapid development and unpredictability raise concerns, touching on job insecurity (Dabbous et al. 2022). These support a study where the top three reasons against using GAI tools were perceived risk, language barrier, and technological anxiety (Pillai et al. 2024). ...
... Social influence also remained weak as the management was encouraging but not prescribing GAI usage. All these factors lead to a very limited primary appraisal that negatively influences behavioral options (secondary appraisal), reducing the chances of developing a habit of using GAI for work (Blunt et al. 2022;Dabbous et al. 2022). ...
Millions have adopted tools like ChatGPT in recent years, yet indifference and resistance among employees remain. This qualitative study employs monodramatic projective techniques to explore employees' hidden assumptions and unconscious beliefs in a division attempting to integrate Generative Artificial Intelligence (AI, GAI). Through pretensive work, soliloquy, symbolic representation, modeling with intermediate objects, concretization, and role reversal techniques, the interviewees' internal representations of GAI and trust were materialized in physical artifacts, such as a ball of straw or a potted plant. The study identified three principal themes: GAI's appearance as a Janus-faced presence, unmet performance promises, and avoided proximity. Findings highlight ambiguities in acceptance and show that adoption was driven more by industry hype and normative pressures than genuine organizational needs, leading to disorganized implementation dependent on individual employee characteristics, mistrust, and disenchantment. The study's main contribution lies in refining human-robot interaction (HRI) models and psychodrama methods for GAI, emphasizing the significance of physicality and embodiment in technology-mediated relationships, identifying trust as a complex phenomenon with potential reciprocal causation, and emphasizing the importance of affective attitudes, illustrating how adoption projects can falter despite cognitive openness – all insights crucial for understanding self-driven, bottom-up GAI adaptation in an organizational context.
... Furthermore, it is also observed that AI may lead to attitudinal and behavioral outcomes, e.g., job satisfaction, career prospects, and turnover intentions (Chen et al., 2023;Kong et al., 2021). One important aspect is whether AI is perceived positively or negatively for the career outcomes of employees (Chuang et al., 2022;Dabbous et al., 2022;Mena-Guacas et al., 2023). Additionally, previous studies lack depth and do not explain how AI may influence career outcomes, e.g., adaptability. ...
... For instance, Ahn and Chen (2022) carried out their research on Public Administration and observed that AI adoption is only possible when the employees are willing to adopt that, while the adoption is dependent upon their familiarity with technology and the cost/benefits associated with its adoption. They also found that fear of job loss is present, which leads to the creation of negative feelings about AI. Dabbous et al. (2022) focused on ways of adopting AI in the workplace. They found that five important factors influence its adoption: organizational culture, habit, perceived self-image, perceived usefulness, and job insecurity. ...
... Ahn and Chen's (2022) study on Public Administration also identified that employees perceive AI negatively because they fear that AI may take away their jobs. Dabbous et al. (2022) observed that feeling fear of AI is natural as employees feel this due to their experience, personality, and knowledge about AI. Zhou et al. (2013) highlighted the dark side of AI-based working in organizations and identified that employees feel that AI might work better and can reduce their value to an organization. ...
... This integration of AI can profoundly impact an enterprise's ability to be agile, enabling faster and more accurate decision-making, efficient resource allocation, and improved responsiveness to customer needs (Wijayati et al., 2022). The introduction of artificial intelligence in company operations can help unlock new levels of agility by optimizing operational efficiency, accelerating product development cycles, and enabling predictive capabilities (Dabbous et al., 2022). This synergy between agility and AI empowers enterprises to navigate complex and uncertain business environments with greater resilience and adaptability. ...
... The questionnaire was designed to incorporate statements related to specific concepts, and participants were asked to indicate their level of agreement using a 5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The items for the construct factors for the transition to an agile approach were adopted from Ajgaonkar et al. (2021), items for the construct AI-supported organizational culture were adopted from Dabbous et al. (2022), items for the construct AI-enabled workload reduction were adopted from Qiu et al. (2022), and items for the construct AI-enabled performance enhancement were adopted from Wijayati et al. (2022). ...
... Our results indicate that large companies possess a more supportive organizational culture for AI integration than small companies. This is consistent with findings from Dabbous et al. (2022), highlighting the importance of organizational culture in AI adoption. Additionally, large enterprises exhibit a higher level of agreement compared to small enterprises when it comes to fostering a culture that values and rewards innovation and experimentation with AI. ...
This article presents the findings of a survey conducted in Slovenia, encompassing a random sample of 275 enterprises, to analyze the factors influencing the transition to an agile approach, the AI-supported organizational culture, AI-enabled workload reduction, and AI-enabled performance enhancement in small and large enterprises. The study investigates whether there are statistically significant differences between small and large enterprises in Slovenia regarding these aspects. These findings provide valuable insights into the distinct perspectives and priorities of small and large enterprises in Slovenia regarding agility and the adoption of AI technologies. The results highlight areas where small businesses may need additional support or targeted strategies to fully leverage the benefits of agility and AI. Policymakers and industry leaders can utilize these findings to promote tailored approaches that enhance agility and facilitate effective AI integration in both small and large enterprises, ultimately contributing to the growth and competitiveness of the Slovenian business landscape.
... While AI may be perceived as a potential threat that could replace human jobs, on the other hand it also offers opportunities for job performance improvement. AI systems can provide timely and accurate information, facilitate data analysis (Dabbous, Aoun Barakat, & Merhej Sayegh, 2022), and assist in the decisionmaking process (Bader & Kaiser, 2019), which is one of its most significant applications (G. Cao, Duan, Edwards, & Dwivedi, 2021). ...
... On such grounds, (Dabbous et al., 2022) extended the technology acceptance literature by proposing a behavioral model which takes into account the peculiarities of AI. This study combines TAM and the TRA, in the context of developing country, and argues that when explaining the intention to use a new technology, it is important to integrate organizational, social, and individual factors. ...
... This model, with the aim to explain employees' intention to use AI, is based on five factors: perceived usefulness, job insecurity, self-image, habit and organizational culture. (Dabbous et al., 2022) excluded variable named ease of use, which could be considered in further research. Job insecurity, as defined by (Tsakonas & Papatheodorou, 2008) is a "perceived threat of job loss and the worries related to that threat". ...
In the last few years, the rapid development of Artificial Intelligence (AI) has created the conditions for its increasing use in various organizations, in order to achieve the well-known goals of increasing productivity, efficiency, effectiveness and more rational use of resources. However, most companies have difficulties in implementing artificial intelligence and realizing the benefits it brings. Most of the researchers in this field, in order to examine in more detail which factors influence the adoption of new technologies in the organization and what their mutual relationship is, uses previously well-developed models such as TAM (Technology acceptance Model), UTAUT (The Unified Theory of Acceptance and Use of Technology) and TOE (TechnologyOrganization-Environment). Through a more detailed review of the literature, this paper provides a framework overview of the factors. The article highlights the insufficient focus of previous studies on the factors related to the intention of employees to use artificial intelligence in everyday business tasks, that is, it proposes a framework for further research in this area with special attention to the intention of employees to use AI. Our results can help scholars and practitioners to include those factors in further theory development.
... As a result, new thinking about organizations and the willingness of managers to adopt AI in the workplace. According Dabbous, Barakat and Sayegh [17] , Healthcare, retail, manufacturing, and other digital technology industries, such as AI, deem it essential. AI is transforming human resource management by introducing new capabilities and reshaping the way the field is currently conducted [10].However, AI helps HR managers make decisions and predict how employees will act at work. ...
... Individuals obtain confidence with the PE that technology helps them boost productivity and accomplish the activity swiftly [17]. using AI in the organization could lead to enhancement the decision-making [9] and performance [25]; those are all related to performance expectations in general. ...
... Statistically, PE significantly impact on AI acceptance intention [17]. similarly, PE statistically important to improve the BI to use AI [11]. ...
The purpose of this study is to examine the measure the Behavioral intentions (BI) to use artificial intelligence (AI) among managers in small and medium enterprises. the targets population of this study was the SMEs managers in Baghdad City after ensuring that the managers were using some form of AI. 184 valid questionnaires have been analyzed by Smart-PLS. The results indicated that performance expectancy (PE), Social influence (SI), Facilitating Conditions (FC), and Top management support (TMS) have a positive and significant impact on behavioral intention to use AI among the managers in SMEs; on the other hand, the effort expectancy (EE) has an insignificant impact on behavioral intention to use AI among the managers.
... This transformation involves adapting to the unique characteristics and ever-evolving digital technologies, which in turn requires a distinct approach to organizational structure and operations. To achieve this, organizations must undergo a cultural shift that embraces change and innovation (Dabbous et al., 2022). ...
... Therefore, it is important to create a clear strategy for the digitization of business and define the company's goals. Employees need to be shown the benefits and advantages of a different way of working and the use of new solutions to become familiar with the benefits of changes in the field of artificial intelligence (Dabbous et al., 2022). Companies must recognize that artificial intelligence is primarily a business challenge that demands changes in organizational culture, personnel, business processes, and business models (Munir et al., 2022). ...
... Solving business challenges using artificial intelligence should not be limited to independent business units but should be integrated into the entire company (Dabbous et al., 2022). The adoption of artificial intelligence requires technical changes, such as building data ecosystems, and depends on trust in artificial intelligence and its integration into business workflows. ...
The paper’s main aim is to analyze five constructs of organizational culture, AI-supported leadership, AI-supported appropriate training of employees, teams’ effective performance, and employee engagement, and their relationship through the prism of artificial intelligence on a sample of large and medium-sized Slovenian companies. The second aim of the paper is to test the proposed model with two different statistical techniques in the scope of structural equation modeling (SEM) that enable us to assess linear (PLS-SEM) and non-linear relationships (CB-SEM) among the constructs. The empirical research included 437 medium-sized and large Slovenian companies. From each company, a CEO or owner participated in our research. The findings of the research with both techniques show that organizational culture had no impact on AI-supported appropriate training of employees and was not significant as well as that organizational culture had an impact on AI-supported leadership. The impact of AI-supported leadership on AI-supported appropriate training of employees were supported only for the PLS-SEM model. The impact of AI-supported leadership for employees on teams was positive. Contrary to that, the impact of AI-supported leadership for business solutions on teams was non-significant. In both cases, AI-supported appropriate training of employees’ impact on teams was strong and positive. Also, employee engagement impact on teams was positive and statistically significant with PLS-SEM and CB-SEM methods. The research yields important implications for companies seeking to integrate artificial intelligence effectively in their operations. It emphasizes the critical role of AI-supported leadership in driving positive outcomes, such as improved employee training and enhanced team effectiveness. Companies should focus on developing leaders who can leverage AI tools to foster a skilled and engaged workforce. By adopting data-driven decision-making processes and incorporating insights from structural equation modeling, organizations can develop effective AI integration strategies. These provide valuable guidance for enhancing human resource management practices and achieving successful AI adoption across companies. The findings contribute to the formation of new views in the field of artificial intelligence implementation in the companies and show companies a broader picture of which aspects of human resource management need to be improved.
... Through enhancing operational efficiency, improving product development cycles, and giving predictive capabilities, artificial intelligence can help organizations accomplish new levels of organizational agility. (Dabbous et al., 2022). Through an analysis of how AItechnologies improve organizational agility, organizations may strategically coordinate their adoption efforts with programs that promote organizational agility, encouraging a comprehensive strategy for AI-driven knowledge (Peeters et al., 2022;Papadopoulos et al., 2020;Klein & Todesco, 2021). ...
... AI-enhanced task automation may significantly boost an organization's agility by facilitating more efficient resource allocation, faster and more precise making decisions, and improved customer responsiveness (Wijayati et al., 2022). By increasing operational efficiency in retail sector speeding up product development cycles, and allowing predictive capabilities, the integration of AI-knowledge & tools into business tasks can help firms to achieve new levels of organizational agility (Dabbous et al., 2022). Through an analysis of how the use AItask automation tools & technologies enhance & improve organizational agility, organizations may consistently plan out their adoption strategy with different programs that prioritize organizational agility, promoting a comprehensive strategy for retail industry (Peeters et al., 2022;Papadopoulos et al., 2020;Klein & Todesco, 2021). ...
Artificial Intelligence (AI) technologies are being quickly integrated into the retail sector to improve staff flexibility and expedite operations in response to changing market needs. This study examines how work automation and AI-driven information accessibility might enhance employee adaptable performance through organizational agility. The study highlights how AI technologies promote real-time decision-making, eliminate repetitive processes, and empower staff to successfully adapt to changing situations. It is based on the Dynamic Capabilities Theory. Survey information from 163 individuals at various hierarchical levels was examined using a structural equation modeling technique. The results show that job automation and AI-driven information accessibility improve organizational agility, which in turn greatly increases worker flexibility. In addition to streamlining processes, AI systems give staff members useful information that encourages innovation and problem-solving skills.The report emphasizes how crucial organizational agility is strategically as a mediator in converting AI efficiency into better worker performance. To stay competitive in a changing industry, retail managers are urged to give AI-driven innovations top priority. Policymakers, business executives, and tech developers may use this research's practical findings to improve worker performance and organizational resilience by utilizing AI technologies.The article offers useful advice for legislators and retail executives, highlighting the necessity of making calculated investments in staff development, AI tools, and agility-driven operational frameworks. Workflows are streamlined by AI technologies, but their effectiveness relies on the organizational flexibility that permits a smooth integration into regular operations.
... Previous research highlights the role of organizational innovativeness in technology adoption. For example, Roslan, Nasharuddin, and Murad (2024) demonstrated that innovative retail firms are more proactive in adopting AI to personalize customer experiences, thus fostering loyalty (Dabbous, Aoun Barakat, & Merhej Sayegh, 2022). Similarly, Yang and Gao (2023) found that retail businesses with a culture of innovation are more inclined to invest in AI tools to improve customer retention (Dabbous et al., 2022). ...
... For example, Roslan, Nasharuddin, and Murad (2024) demonstrated that innovative retail firms are more proactive in adopting AI to personalize customer experiences, thus fostering loyalty (Dabbous, Aoun Barakat, & Merhej Sayegh, 2022). Similarly, Yang and Gao (2023) found that retail businesses with a culture of innovation are more inclined to invest in AI tools to improve customer retention (Dabbous et al., 2022). Despite these findings, limited studies focus on how organizational innovativeness impacts AI adoption in Bangladesh, particularly in the retail context. ...
Purpose: This study investigates the factors influencing retail firms' intentions to adopt Artificial Intelligence (AI) to enhance customer retention and loyalty in Dhaka, Bangladesh. The research focuses on examining how perceived usefulness, perceived ease of use, competitive pressure, technological readiness, and organizational innovativeness influence retail entrepreneurs' adoption of AI as a strategic tool for customer engagement. Research Methodology: A quantitative research design was employed, incorporating a hypothetical-deductive approach. The study utilized a cross-sectional design, drawing a sample of 250 retail firms through stratified random sampling in Dhaka. Data were collected using structured questionnaires and analyzed using statistical techniques to assess the relationships between the variables. Results: The study identified that all five factors perceived usefulness, perceived ease of use, competitive pressure, technological readiness, and organizational innovativeness positively and significantly influence retail entrepreneurs' intentions to adopt AI. These findings emphasize the crucial role of both technological and organizational dynamics in driving AI adoption decisions within the retail sector. Limitations: The research is geographically confined to retail firms in Dhaka, which may limit the generalizability of the findings to other regions or countries. Furthermore, the study's cross-sectional design restricts the ability to monitor AI adoption trends over time, indicating that future research could benefit from employing longitudinal designs and encompassing a broader geographical scope. Contribution: This study provides valuable insights for retail managers and entrepreneurs seeking to leverage AI to enhance customer loyalty. It underscores the importance of fostering technological readiness and cultivating a culture of innovation within retail firms. The research contributes to the expanding body of knowledge on AI adoption in emerging markets, particularly concerning customer retention strategies in the retail sector.
... For instance, the measurement items for SI and FC were adapted from Naranjo-Zolotov et al. (2019), while those for behavioral intention, PV, and PRE were adapted from . The measurement items for job insecurity and resistance to change were adapted from 9 Dabbous et al. (2021) and White et al. (2020), respectively. A 5-point to 7-point Likert scale was applied throughout to gauge the statements that needed scaling, with the respondents being asked to indicate the extent to which they agreed or disagreed with the statement. ...
... -Job Insecurity (JI): Measured using items from Dabbous et al. (2021), capturing the anxiety associated with potential job loss due to new technology adoption. ...
Aim/Purpose: This research investigates factors influencing consumers’ decisions to use artificial intelligence cybersecurity technology in the United Arab Emirates. Background: The cyber-security risks are getting more complex as technology develops, putting the United Arab Emirates (UAE) businesses and government agencies at risk of severe losses from cybercrime. Methodology: A correlational study design and a quantitative research approach were employed, and 340 professionals working for different government and semi-government organizations in the United Arab Emirates were given questionnaires. The PLS-SEM approach was used to analyze the replies. Contribution: The present research framework remedies the inherent limitations in the PMT model by adding factors used to explain the influence of environmental factors and individual difference factors on behavior. This research framework is an extended application of the PMT model in the context of AI-based cybersecurity systems. Meanwhile, this study confirms the importance of perceived vulnerability in AI technology scenarios. Findings: The findings demonstrated that users’ adoption intentions were significantly and favorably impacted by social influence, facilitating conditions, perceived vulnerability, and perceived response efficacy. Meanwhile, job insecurity enhanced employees’ resistance to change, making resistance to change a major resistance to the intention to adopt AI-based cybersecurity systems. Recommendations for Practitioners: The report offers crucial insights that organizations can utilize to evaluate their readiness for adopting AI-based cybersecurity technologies and create plans to lessen employee resistance to advancements in the cybersecurity industry. Recommendation for Researchers: Researchers on this specific application can make use of the extension of the framework. Impact on Society: The research can be utilized to evaluate their readiness to adopt AI-based cybersecurity technologies. Future Research: Future research should broaden the scope to acquire a more thorough understanding of the behavioral intentions to use AI-cybersecurity systems in the United Arab Emirates. Other elements that could be considered include facilitating settings, Artificial Intelligence knowledge, social impact, effort efficacy, and other frameworks.
... Artificial intelligence (AI) algorithms possess the capability to efficiently and accurately handle substantial amounts of data, thereby reducing the occurrence of human errors and improving the overall quality of financial reporting (Nickel, 2022). In addition, artificial intelligence (AI) has the potential to improve operational effectiveness by automating labor-intensive processes, thereby allowing accountants to allocate their efforts towards more valuable endeavors such as analyzing data, making strategic decisions, and providing advisory services to clients (Dabbous et al., 2022). AI-powered tools have the potential to enhance fraud detection capabilities by effectively identifying and flagging suspicious patterns and transactions that may pose difficulties when detected through manual means (Coman et al., 2022). ...
... Organizing, Managing and Controlling in the Information Age (pp. [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Springer International Publishing. ...
The aim of this research is to examine and assess the mediator role of information technology (IT) in the amalgamation of artificial intelligence (AI) and contemporary accounting methodologies. The present study employed a quantitative research design to investigate the mediating role of information technology in the relationship between artificial intelligence (AI) and contemporary accounting practices. The study aims to include a sample size of 138 participants from different private businesses in Erbil. The findings revealed that the reliability of information technology mediates the relationship between artificial intelligence and modern accounting, accordingly, the first research hypothesis was supported. Moreover, it was found that the consistency of information technology mediates the relationship between artificial intelligence and modern accounting, accordingly, the second research hypothesis was supported. Lastly, it was found that the relevance of information technology mediates the relationship between artificial intelligence and modern accounting, 1313 accordingly, the third research hypothesis was supported. In order to effectively leverage the capabilities of artificial intelligence (AI) in contemporary accounting practices, it is imperative to establish a strong collaborative relationship between the information technology (IT) and accounting departments.
... Human-AI collaboration (HAIC) has emerged as a new paradigm in which AI flexibly adapts to its human counterparts, e.g. by processing data as well as giving recommendations and making decisions. The integration of AI facilitates a shift toward more interactive and independent forms of AI, thereby enhancing productivity and decision-making processes (Dabbous et al., 2022). ...
... This concern not only leads to operational challenges but also raises human resource management issues (Park et al., 2021). These challenges primarily arise in areas where AI-powered automation is likely to replace many routine jobs, although it may also create new roles that require creative and cognitive functions (Dabbous et al., 2022;Kar et al., 2021). To alleviate these fears, it is suggested that organizations adopt human-centered AI approaches, which aim to augment human capabilities rather than replace them (Shneiderman, 2020;Xu et al., 2023). ...
Though human-AI collaboration (HAIC) is increasing, significant challenges persist in its effective adoption. Based on a systematic literature review, we propose a framework encompassing 15 obstacles to the adoption of HAIC in organizations, organized into three components of competence learning: knowledge, skills, and attitudes. Important obstacles include the lack of an AI strategy, limited understanding of AI responses, a technology readiness gap, cultural adoption resistance, as well as ethical and privacy concerns. We apply our framework to a case study of an international automotive OEM. 14 semi-structured interviews with executives provide further insights, such as categorizing the lack of technical readiness into over-reliance on human interaction, digital mindset, fear of AI, and operational readiness. Our framework provides a comprehensive overview of relevant obstacles to HAIC adoption and can help to develop effective strategies for integrating AI, leading to improved productivity and decision-making processes in organizations.
... Neural networks are a special class of algorithms within the framework of artificial intelligence, which is based on mimicking the work of the human brain. These networks consist of layers, or "neurons," that are interconnected and can process data in a complex, multi-level process (Dabbous et al., 2022). The use of neural networks has demonstrably improved AI's ability to process speech, recognise images, predict weather conditions, and even help with medical diagnostics. ...
This research examines the perspectives and challenges of artificial intelligence (AI) implementation in small and medium-sized enterprises (SMEs). Through analysis of academic literature, industry reports, and survey data from 63 companies, the study investigates the potential applications, benefits, and barriers to AI adoption among SMEs. The findings reveal that while AI offers significant opportunities for SMEs in areas such as process automation, data analytics, customer experience personalisation, and operational optimisation, adoption rates remain low. The research identifies several key barriers, including limited access to industry data, insufficient financial resources, lack of technical expertise, and challenges with data integration. Survey results indicate that only 13% of surveyed companies have experience working with AI, despite widespread use of basic information management systems. The study highlights five primary areas where generative AI can enhance SME performance: content creation, automated operations, venture business ideation, financial management, and operational optimisation. The conclusions emphasise the need for targeted support mechanisms, improved educational programmes, and policy frameworks to facilitate AI adoption among SMEs. This research contributes to understanding the role of AI in SME development and provides practical insights for business leaders, policymakers, and researchers working to enhance AI integration in small and medium-sized businesses.
... For instance, a study done with 203 employees from multiple businesses by Dabbous et al. (2022) found that organizational culture and regular use were strong motivators of AI adoption, while job insecurity inhibited the adoption. A qualitative study on AI adoption in recruitment showed that chatbots were reliable and efficient, but privacy concerns and reduced human engagement remained (Rukadikar & Khandelwal, 2024). ...
Artificial intelligence (AI) mediated communication tools supported collaboration, maintained production and even provided emotional support during remote work. Employees thereby expected workplace engagement to rely more heavily on AI-driven efficiency. Although COVID-19 did not lead to the creation of AI, the COVID-19 pandemic did drive AI to the vanguard of corporate operations through the growth of digital innovation. This research explored workplace reintegration and AI adoption using a qualitative approach to understand employee experience of AI adoption and workplace reintegration. In-depth interviews with employees who had experienced this transition were the data source. The AI dependency, how to develop interpersonal skills, and where leadership lies in managing the balance between AI and human-centered work culture were thematically analyzed to determine the main patterns and concerns. The findings show that many employees struggled with face-to-face interaction because they were used to the assistance of AI in workplace communication and decision-making, which used to be effective. A psychological and behavioral change was needed for the transition to in-person collaboration. While AI chatbots helped employees schedule, troubleshoot, and get answers to their questions more expeditiously than in the traditional workplace, traditional workplace where interactions are now slower and less efficient.
... While Artificial intelligence may have a deleterious impact on the job market overall, AI also has the potential to reduce workloads and enhance worker performance, suggesting a significant positive relationship between AI integration and employee satisfaction and productivity [49]. However, as highlighted by Dabbous et al., the successful implementation of AI technologies hinges on the willingness of employees to adopt these innovations, underscoring the need for leadership that emphasizes training, awareness, and supportive workplace cultures [50]. ...
Artificial Intelligence (AI) is increasingly recognized as a disruptive technology with profound potential to reshape complete sectors of our economy and the way we live and work. The present study investigates global public perceptions regarding the risks associated with AI technology in the early to mid-2020s, utilizing data from the Munich Security Index spanning 2022 to 2025 across G7 and BICS nations. Initial findings indicate that while AI risk perception is steadily rising in G7 countries—reflecting concerns about job displacement and ethical implications—public sentiment in BICS nations presents a more complex picture, influenced by varying socio-economic factors and cultural contexts. The study emphasizes the critical need for organizations to address public anxieties through transparent communication and engagement, ensuring that AI integration is managed ethically and responsibly. By promoting public AI literacy and fostering informed dialogues, stakeholders can better navigate the challenges posed by this rapidly evolving technology.
... In the 21st century, artificial intelligence (AI) has significantly impacted online commerce platforms and various industries (Dabbous et al., 2022;Boustani, 2022). AI-powered chatbots have garnered interest and assist users in customer service, e-commerce and online platforms (Adamopoulou and Moussiades, 2020). ...
Purpose
Anchoring on the social exchange theory (SET), this study aims to examine the impact of artificial intelligence (AI) on customer behavior within the e-commerce sector. This study investigates the interconnections between perceived benefits of AI, customer trust, customer satisfaction and electronic word of mouth (eWOM).
Design/methodology/approach
A mixed-methods approach was used in the Vietnamese e-commerce context. A quantitative survey of 291 respondents was conducted to examine the proposed relationships, while qualitative research involving semistructured interviews with 10 participants provided deeper insights. Thematic analysis of the interview data enriched and validated the quantitative findings, offering a holistic understanding of the phenomena.
Findings
The quantitative analysis indicates that perceived benefits of AI do not directly affect eWOM; however, they significantly influence eWOM indirectly via trust and customer satisfaction, which serve as full mediators. The qualitative findings reveal three primary themes: the impact of AI’s perceived advantages on trust and satisfaction, the indirect connection between AI’s benefits and eWOM and the mediating functions of trust and satisfaction within this framework.
Originality/value
This research addresses a gap in existing literature by analyzing the influence of AI on consumer behavior in a developing market, specifically focusing on Vietnam as a case study. The integration of quantitative and qualitative methods provides a comprehensive framework for analyzing AI-mediated customer interactions. The findings contribute to the theoretical framework of SET within the realm of AI, while also offering practical insights for businesses and policymakers. They emphasize strategies aimed at improving customer trust, satisfaction and eWOM in the context of e-commerce through the application of AI.
... elements, providing a more comprehensive understanding of how technical readiness influences human adoption and vice versa. What's more, organizational culture was also an important theme (e.g., Dabbous et al. 2022;Lichtenthaler 2020). The role of organizational culture in shaping employees' attitudes towards AI is a well-established theme in the literature, with studies highlighting the importance of experimentation and risk-taking in promoting AI adoption (e.g., Polas et al. 2022;Teece 2018). ...
This paper presents findings from a qualitative study that explored the factors affecting start‐up employees' adoption of AI technology for innovation ecosystems. Using thematic analysis based on interviews with 35 employees from start‐ups, we employed a grounded theory approach due to the newness of the context and the limited prior research. Our analysis identified several key themes, including perceived benefits of AI, technical considerations, organizational culture and values, employee attitudes and beliefs, access to resources, legal and ethical considerations, and industry‐specific factors. We found that technical considerations, such as data quality and technical infrastructure, played a significant role in determining AI adoption. Additionally, organizational culture and values were important in shaping employees' attitudes towards AI. Our study highlights the importance of addressing both technical and cultural factors when promoting the adoption of AI technology for innovation ecosystems in start‐ups. Our findings provide valuable insights for start‐up managers, policy makers, and researchers seeking to better understand the factors influencing AI adoption in this context.
... Understanding the tasks and AI Models related to perceived usefulness is crucial for enhancing the intention to engage in AI to achieve equitable outcomes (Shih-Yeh et al., 2024). Understanding the tasks and AI Models related to human tasks is crucial for comprehending how these factors influence employees' willingness to adopt an AI environment by developing a bottom-up vision of how to use and absorb AI technologies that include the use of AI within the workplace (Dabbous et al., 2022). AI will significantly and broadly shape future work environments to increase workplace creativity and productivity, where AI can fundamentally alter how people interact, learn, and work. ...
This nonexperimental survey-based online quantitative study was conducted to explore how an independent variable, human tasks, affects the dependent variable, AL Models percentage of tasks within the job role influenced by artificial intelligence (AI). The Technology Acceptance Model (TAM) theoretical framework is used to understand and predict dependent variable Al Models, which refers to the degree to which Artificial Intelligence AI will transform daily tasks. The study addresses the existing research gap by exploring areas that have not been sufficiently investigated and understood to improve human tasks and effectively fill a knowledge gap regarding the dynamics of human tasks that AI influences. AI Models in this study are impacted by how human tasks are integrated, representing AI's influence on the job. Different AI Models that include machine learning algorithms, deep learning, or rule-based systems may vary and be influenced by Human Tasks. In the online survey, participants were chosen from nearly every industry that has used AI systems to help with task automation and workload distribution, which makes them perfect subjects for assessing how beneficial and effective people think of Artificial Intelligence in practical situations.
... In this context, many theoretical models have been established in the literature recently in the fields of IS, psychology, and sociology. Among these models, several studies supported the TAM [18,37] in their study. ...
This study pursues dual objectives; firstly, to scrutinize the determinants critical to AI’s sustained utilization within banking and secondly, to scrutinize the intermediary role of technological knowledge amidst the factors of technology adaptation and continued usage intention. A survey engaging bank professionals who routinely employ AI for risk and fraud assessment was conducted. The data was analyzed using SmartPLS. 4 in two stages using structural equation modeling (SEM) and artificial neural network (ANN). The study proposes a hierarchical model showing that the perceived ease of use has a significant positive influence on the attitude toward the use of technology, but holds no direct significance on continued usage intention for artificial intelligence. The results are further validated using artificial neural network analysis. In light of these insights, bank policy strategists are better equipped to tailor approaches to navigate the structural and regulatory impediments to the AI adoption process.
... The application of AI in performance evaluation requires support and readiness from the entire organization. A culture that encourages acceptance of technological change is also essential to ensure the successful implementation of AI in performance evaluation (Dabbous et al., 2021). Building understanding and a positive spirit among organizational members is an essential step in creating a supportive environment for the use of AI technologies. ...
Employee performance evaluation is a crucial process in human resource management. It measures an individual's contribution to organizational goals. However, traditional evaluation methods face obstacles like subjective bias, inefficiency, and lack of objectivity. Artificial Intelligence (AI) technology offers a promising solution. This paper discusses AI's implementation as an evaluation tool and its impact on human resource development. Previous research shows that AI improves objectivity, fairness, and efficiency in appraisal. It accurately identifies employee potential, aiding targeted development programs. However, research gaps remain, such as AI's use in different industries and ethical concerns affecting employees and organizational culture. This study aims to investigate AI's use in various industry contexts, understand ethical and trust aspects, and analyze its impact on employees and organizational culture. The results will provide valuable insights into AI's benefits in performance evaluation, benefiting human resource development and improving the evaluation process. Organizational understanding of AI's challenges and benefits in human resource development can enhance overall productivity and performance.
... Employees often work in fear of job loss due to external factors, such as the use of AI, because they believe this automation threatens their careers by leading to job replacement (Tschang & Almirall, 2021). Past literature supports this argument, highlighting the fear of automation and job insecurity in the health sector among nurses (Dabbous et al., 2022), and detailing how their jobs are at risk of being replaced by machines. Another literature also indicates that AI tools bring negative changes in the behavior of the employee (Dospinescu & Dospinescu, 2020;Schlögl et al., 2019). ...
Drawing on self-determination theory (SDT), this research examines how the utilization of artificial intelligence (AI) in hospitality organizations is influencing employee work and career outcomes (well-being and career success). We explore the underlying role of job insecurity in the associations of AI use with employee wellbeing and career success. We further explore the boundary condition effect of technostress in our proposed model. This study used a three-wave online survey approach to collect data from 277 workers of fast-food chain restaurants in the People's Republic of China. We used Smart PLS 4 to analyze our data. The results showed a positive relationship among AI use and employee well-being, but not with career success. Job insecurity mediates the relationship between AI use and employee outcomes. Additionally, technostress moderated the associations between AI use and employee well-being and career success. This study contributes to the theory and practice in the field of AI and hospitality industry and helps understand the nexus between increasing use of AI and employees' work and career outcomes.
... Researchers have looked at how AI affects sustainable business models in the agri-food sector, and it is crucial that stakeholders know how AI works in supply chain management [56]. A number of studies have looked at AI from the viewpoint of the organization's employees, drawing attention to the need to learn what makes workers want to embrace new tech at work [57]. New studies have looked at how different HR functions inside companies affect HR professionals' views on AI adoption and how factors like performance expectations, backing from upper management, and competitive pressures influence HRM's decision to use AI [58]. ...
This paper examines the key factors recognized as transformative in the field of human resource management (HRM) and explores their influence on the global adoption of artificial intelligence (AI). While AI holds significant promise for enhancing HRM efficiency, employee engagement, and Decision Making, its implementation presents a range of organizational, technical, and ethical challenges that organizations worldwide must navigate. Change aversion, data security worries, and integration expenses are major roadblocks, but strong digital leadership, company culture, and advancements in NLP and machine learning are key enablers. This paper presents a complex analysis that questions the common perception of AI as only disruptive by delving into the relationship between power dynamics, corporate culture, and technology infrastructures. In this paper, we bring together research from several fields to help scholars and practitioners understand the nuances of AI adoption in HRM, with an emphasis on the importance of inclusive methods and ethical frameworks.
... Artificial intelligence, conversely, refers to developing computer systems that can perform tasks that typically require human intelligence, such as natural language processing, image recognition and decision-making (Chedrawi and Atallah, 2022;Saeed et al., 2023). Artificial intelligence algorithms, including machine learning, deep learning and neural networks, enable computers to learn from data, identify patterns and make predictions without explicit programming (Dabbous et al., 2022;Saeed et al., 2023). Thus, we posit that combining big data analytics with artificial intelligence enables MSFBs to extract meaningful insights from large datasets, uncover hidden patterns and automate the decision-making process. ...
Purpose
This study examines the impact of entrepreneurial leadership (EL) on Chinese micro and small family businesses’ (MSFBs) innovativeness. Drawing on the resource-based view, this research study further explores the intermediary roles of proactive personality (PP) and affective commitment (AC) between ELs’ and MSFBs’ innovativeness. Besides this, the present work proposes a novel contingency impact of big data-powered artificial intelligence (BDAI) between EL, PP and AC, which indirectly spurs MSFBs’ innovativeness.
Design/methodology/approach
This study proposed a moderated mediation model using multi-wave, multi-source, time-lagged datasets of 380 employees from 190 Chinese MSFBs. We tested our hypotheses using structural equation modeling through the PLS technique.
Findings
The findings reveal a significant impact of EL on MSFB innovativeness, underscoring the pivotal intermediary roles of EL in driving MSFB innovativeness. Furthermore, BDAI emerges as a critical contingency factor, amplifying the effects of EL on both PP and AC to spur MSFBs’ innovativeness.
Practical implications
Our research offers several practical implications for Chinese MSFBs aiming to enhance innovativeness and competitive advantage. Firstly, understanding the direct impact of EL on MSFBs’ innovativeness provides valuable guidance for MSFB leaders. Secondly, recognizing the mediating roles of PP and AC underscores the importance of human and social capital in driving innovation within Chinese MSFBs. Thirdly, leveraging BDAI as a contingency factor can further augment the effects of EL on both PP and AC, thereby enhancing innovation outcomes. Thus, managers can capitalize on BDAI to gain actionable insights to increase MSFBs’ innovativeness.
Originality/value
This study enlightened how EL can develop MSFBs innovativeness through PP and AC. Our findings reveal that MSFBs can increase their innovation by leveraging PP and AC, leading to higher proactive provision in employees’ behavior. Subsequently, our results synchronized the exploration of BDAI as a novel insight for MSFB innovativeness. This shed light on a highly notable contribution to understanding BDAI to benefit MSFBs, acting as a critical contingency between EL, PP and AC.
... This suggests that firms with a strategic orientation, particularly those focused on innovation and customer needs, are more inclined to adopt AI technologies. Recent findings by Dabbous et al. (2022) also indicate that Bagozzi and Yi (1988); McDonald and Ho (2002) organizational culture and supportive environments play crucial roles in shaping employees' AI adoption intentions. Adiguzel et al. (2023) further emphasized that strategic vision and digital innovation positively influence AI perception and adoption in the banking sector. ...
The objective of the study was to develop a model of Big Data Analytics and Artificial Intelligence (BDA-AI) technology acceptance in the hospitality and tourism industry in Malaysia. The model developed in this study is Comprehensive Theory of Use and Adoption of Technology (CTUAT). This is an empirical and quantitative study based on a unified model developed through a massive literature review. The study adopted a cross-sectional online survey among 343 owners/managers of tourism and hospitality firms. Applying structural equation modeling and using AMOS software, data was purified and analyzed. The study identified that strategic orientation, performance effectiveness; top management support, organizational resources, employee readiness, and technology expectancy are the predictors of the behavioral intention of BDA-AI technology acceptance except for innovation on behavioral intention.
... Es gibt eine Reihe von Einflussfaktoren, die sich auf die Akzeptanz von KI auswirken. In der Literatur werden insbesondere Sicherheit, Nutzen, Kompatibilität, Privatsphäre und Vertrauen, aber auch die Organisationskultur und Jobsicherheit angeführt (Wilkens 2020;Dabbous et al. 2022;Hasija/Esper 2022;Mirbbaie et al. 2022). D. h., KI-Einsatz wird eher akzeptiert, wenn die Anwendungen sicher sind und nicht die Privatsphäre berühren, keine Arbeitsplätze gefährden und sie praktischen Nutzen bringen. ...
-Wie wirkt sich der Einsatz Künstlicher Intelligenz auf Arbeitspraktiken der Wissensarbeit aus?
-Welche Herausforderungen und Problemfelder sind damit verbunden und welche Zusammenhänge zum Risiko von (Deep) Automation Bias?
-Welche Einflussfaktoren sind für den konstruktiven KI-Einsatz relevant?
-Welche Grundkompetenzen umfasst Critical AI Literacy und mit welchen Ansätzen ist das entsprechende Wissen vermittelbar?
... However, this does not necessarily impede the implementation of AI technologies. According to (Dabbous et al., 2021), understanding the factors that encourage employees to utilize AI technologies can lead to successful implementation, even in the face of initial resistance. Similarly, the reference to the need for organizations to communicate the benefits of AI and involve employees in decision-making processes to build trust and acceptance is supported by a recent study by (Nyathani, 2023). ...
The rapid integration of Artificial Intelligence (AI) in the banking sector offers both transformative opportunities and significant challenges. This study investigates the impact of resistance to change, talent and skills gaps, and regulatory compliance on the successful implementation of AI technologies at RHB Bank Headquarters in Malaysia. Using a quantitative research methodology, data were collected through a self-administered questionnaire distributed to 370 employees across various departments and hierarchical levels. Respondents were selected using a simple random sampling method. The study aimed to evaluate the relationships between these critical factors and AI implementation. Findings indicate that all three factors significantly influence AI implementation at RHB Bank, with regulatory compliance emerging as the strongest predictor, followed by resistance to change and the talent and skills gap. These results suggest that addressing employee resistance, bridging workforce skill deficiencies, and ensuring regulatory adherence are crucial for overcoming barriers to AI implementation. The study concludes that for RHB Bank to fully harness the advantages of AI, strategic efforts must focus on fostering a culture of adaptability, investing in talent development, and maintaining compliance with evolving regulatory frameworks. Such efforts are essential for enhancing the bank's operational efficiency and securing a competitive edge in the digital banking era.
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... Integrating emerging technologies within the RBV framework brings profound strategic implications, reshaping the competitive landscape and industry norms (Hajar et al., 2023). Artificial intelligence, for instance, enables organisations to unlock new insights from vast troves of data, automate tasks, and personalise customer experiences (Brynjolfsson & McAfee, 2017;Dabbous et al., 2022). Organisations can utilize machine learning algorithms to create predictive analytics models, enhance decision-making processes, and foster innovation across multiple domains (Davenport & Ronanki, 2018). ...
In today's rapidly evolving digital landscape, organisations are challenged to strategically deploy digital resources to secure competitive advantage. Drawing upon the Resource-Based View (RBV) theory, this paper explores the intricate interplay between digital resources and organisational success. Through thematic analysis of empirical literature, the study delves into key facets such as data utilisation, digital platforms, technological capabilities, talent management, agility, and strategic partnerships. Methodologically, qualitative research techniques, notably thematic analysis, are employed to synthesise existing research findings. The study uncovered that human capital is pivotal in propelling digital transformation and fostering innovation. Organisational success in the digital era relies on implementing effective methods for managing digital talent, including recruiting, training, and keeping workers with digital competence. The study also highlights the strategic significance of digital resource utilisation within the RBV framework. Finally, the paper presents practical implications for organisational leaders seeking to harness digital resources effectively to achieve sustainable competitive advantage in the digital era. Findings underscore the strategic importance of digital resource utilisation in enhancing organisational competitiveness and driving long-term growth. The study contributes to understanding how organisations can optimally leverage digital resources within the RBV framework.
... Public organizational culture refers to the values, beliefs, behavioral patterns, and work climate shared within a public organization (Akpa et al., 2021) and people's identification with and sense of belonging to the organization (Yue et al., 2020). In the context of AI, public organizational culture includes attitudes and responsiveness to new technologies, innovations, and changes, as well as core values, leadership styles, communication styles, and employee engagement (Dabbous et al., 2022). The rapid development and application of AI technology have contributed to the culture change in public organizations. ...
This study constructs a moderating mediation model to link public sector employees’ Artificial Intelligence (AI) usage with employees’ moral norms and ethical decision-making behaviors. Based on the theory of public service motivation, this study hypothesizes that the impact of AI usage on employees’ ethical decision-making behaviors acts through the mediating effects of employees’ service motivation, employees’ moral norms, and employees’ ethical perceptions and that the relationship between AI usage and employees’ service motivation, employees’ ethical norms, and employees’ ethical perceptions is moderated by the culture of the public organization. The selected data from 417 public sector employees in China supported most of the research hypotheses. The findings show that employee service motivation, employee moral norms, and employee moral cognition mediate the relationship between AI usage and employee ethical decision-making behavior. Public organization culture moderated the relationship between AI usage and employee service motivation, as well as AI usage and employee ethics. This study reveals the complex mediating and moderating relationships between AI usage and employees’ ethical decision-making behaviors in the public sector. It provides important theoretical and practical insights for further understanding and promoting public sector employees’ ethical behaviors in the era of AI.
... Multiple studies stress that the effects of the implementation of AI cannot be explained by considering only the AI system itself; instead, these effects result from how AI is embedded within organizational practices by organization members (Rudko, 2021;Dabbous et al., 2022;Christin, 2017;Haesevoets et al., 2021;Faulconbridge et al., 2024;Anthony, 2021;Gualdi & Cordella, 2024). Expectations that depict AI systems as a specific kind of rational machine remain prevalent in such empirical projects, as we demonstrate later in this article. ...
This article investigates the elective affinity between decision-making models in the fields of organizational theory and artificial intelligence (AI), exploring the decision-making influence of societal ideas in these two research contexts. Using Herbert Simon's work on organizations and AI as an example, we examine the properties of these societal ideas and identify six key characteristics, emphasizing rational calculations based on a logic of consequences. These specific notions of decision-making converge again in the phenomenon of AI-based algorithmic decision-making in organizations, as we demonstrate using examples from descriptions and advertisements of such systems, the current literature on their use, and empirical research concerning organizational practices.
KEYWORDS: decision-making, artificial intelligence, rational choice, organizational theory, Herbert Simon, performativity
... Job uncertainty, stemming from organisational changes, such as downsizing, consistently engenders employee apprehension and unease (Dlouhy and Casper 2021). Previous research showed that AI implementation can lead to job insecurity, a significant stressor that negatively impacts intentions (Dabbous, Aoun Barakat, and Merhej Sayegh 2022;Koo, Curtis, and Ryan 2021). Therefore, job uncertainty can be described as the stress nurses experience when adopting AI platforms, leading to uncertainty regarding their jobs and concerns about potential job replacements due to AI implementation (Aung, Wong, and Ting 2021;Gao et al. 2020;Lambert et al. 2023). ...
Aim
This study examines how social influence, human–machine trust and perceived job stress affect nurses' behavioural intentions towards AI‐assisted care technology adoption from a new perspective and framework. It also explores the interrelationships between different types of social influence and job stress dimensions to fill gaps in academic literature.
Design
A quantitative cross‐sectional study.
Methods
Five hospitals in Taiwan that had implemented AI solutions were selected using purposive sampling. The scales, adapted from relevant literature, were translated into Chinese and modified for context. Questionnaires were distributed to nurses via snowball sampling from May 15 to June 10, 2023. A total of 283 valid questionnaires were analysed using the partial least squares structural equation modelling method.
Results
Conformity, obedience and human–machine trust were positively correlated with behavioural intention, while compliance was negatively correlated. Perceived job stress did not significantly affect behavioural intention. Compliance was positively associated with all three job stress dimensions: job uncertainty, technophobia and time pressure, while obedience was correlated with job uncertainty.
Conclusion
Social influence and human–machine trust are critical factors in nurses' intentions to adopt AI technology. The lack of significant effects from perceived stress suggests that nurses' personal resources mitigate potential stress associated with AI implementation. The study reveals the complex dynamics regarding different types of social influence, human–machine trust and job stress in the context of AI adoption in healthcare.
Impact
This research extends beyond conventional technology acceptance models by incorporating perspectives on organisational internal stressors and AI‐related job stress. It offers insights into the coping mechanisms during the pre‐adaption AI process in nursing, highlighting the need for nuanced management approaches. The findings emphasise the importance of considering technological and psychosocial factors in successful AI implementation in healthcare settings.
Patient or Public Contribution
No Patient or Public Contribution.
... Big Data. This variable was measured using four items adapted from Dabbous et al. (2022) with a reliability coefficient of 0.87. ...
This study examines the evolving influence of artificial intelligence (AI) on customer engagement dynamics, aiming to provide insights into how AI technologies can enhance engagement, loyalty, and trust among consumers. A quantitative research approach was employed with a causal research design. Data was collected from 140 individuals in the Kathmandu Valley, primarily undergraduate students aged 20-29 years, through a questionnaire utilizing a 7-point Likert scale. Correlation analyses were conducted using SPSS software version 23 to analyze the data. The study's findings highlight the transformative potential of AI in shaping customer engagement dynamics. By leveraging AI technologies, businesses can significantly enhance customer engagement and foster stronger relationships, ultimately driving competitive advantage and sustainable growth in an increasingly digital marketplace. The implications of this research suggest that understanding AI's capabilities can empower businesses to develop more effective customer engagement strategies. This presents opportunities for further investigation into the factors influencing the adoption and effectiveness of AI in customer engagement. Overall, this study contributes to the understanding of AI's role in customer engagement within the context of Nepal. Future research can provide valuable insights for businesses operating in emerging markets, informing the development of AI solutions tailored to the specific needs and preferences of local customers. By exploring these dynamics, organizations can better navigate the challenges of customer engagement in a rapidly changing technological landscape.
... Pan et al. [48] utilised the TOE theory and the transaction cost theory to explain the companies' AI adoption. Dabbous et al. [49] built on the theory of reasoned action (TRA) and TAM to investigate the employees' intentions to use AI systems. Table 8 provides a summary of identified influential factors. ...
The adoption of artificial intelligence (AI) systems is on the rise owing to their many benefits. This study conducted a bibliometric analysis to identify (1) how the literature on AI adoption has evolved over the past few years, (2) key themes associated with AI adoption in the literature, and (3) the gaps in the literature. To achieve these objectives, we utilised the Biblioshiny of R-package bibliometric analysis tool to analyse the AI adoption literature. A total of 91 articles were reviewed and analysed in this study. Four major themes were identified: AI, machine learning, the unified theory of acceptance and use of technology (UTAUT) model and the technology acceptance model (TAM). Using a content analysis of the identified themes, the study gained additional insight into the studies on AI adoption. Previous studies have been limited to specific industries and systems, and adoption theories like the UTAUT and TAM have also been utilised to a limited extent. Directions for future studies were provided.
... We used a questionnaire that was a closed-type 5-point Likert-type scale. Items for the AI-supported entrepreneurial culture construct were adopted from Dabbous et al. [63], and items for the AI-enhanced leadership and employee engagement construct were adopted from Wijayati et al. [4]. Items for the adoption of AI to reduce employee workload construct were adopted from Qiu et al. [64]. ...
Background: Our research delved into exploring various selected facets of AI-driven employee engagement, from the gender perspective, among Slovenian entrepreneurs. Methods: This research is based on a random sample of 326 large enterprises and SMEs in Slovenia, with an entrepreneur completing a questionnaire in each enterprise. Results: Findings suggest that there are no significant differences between male and female entrepreneurs in Slovenia regarding various aspects of AI-supported entrepreneurial management practice including the following: AI-supported entrepreneurial culture, AI-enhanced leadership, adopting AI to reduce employee workload, and incorporating AI tools into work processes. The widespread integration of AI into entrepreneurship marks a transition to a business landscape that values inclusivity and equity, measuring success through creativity, strategic technology deployment, and leadership qualities, rather than relying on gender-based advantages or limitations. Our research also focused on the identification of gender differences in path coefficients regarding the impact of the four previously mentioned aspects of AI on employee engagement. While both genders see the value in using AI to alleviate employee workload, the path coefficients indicate that female entrepreneurs report higher effectiveness in this area, suggesting differences in the implementation of AI-integrated strategies or tool selection. Male entrepreneurs, on the other hand, appear to integrate AI tools into their work processes more extensively, particularly in areas requiring predictive analytics and project scheduling. This suggests a more technical application of AI in their enterprises. Conclusions: These findings contribute to understanding gender-specific approaches to AI in enterprises and their subsequent effects on employee engagement.
... Such a theory can also help explain how OTA employees' perceived benefits and perceived risks of ChatGPT affect their job insecurity and turnover intention. That is, if employees perceive benefits from ChatGPT, their job insecurity may decrease (Dabbous et al., 2022), and turnover intention may also decrease through reduced job insecurity. Conversely, if employees perceive ChatGPT as a threat to their jobs, job insecurity may increase, and turnover intention may also increase through increased job insecurity. ...
Based on the conservation of resources theory, this study investigated the relationships between the online travel agency employees' perceived benefits and risks of ChatGPT, job insecurity, and turnover intention. Additionally, we also examined the mediating role of job insecurity and the moderating role of organizational support. Using data from a sample of 432 United States OTA employees, the findings demonstrated that the perceived benefits and risks of ChatGPT significantly affected perceived job insecurity. Moreover, the perceived benefits and risks of ChatGPT indirectly influenced turnover intention through the intermediary variable of perceived job insecurity. Organizational support positively moderated the impact of perceived benefits and negatively moderated perceived risks on job insecurity and turnover intentions, thus helping employees cope with challenges and reduce uncertainty. The findings underscore the need for organizations to foster supportive environments to manage the impact of ChatGPT on OTA employee retention. The theoretical and practical implications were discussed.
... Furthermore, the study was implemented in a developing country context (South Africa); hence it may not represent the true reflection of other developing countries. Therefore, the study results may not be generalizable to all developing countries [64], [65]. ...
div align="center"> DevOps software development approach is widely used in the software engineering discipline. DevOps eliminates the development and operations department barriers. The paper aims to develop a conceptual model for adopting DevOps practices in software development organizations by extending the unified theory of acceptance and use of technology (UTAUT). The research also aims to determine the influencing factors of DevOps practices’ acceptance and adoption in software organizations, determine gaps in the software development literature, and introduce a clear picture of current technology acceptance and adoption research in the software industry. A comprehensive literature review clarifies how users accept and adopt new technologies and what leads to adopting DevOps practices in the software industry as the starting point for developing a conceptual framework for adopting DevOps in software organizations. The literature results have formulated the conceptual framework for adopting DevOps practices. The resulting model is expected to improve understanding of software organizations’ acceptance and adoption of DevOps practices. The research hypotheses must be tested to validate the model. Future work will include surveys and expert interviews for model enhancement and validation. This research fulfills the necessity to study how software organizations accept and adopt DevOps practices by enhancing UTAUT. </div
The integration of artificial intelligence (AI) into the workplace has raised concerns among employees regarding job security and career development, while also influencing their behavior. In response to this issue, this study employed a questionnaire-based approach grounded in general strain theory to investigate the mechanisms through which the risk of job substitution by AI affects employee deviant behavior. The findings indicate that the risk of job substitution by AI positively influences both interpersonal deviant behavior and organizational deviant behavior among employees. Furthermore, digital transformation stress serves as a mediating factor in the relationship between them. Digital self-efficacy can alleviate the adverse effects of digital transformation stress on deviant behaviors. This research elucidates the mechanisms underlying how the risk of job substitution by AI impacts employee deviant behavior, thereby enriching existing literature on new technologies’ influence on employee conduct while providing valuable insights for organizations to implement AI responsibly.
Background
Although occupational change is becoming commonplace for contemporary employees, it remains understudied from the theoretical perspective. With employees bringing along previous job experiences into their new roles, occupational changes potentially create favorable conditions for employees’ job crafting and innovation performance.
Objective
Based on Career Construction Theory, this study aims to gain a better understanding of the increasingly prevalent phenomenon of occupational change. Specifically, this study explores the potential facilitating effect of occupational change on job crafting and subsequently on employee innovation performance.
Method
A questionnaire survey administered to 413 employees was conducted to examine the proposed hypotheses. Individual samples t-tests and structural equation modeling technique were employed in the data analyses.
Results
The results confirmed the hypothesis that occupational change experience is positively associated with employee job crafting. Moreover, job crafting was found to play a full mediating role in the relationship between occupational change experience and employee innovation performance.
Conclusion
This study serves as an exploratory attempt to better understand the new and under-researched topic of occupational change. By focusing on the new experience and capabilities that occupational changers can bring to their new jobs, this study proposes that occupational changes could potentially facilitate job crafting which further enhances innovation performance. In this vein, this study provides new theoretical insights and meaningful managerial suggestions on the topic of occupational change.
Tujuan penelitian untuk menganalisis faktor-faktor nilai adopsi Artificial Intelligence (AI) dan peran lingkungan dalam faktor-faktor niat adopsi AI dengan pendekatan berbagai teori. Perkembangan teknologi yang pesat menjadikan adopsi AI urgensi bagi bisnis ritel untuk meningkatkan efisiensi operasional dan pengalaman pelanggan. Metode penelitian yang digunakan adalah kuantitatif dengan pendekatan survei, melibatkan 135 pelaku bisnis ritel yang dipilih secara acak. Data dikumpulkan melalui kuesioner dengan skala Likert 5 poin dan dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa Perceived Useful, Effort Expectacy dan Environment mempunyai pengaruh positif dan signifikan terhadap Intention to Adopt AI, sedangkan Perceived Easy of Use, Performance Expectacy tidak berpengaruh signifikan terhadap Intention to Adopt AI. Environment memoderasi pengaruh pengaruh Perceived Usefull terhadap Intention to Adopt AI, tetapi tidak memoderasi pengaruh Perceived Easy to Use, Performance Expectacy dan Effort Expectacy terhadap Intention to Adopt AI. Penelitian ini hanya menganalisis aspek niat dalam perilaku adopsi AI pada bidang bisnis ritel dengan berdasar pada teori TAM, UTAUT, dan TEO Framework, sehingga bagi peneliti yang akan datang dapat menganalisis faktor demografi dalam aspek perilaku yang lain seperti sikap atau kekonsistenan dalam adopsi Ai, menggunakan subyek dari berbagai bidang bisnis, menggunakan perspektif teori seperti teori TPB, DIF dan sebagainya dalam menganalisis faktor kunci pada adopsi AI.
Integrating artificial intelligence into human resource management practices, particularly in performance appraisal, transforms how organisations evaluate and manage employee performance. AI-driven systems offer enhanced accuracy and efficiency through continuous data collection and analysis. Yet, human judgement remains indispensable in capturing the nuances of employee behaviour, interpersonal dynamics, and organisational culture. This chapter explores the balance between AI-driven automation and human oversight in performance appraisal, emphasising the need for a hybrid model that leverages both strengths. This chapter examines AI's role in performance management, focusing on its advantages and limitations. It also offers a conceptual model to integrate artificial intelligence with human judgment. This framework highlights the critical role of human judgment, ensuring that AI enhances rather than supplants human decision-making processes. By leveraging the complementary strengths of AI and human insight, organisations can develop equitable and efficient evaluation systems.
Purpose – This study aims to assess the factors that impact the adoption of artificial intelligence (AI) in the human resource (HR) recruitment procedure in Vietnam’s medium-sized firms. Design/methodology/approach – Through a quantitative approach, this paper collected data of 297 hiring managers, HR directors and top-level executives from Vietnam’s medium-sized firms with a structured questionnaire. The partial least squares structural equation model was used to analyze the data and evaluate the hypothesis model (on platform Smart PLS 3.0).
Findings – The results show that in Vietnam’s medium-sized companies, both perceived benefits and perceived sacrifices directly impact on perceived value, which leads to organizations’ adoption of AI. HR readiness also has a moderating effect between perceived value and AI adoption.
Research limitations/implications – Future research can compare AI adoption between large and medium companies, as well as other criteria in Asian countries. Other organizational constructs can be considered moderators between perceived value and AI adoption.
Practical implications – This study offers a context-specific understanding of the practice of using AI to acquire talent in Vietnam. Both of AI technology’s perceived benefits and perceived sacrifices directly impact its perceived value, therefore indirectly impacting its adoption. In this study, HR readiness serves as an inhibitor to adoption. Some essential managerial implications are suggested.
Originality/value – This study provides valuable insights into applying AI to Vietnam’s medium-sized companies, especially in the recruitment process. It adds to a substantial body of work on applying AI to HR management.
Organisational culture (OC) is a crucial factor that every organisation must address in order to thrive in the digital economy. The present study investigates the crucial drivers employing OC, external factors (EF), and organisational internal resources (OR) in adopting artificial intelligence tools by Delhi-NCR small and medium-sized businesses. Extensive scholarly literature forms the foundation of the study's conceptual framework. The study adopts a research philosophy rooted in positivism and utilises a deductive approach to investigate the relationship. The research strategy employed is survey-based. Utilising a straightforward method of random
sampling to encompass a diverse range of industries and businesses. The study collected data from 196 SME owner-managers in order to investigate the representable factors of the entire Delhi-NCR SMEs that influence their success or failure to adopt AI. We conducted the analysis using Smart-PLS 4 to evaluate the relationship between the endogenous and exogenous variables in the measurement and structural model. The study's findings indicate that OR, OC, and EF have a significant positive influence on AI adoption. This suggests that SMEs can improve their AI outcomes by enhancing their OR, OC, and EF processes. These findings can assist decisionmaking and resource allocation by emphasising the significance of critical factors in promoting
Al outcomes and identifying areas where efforts may not yield desired results. According to the study, a key factor contributing to the limited adoption of AI among SMEs is the absence of support from top management. The study findings will provide valuable insights for policymakers and institutional chambers regarding the role of OC, OR, and EC in information system adoption. These insights can help inform the development of policies that take these important connections into account. Finally, the findings would enhance the understanding of the literature by presenting empirical evidence from the SMEs of Delhi-NCR, India.
The perception of artificial intelligence among employees is increasingly relevant as artificial intelligence technologies become more prevalent in the workplace. A positive perception of artificial intelligence can lead to improved job performance, as employees may be more willing to embrace and utilize artificial intelligence technologies to enhance their work. Conversely, a negative perception of artificial intelligence can hinder job performance, as employees may resist or feel threatened by artificial intelligence technologies. This study aims to examine the association between attitudes to artificial intelligence and job performance among 500 academicians employed in universities in Istanbul, using structural equation modeling. The findings of the analysis indicate a statistically significant positive relationship (β=0.412, p<0.01) between artificial intelligence and job performance. It is necessary to emphasize the importance of making academics' attitudes towards artificial intelligence positive.
Artificial intelligence (AI) is a constantly evolving frontier of innovative computing capabilities rather than a single technology or group of technologies. Whenever a person picks up their smartphone, AI systems operate in the background. This scenario implies that individuals now find themselves involved with AI, irrespective of their awareness. The increasing adoption of AI-enabled systems has significant consequences for society, organizations, and individuals. AI has permeated every aspect of human life, impacting individuals' choices, preferences, and behaviour in numerous ways. It is essential to comprehend these new behaviours in order to predict how human behaviour will evolve in AI-infused environments. This study focuses mainly on how consumption values affect the behavioural intention to use AI with different contributions. The suggested study strategy takes a two-phase method to completely investigate the elements influencing behavioural intentions towards behavioural intention to use AI.
BACKGROUND
The insurance sector in Jordan stands to gain significant benefits from the adoption of Accounting Intelligence (AI) systems, which can transform decision-making processes, enhance operational efficiency, and elevate customer satisfaction.
OBJECTIVE
This study investigates the mediating role of organizational culture in the relationship between three key organizational factors—top management support (TMS), information technology (IT), and organizational size (OS)—and AI system adoption (AIA) in Jordan’s insurance companies.
METHODS
Using a quantitative approach, the study surveyed 274 employees from Jordanian insurance companies. The data was analyzed using structural equation modeling (SEM) and confirmatory factor analysis (CFA) to assess the study’s hypotheses and ensure data reliability and validity.
RESULTS
The findings revealed substantial support for the relationships between TMS, IT, OS, and organizational culture (ORC). Additionally, organizational culture emerged as a mediating factor influencing AI system adoption (AIA) adoption. However, certain direct relationships, such as ORC and AIA, and the mediating role of organizational culture in the relationship between IT and AIA, were context-dependent and lacked support.
IMPLICATIONS
Organizational culture plays a critical role in mediating the relationship between key organizational factors and AIA in Jordan’s insurance sector.
Yapay zekanın örgütsel davranış üzerindeki etkisi özellikle son dönemde sıkça tartışılan bir konu haline gelmiştir. Yapay zekanın örgütlerdeki çalışanların davranışları, örgütteki bireylerin oluşturduğu yapı ve süreçler üzerindeki etkileri araştırılmakla birlikte bu çalışmalardan elde edilen sonuçlar örgütsel davranış alanını oluşturan tekil konularla ilgili olup bu sonuçlar bir bütün halinde örgütsel davranışı kapsayacak şekilde bir araya getirilmemiştir. Teknolojinin ve yapay zekanın hızlı gelişiminin örgütlerin işleyişini ve örgüt çalışanlarını önemli ölçüde etkilemeye başlamasından hareketle yapılan bu çalışmada, yapay zekanın örgütsel davranış alanı üzerindeki etkilerinin bütüncül bir şekilde incelenmesi amaçlanmıştır. Araştırma sonucunda yapay zekanın örgütlerde gerek işlevsel gerekse çalışan ve takım davranışları üzerinde pek çok faydalı kullanım alanı olmasına rağmen örgütlerdeki çalışanlar üzerinde çoğunlukla stres, motivasyon ve performans düşmesi ve işten ayrılma niyeti gibi olumsuz etkilerinin olduğu belirlenmiştir. Bu olumsuz etkilerin olumluya dönüştürülebilmesi için çalışanlara yapay zeka ve teknolojinin örgütlerdeki kullanımı ve örgütler üzerindeki gerçek etkisi hakkında eğitim verilmesi ve örgütlerdeki bu değişim için doğru örgütsel kültür ve iklimin oluşturulması gerektiği ve bu süreçte doğru liderliğin son derece önemli olduğu sonucuna varılmıştır.
Purpose
Considering the increasing impact of Artificial Intelligence (AI) on financial technology (FinTech), the purpose of this paper is to propose a research framework to better understand robo-advisor adoption by a wide range of potential customers. It also predicts that personal and sociodemographic variables (familiarity with robots, age, gender and country) moderate the main relationships.
Design/methodology/approach
Data from a web survey of 765 North American, British and Portuguese potential users of robo-advisor services confirm the validity of the measurement scales and provide the input for structural equation modeling and multisample analyses of the hypotheses.
Findings
Consumers’ attitudes toward robo-advisors, together with mass media and interpersonal subjective norms, are found to be the key determinants of adoption. The influences of perceived usefulness and attitude are slightly higher for users with a higher level of familiarity with robots; in turn, subjective norms are significantly more relevant for users with a lower familiarity and for customers from Anglo-Saxon countries.
Practical implications
Banks and other firms in the finance industry should design robo-advisors to be used by a wide spectrum of consumers. Marketing tactics applied should consider the customer’s level of familiarity with robots.
Originality/value
This research identifies the key drivers of robo-advisor adoption and the moderating effect of personal and sociodemographic variables. It contributes to understanding consumers’ perceptions regarding the introduction of AI in FinTech.
Artificial intelligence can provide organizations with prescriptive options for decision-making. Based on the notions of algorithmic decision-making and user involvement, we assess the role of artificial intelligence in workplace decisions. Using a case study on the implementation and use of cognitive software in a telecommunications company, we address how actors can become distanced from or remain involved in decision-making. Our results show that humans are increasingly detached from decision-making spatially as well as temporally and in terms of rational distancing and cognitive displacement. At the same time, they remain attached to decision-making because of accidental and infrastructural proximity, imposed engagement, and affective adhesion. When human and algorithmic intelligence become unbalanced in regard to humans' attachment to decision-making, three performative effects result: deferred decisions, workarounds, and (data) manipulations. We conceptualize the user interface that presents decisions to humans as a mediator between human detachment and attachment and, thus, between algorithmic and humans' decisions. These findings contrast the traditional view of automated media as diminishing user involvement and have useful implications for research on artificial intelligence and algorithmic decision-making in organizations.
Keywords: Algorithmic and human-based intelligence, algorithmic decision-making, artificial intelligence, interface, workplace decisions
Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision-making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI, and examines how each can bring their own strength in organizational decision making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans' cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision-making. This premise mirrors the idea of 'intelligence augmentation': AI systems should be designed with the intention of augmenting, not replacing, human contributions.
Small and medium enterprises (SMEs) can play an important role in the national economy of developing countries. The adoption of information and communication technology (ICT) has enabled local SMEs to participate in the international market. However, little research has addressed issues related to SMEs adopting ICTs, especially in rural areas of Middle Eastern developing countries such as Saudi Arabia. Using the extended technology-organization-environment framework with personal innovativeness, this study examined the factors that influence the adoption of ICTs among SMEs in rural areas of Saudi Arabia. The study found that relative advantages, top management support, culture, regulatory environment, owner/manager innovativeness and ICT knowledge had a significant relationship with ICT adoption among SMEs in Saudi Arabia, whereas compatibility, complexity and a competitive environment had no significant relationship with ICT adoption. The findings of this study will potentially help SME managers/owners and the Saudi government in the successful adoption and diffusion of ICT in SMEs located in rural areas in Saudi Arabia.
Innovation is considered to be one of the key factors that influence the long-term
success of a company in the competitive markets of today. As a result, there is a growing interest
in the further study of the determining factors of innovation. Today, the focus is on these factors
related to people and behavior, emphasizing the role of organizational culture, as a factor that
can both stimulate or restrain innovation, and therefore affect company performance. However,
there is little empirical research linking these variables, particularly in the Spanish context.
The purpose of this paper is to study these links by using a sample of industrial companies. The
results show that culture can foster innovation, as well as company performance, or it could also
be an obstacle for both of them, depending on the values promoted by the culture. It has been
found specifically, that an adhocratic culture is the best innovation and performance predictor.
Based on these results, it can be concluded that, innovation mediates the relationship between
certain types of organizational cultures and performance.
The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.
Recent years have witnessed increased use of self-service technologies (SSTs) across the services sector, which has dramatically changed the nature of the service delivery process. Although an abundance of research has investigated how customers evaluate a new SST and what drives the initial adoption, little is known about how customers interact with, and adapt to, an SST after their first experience. Thus, this study focuses on the dynamic and complex process through which customers move from initial adoption to continued use, after repeated interactions with an SST. A three-wave longitudinal study examines how habit, self-efficacy, and satisfaction affect SST usage over time in a retailing context. The results indicate that as learning occurs and experience accumulates, customers’ continued use of an SST is initially largely rational driven (self-efficacy), then largely emotional driven (satisfaction), and, finally, habitual (habit). Over time, habit completely mediates the impact of intentions on future usage. The article concludes with a discussion of the managerial implications and directions for further research.
In this article, the authors investigate whether the fit between consumers’ image and the image of an innovation, commonly referred to as image congruence, has an impact on customers’ attitudes toward innovative services as well as the intention to use these new services. Moreover, situational influences (i.e., private vs. public, and friends vs. colleagues) are examined. The results of the two experiments show that not only does image congruence have a significant impact on consumer attitudes and the adoption decision, but there is also an interaction between image congruence and the consumption situation. Contrary to the authors’ expectations, consumers with low image congruence are influenced more by their surroundings than consumers with high image congruence. It seems that as long as the fit between the consumer’s self-image and the service image is high, signals from the consumer’s environment are overruled.
This study evaluates the factors that lead small to medium-sized enterprises (SMEs) to adopt information and communication technology (ICT). Our research proposes an ICT adoption model using structural equation modelling that examines the role of SMEs’ owner-managers as mediators in the adoption of ICT. The model is focused on the investigation of the direct and indirect influences of technological, cultural, environmental and organisational factors on the SME’s adoption process. The results indicate the validity of these adoption factors and their applicability to the United Arab Emirates (UAE) environment. The main focus of the research was to develop a new framework that develops further the research of Rashid and Al-Qirim (2001). The present paper identifies the critical mediating role of the owner-anager’s decision to adopt ICT in SMEs in the UAE, and confirm the importance of technological, organisational and environmental factors in the adoption process. Although there is cultural varsity in the UAE, culture is not found to be a significant factor, either directly or indirectly, in SMEs’ ICT adoption in the country.
Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing researchdespite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also provide an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.
Motivation is the main force through which individuals allocate effort to generate and implement innovative ideas. However, employees are only motivated to go beyond their designated role and get involved in spontaneous and innovative activities if they have a strong identification with the organization. Organizational culture plays a critical role in motivating innovative behaviour, as it can create commitment among members of an organization in terms of believing in innovation as an organizational value and accepting innovation-related norms prevalent within the organization. The research this paper reports on addresses the motivational aspects of the relationship between culture and innovation in construction firms. Specifically, it focuses on those managerial actions through which the importance of innovation may be communicated and innovation-related behaviour may be induced and reinforced. An in-depth case study investigating the innovation activities of a Swiss contractor revealed that project constraints and regional separation may diminish the motivational effects of managerial actions in construction firms. It is concluded that a culture that motivates new solutions and innovative improvements in particular first of all prevents ideas from getting lost in daily business and within the organization. Giving immediate feedback, providing communication channels for implicit knowledge, allowing for autonomous work and task identity, initiating innovation projects and using a comprehensive reward and incentive system are appropriate managerial actions in this regard.
Traditional technology adoption research has assumed a single adopting group. However, there are many settings in which multiple groups must jointly adopt an innovation in order for it to succeed. This is particularly true for new information technology innovations that mediate the relationship between two groups. For example, online exchanges (e.g., Freemarkets, GoFish) must attract both suppliers and buyers in order to be successful. The same is true for providers of hardware/software solutions for electronic data interchange and supply chain management.
This article describes the phenomenon of multigroup adoption with a particular focus on applications within the financial services and retailing industries. Empirically, the article reports findings from a study that illustrates the importance of evaluating and managing multigroup technology adoption in the specific context of an in‐market trial of a new smart card‐based electronic payment system. Two distinct groups critical to the smart card's success are studied: consumers (who must decide to use the new card) and retailers (who must agree to adopt and use new technology needed to process smart card transactions). The study identifies which characteristics of the smart card innovation are most closely linked to intention to adopt for each group, and examines how these key characteristics differ by group.
Perceptual data were collected via a mail survey from consumers and merchants living in the city where a one‐year market trial of the new card was taking place. Four separate sampling frames were established for both consumers and merchants who were participating in the trial as well as both consumers and merchants who were not participating in the trial. Random samples were then drawn from these frames. More than 350 consumers and over 250 merchants completed and returned the survey. Responses were analyzed separately for each of the four groups sampled.
The most important characteristic leading to adoption identified by all four groups was relative advantage—the smart card had to demonstrate a clear competitive advantage over what they currently used. Compatibility (i.e., the degree to which the smart card fit with their current preferences) was also noted as important to all but the nonparticipating merchant group. Beyond this, the key drivers of adoption differed considerably by group.
Participating consumers and participating merchants appeared to possess different perspectives when assessing their decision to adopt the smart card technology. Consumers seemed to value the notion that the adoption decision is under their control, whereas merchants seemed to place more value on the antecedents that had the potential to add to their bottom line. This suggests that it is necessary to institute different marketing tactics to attract the early adopting groups. In addition, significant differences in the importance of antecedents between participating and nonparticipating consumers and participating and nonparticipating merchants suggest that, over time, it may also be necessary to develop and use different marketing tactics for later adopters.
This study examines whether the same personal and contextual characteristics that enhance innovation could also contribute to quality and efficiency. Three hundred and forty-nine engineers and technicians in 21 units of a large R&D company participated in the study. Using CFA and HLM models, we demonstrated that people have the ability to both be creative and pay attention to detail, and that an innovative culture does not necessarily compete with a culture of quality and efficiency. Yet, to reach innovative performance creative people need to take the initiative in promoting their ideas, with the possible corresponding price of low performance quality. Copyright © 2004 John Wiley & Sons, Ltd.
Although initial use is an important indicator of information system (IS) success, it does not necessarily lead to the desired managerial outcome unless the use continues. However, compared with the great amount of work done on IS adoption, little systematic effort has gone into providing insight into continued IS use over time. The objective of this study is to develop a longitudinal model of how users' evaluations and behavior evolve as they gain experience with the information technology application. The proposed model is a unified framework that sheds light on four different mechanisms underlying postadoption phenomena: (1) the processes suggested by the technology acceptance model; (2) sequential updating mechanisms; (3) feedback mechanisms; and (4) repeated behavioral patterns. The proposed model was empirically tested in the context of Web-based IS use in a nonexperimental setting. Our findings suggest that, as hypothesized, each of the four theoretical viewpoints is essential for a deeper understanding of continued IS use. We discuss important findings that emerged from this longitudinal study and suggest directions for additional research.
This paper extends the unified theory of acceptance and use of technology (UTAUT) to study acceptance and use of technology in a consumer context. Our proposed UTAUT2 incorporates three constructs into UTAUT: hedonic motivation, price value, and habit. Individual differences — namely, age, gender, and experience — are hypothesized to moderate the effects of these constructs on behavioral intention and technology use. Results from a two-stage online survey, with technology use data collected four months after the first survey, of 1,512 mobile Internet consumers supported our model. Compared to UTAUT, the extensions proposed in UTAUT2 produced a substantial improvement in the variance explained in behavioral intention (56 percent to 74 percent) and technology use (40 percent to 52 percent). The theoretical and managerial implications of these results are discussed.
The IT implementation literature suggests that various implementation factors play critical roles in the success of an information system; however, there is little empirical research about the implementation of data warehousing projects. Data warehousing has unique characteristics that may impact the importance of factors that apply to it. In this study, a cross-sectional survey investigated a model of data warehousing success. Data warehousing managers and data suppliers from 111 organizations completed paired mail questionnaires on implementation factors and the success of the warehouse. The results from a Partial Least Squares analysis of the data identified significant relationships between the system quality and data quality factors and perceived net benefits. It was found that management support and resources help to address organizational issues that arise during warehouse implementations; resources, user participation, and highly-skilled project team members increase the likelihood that warehousing projects will finish on-time, on-budget, with the right functionality; and diverse, unstandardized source systems and poor development technology will increase the technical issues that project teams must overcome. The implementation's success with organizational and project issues, in turn, influence the system quality of the data warehouse; however, data quality is best explained by factors not included in the research model.
Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empiri- cally compare the eight models and their exten- sions, (3) formulate a unified model that integrates elements across the eight models, and (4) empiri- cally validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models ex- plained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Tech- nology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted
Practical problems that are frequently encountered in applications of covariance structure analysis are discussed and solutions are suggested. Conceptual, statistical, and practical requirements for structural modeling are reviewed to indicate how basic assumptions might be violated. Problems associated with estimation, results, and model fit are also mentioned. Various issues in each area are raised, and possible solutions are provided to encourage more appropriate and successful applications of structural modeling
Among the many disruptions caused by artificial intelligence (AI) and other digital technologies (including automation, cyberwarfare, surveillance, loss of privacy, fake news, infrastructure vulnerability), the effects on development pathways are likely to be significant and complex. AI will enable low-income countries to leapfrog in several sectors, including e-governance, e-finance, e-health, and e-education. Yet AI will also lead to automation, reducing the demand for labor, especially unskilled labor. Labor-intensive sectors such as apparel will provide fewer jobs, and lower export earnings. Development strategies will need to adjust accordingly.
Purpose
This paper aims to revisit the role of perceived risk in online banking, using an alternative view on trust as a moderator on the relationship between perceived risk and behavioral intention (BI). With this aim, the conceptual model was proposed to examine the impact of perceived risk on BI directly and indirectly via unified theory of acceptance and use of technology 2 along with its interactionist relationship with trust.
Design/methodology/approach
Structural equation modeling technique is used to analyze data collected from 677 bank customers via personal contact using a self-administered questionnaire.
Findings
The results indicate that perceived risk as a multi-dimensional construct has a direct and indirect impact on BI via performance expectancy, social influence, hedonic motivation and price value. Moreover, it was found that trust moderates the relationship between perceived risk and BI.
Practical implications
This study suggests that banks should create a trust-building mechanism in the online banking environment and develop certain risk management strategies such as providing detailed and thorough information, money-back guarantee and reassurance services to enhance confidence among the customers to use such services. The banks should also devote valuable efforts in designing website interface with improved security features to facilitate usability and reliability of online banking services.
Originality/value
The present study makes an important contribution to the existing literature on e-commerce, especially in the field of online banking, by proposing an interactionist model between perceived risk and trust. The proposed model has never been examined in the relevant literature and could be used to provide a solid theoretical foundation in the context of online banking adoption.
To ensure that the full potential of innovative technology is maximised, it is crucial to understand the psychological factors that influence technology adoption in all industrial consumers. The oil and gas (O&G) industry exemplifies industrial consumers’ reluctance to adopt new technology. Our critical incident interviews identified the key psychological factors that influence technology adoption in the O&G industry. These were personality (innovativeness and risk aversion), attitudes (trust, motivations, “not invented here” syndrome and “engineering mindset”), social (subjective norms and self-image), cognitive (risk perception, uncertainty and familiarity, expertise, and previous experiences) and organizational level factors (leadership, management, organisational culture, adoption culture, and rewards system). In combination with future case studies, these results can be used to develop interventions that support the successful introduction and acceptance of new technology not only in O&G but in other high-risk sectors.
Success of an organization, both in the public and private sectors, depends on increases in efficiency due to acceptance of information technology. This has focused attention on factors that influence individuals’ acceptance and subsequent use of information technologies in organizations using the unified theory of acceptance
and use of technology (UTAUT) model. In this research, we investigate the role of espoused organizational culture traits as an antecedent to the UTAUT model. We found that espoused cultural traits influence users’ acceptance and use of the Internet technology in a government agency in an emerging economy, India.
This study analyzes U.S.-based survey data from the Pew Research Center and explores the relationships between perceived job insecurity, technology usage, and long-term projection on the transition toward the Fourth Industrial Revolution. Keen attention to the upcoming predicted changes in job, work, and employment due to technological revolution, such as robotics and artificial intelligence, will help fill a gap in prior research that heavily emphasized organizational and psychological aspects of job insecurity. The findings show that employees' present perception of job insecurity is highly associated with technology usage and long-term perceptions of job and work. This article contributes to a new direction of job insecurity research (i.e., linkage of job insecurity as an organizational and psychological concept with attitudes about technology adoption and use) and proposes practical suggestions (e.g., a proactive approach to adapting to technology-driven changes in organizations) to prepare for the transition toward the Fourth Industrial Revolution.
The impact of the industrial and digital (information) revolutions has, undoubtedly, been substantial on practically all aspects of our society, life, firms and employment. Will the forthcoming AI revolution produce similar, far-reaching effects? By examining analogous inventions of the industrial, digital and AI revolutions, this article claims that the latter is on target and that it would bring extensive changes that will also affect all aspects of our society and life. In addition, its impact on firms and employment will be considerable, resulting in richly interconnected organizations with decision making based on the analysis and exploitation of “big” data and intensified, global competition among firms. People will be capable of buying goods and obtaining services from anywhere in the world using the Internet, and exploiting the unlimited, additional benefits that will open through the widespread usage of AI inventions. The paper concludes that significant competitive advantages will continue to accrue to those utilizing the Internet widely and willing to take entrepreneurial risks in order to turn innovative products/services into worldwide commercial success stories. The greatest challenge facing societies and firms would be utilising the benefits of availing AI technologies, providing vast opportunities for both new products/services and immense productivity improvements while avoiding the dangers and disadvantages in terms of increased unemployment and greater wealth inequalities.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
The widespread of social media (SM) provides information system (IS) research with a great opportunity to examine IS usage habit. This study adapts habit perspectives of social psychology into social media use and contextualizes findings from both service marketing and IS usage literature for developing a model of SM usage habit. The model suggests a set of behavior-, goal-, affect-, and expectation-based factors that drive the formation of habit for SM use, which in turn influences future usage behavior. By testing the model with a longitudinal field survey of 518 social networking service (SNS) users, this study provides an integrative context-specific view of SM usage habit and addresses unconscious habitual IS use beyond the traditional perspectives of conscious intentional behavior. The findings have important implications for promoting sustainability of social media applications and services.
This study examines how processes of external influence shape information technology acceptance among potential users, how such influence effects vary across a user popula- tion, and whether these effects are persistent over time. Drawing on the elaboration-likelihood model (ELM), we com- pared two alternative influence processes, the central and peripheral routes, in motivating IT acceptance. These pro- cesses were respectively operationalized using the argument quality and source credibility constructs, and linked to per- ceived usefulness and attitude, the core perceptual drivers of IT acceptance. We further examined how these influence pro- cesses were moderated by users' IT expertise and perceived job relevance and the temporal stability of such influence effects. Nine hypotheses thus developed were empirically validated using a field survey of document management system acceptance at an eastern European governmental agency. This study contributes to the IT acceptance literature by introducing ELM as a referent theory for acceptance research, by elaborating alternative modes of influence, and by specifying factors moderating their effects. For prac- titioners, this study introduces influence processes as policy tools that managers can employ to motivate IT acceptance within their organizations, benchmarks alternative influence strategies, and demonstrates the need for customizing influence strategies to the specific needs of a user population.
The study aims to examine if the Technology Acceptance Model (TAM) works for restaurant operations using computing systems. In addition, we pursued other external variables, which were not included in the original TAM, to see how they affect perceived ease of use, perceived usefulness, and intention to use. The external factors were user characteristics, system quality and organizational support. The survey collected data from restaurants in Kentucky, and the response rate was 25% based on the total contacts eligible. SPSS 15.0 and AMOS 7.0 were used for the data analysis. Structural Equation Modeling (SEM) was the primary analysis used to examine the proposed hypotheses developed in fulfilling the study objectives. The SEM statistics supported all the proposed hypotheses but one. The SEM results were interpreted relative to industry implications.
The relationship between self-image and product/brand imagery remains an important area of concern in marketing research and marketing practice because of its impact on product/brand evaluation and choice; however many studies report inconclusive findings about this relationship. A conceptual model is developed which links a function of attitudes – as the pursuit and maintenance of self-esteem and self-identity – to the public and private contexts of self-concepts; and the subsequent intrinsic and extrinsic congruence between brand evaluation and choice. In this exploratory study the Self-Monitoring Scale is used to explore the link between the social and psychological determinants of self-presentation in the pursuit of self-esteem and maintenance of self-identity, and to inform the examination of the relationship between self-concept and product symbolism. Findings from the qualitative and quantitative stages of a study of the UK alcoholic soft drinks market are presented. There were distinct differences between the self-monitoring groups when the interpretation of specific brand images was investigated. The results provided empirical support for viewing the self as a divisible entity. The implications for marketing practice are discussed.
The purpose of this article is to present, by means of a model, the determinants of organisational culture which influence creativity and innovation. A literature study showed that a model, based on the open systems theory and the work of Schein, can offer a holistic approach in describing organisational culture. The relationship between creativity, innovation and culture is discussed in this context. Against the background of this model, the determinants of organisational culture were identified. The determinants are strategy, structure, support mechanisms, behaviour that encourages innovation, and open communication. The influence of each determinant on creativity and innovation is discussed. Values, norms and beliefs that play a role in creativity and innovation can either support or inhibit creativity and innovation depending on how they influence individual and group behaviour. This is also explained in the article.
Though the common default maximum likelihood estimator used in structural equation modeling is predicated on the assumption of multivariate normality, applied researchers often find themselves with data clearly violating this assumption and without sufficient sample size to utilize distribution-free estimation methods. Fortunately, promising alternatives are being integrated into popular software packages. Bootstrap resampling, which is offered in AMOS (Arbuckle, 1997), is one potential solution for estimating model test statistic p values and parameter standard errors under nonnormal data conditions. This study is an evaluation of the bootstrap method under varied conditions of nonnormality, sample size, model specification, and number of bootstrap samples drawn from the resampling space. Accuracy of the test statistic p values is evaluated in terms of model rejection rates, whereas accuracy of bootstrap standard error estimates takes the form of bias and variability of the standard error estimates themselves.
This research proposes that frustration during the purchase process for high-technology durable goods has a significant effect on the probability that consumers will commit to a technology and make a purchase. In order to explore the effects of consumer frustration on the purchase process, a scale is developed that reveals that frustration in high-technology decision environments is composed of two dimensions, processing frustration and frustration with the pace of technological change. These dimensions of frustration have a significant effect on consumer choice behavior. While processing frustration significantly reduces the probability of commitment to a technology, the probability of making a decision is significantly lower when consumers are frustrated with the pace of technological change. © 2004 Wiley Periodicals, Inc.
As new technological innovations are rapidly introduced and changed, identifying an individual characteristic that has a persistent effect on the acceptance decisions across multiple technologies is of substantial value for the successful implementation of information systems. Augmenting prior work on individual innovativeness within the context of information technology, we developed a new measure of adopter category innovativeness (ACI) and compared its effectiveness with the existing measure of personal innovativeness in IT (PIIT). Further, we examined two alternative models in which the role of individual innovativeness was theorized differently—either as a moderator of the effects the perceived innovation characteristics of usefulness, ease of use, and compatibility have on future use intention (moderator model) or as a direct determinant of the innovation characteristics (direct determinant model). To ensure the generalizability of the study findings, two field studies (N= 634) were conducted, each of which examined the two models (moderator and direct determinant) and measured individual innovativeness using the two measures (ACI and PIIT). Study 1 surveyed the online buying practices of 412 individuals, and Study 2 surveyed personal digital assistant adoption of 222 healthcare professionals. Across the markedly different adoption contexts, the study results consistently show that individual innovativeness is a direct determinant of the innovation characteristics, and the two measures share many commonalities. The new measure offers some additional utilities not found in the PIIT measure by allowing individuals to be directly classified and mapped into adopter categories. Implications are drawn for future research and practice.
This research aims to explain the adoption of mobile gaming based on a refined model of Rogers' adoption theory, including context-specific factors and consumer traits. Overall, the empirical findings suggest that perceived risk plays a crucial role in the adoption process, followed by complexity and compatibility. Moreover, through cluster analysis we identified three consumers segments, termed “Value Seekers,” “Risk Avoiders,” and “Game Players.” Whereas perceived risk remains the most important factor for the Risk Avoiders, Value Seekers also are concerned about compatibility. Game Players emphasize navigation, communicability, and payment options. © 2004 Wiley Periodicals, Inc. and Direct Marketing Educational Foundation, Inc.
This paper consolidates the state of academic research on "innovation". Based on a systematic review of literature published over the past 27 years, we synthesize various research perspectives into a comprehensive multi-dimensional framework of organizational innovation - linking leadership, innovation as a process, and innovation as an outcome. We also suggest measures of determinants of organizational innovation and present implications for both research and managerial practice. Copyright (c) 2009 Blackwell Publishing Ltd and Society for the Advancement of Management Studies.
Drawing upon organizational culture and institutional theory, this study investigates how institutional pressures motivate the firm to adopt Internet-enabled Supply Chain Management systems (eSCM) and how such effects are moderated by organizational culture. The results of a survey of 131 firms suggest that the dimensions of institutional pressures (i.e., normative, mimetic, and coercive pressures) have differential effects on eSCM adoption intention. While mimetic pressures are not related to eSCM adoption intention, normative and coercive pressures are positively associated with eSCM adoption intention. In addition, organizational culture (i.e., flexibility orientation and control orientation) plays different roles in the relationships between these three dimensions of institutional pressures and eSCM adoption intention. While flexibility orientation negatively moderates the effects of coercive pressures and positively moderates the effects of mimetic pressures, control orientation positively moderates the effects of coercive and normative pressures and negatively moderates the effects of mimetic pressures. Implications and suggestions for future research are provided.
We champion the view that a richer understanding of electronic markets is obtained when their implications for consumers are jointly studied from social, economic, and psychological perspectives. We adopt this perspective triad to build a Social–Economic–Psychological (SEP) Model of technology adoption and usage, and apply the model to understand and explain the behavior of online investors. The SEP model provides a foundation for a multidisciplinary approach to the study of electronic markets and online consumers in the fields of MIS, marketing, and finance.
Advances in the publishing world have emerged new models of digital library development. Open access publishing modes are expanding their presence and realize the digital library idea in various means. While user-centered evaluation of digital libraries has drawn considerable attention during the last years, these systems are currently viewed from the publishing, economic and scientometric perspectives. The present study explores the concepts of usefulness and usability in the evaluation of an e-print archive. The results demonstrate that several attributes of usefulness, such as the level and the relevance of information, and usability, such as easiness of use and learnability, as well as functionalities commonly met in these systems, affect user interaction and satisfaction.
This paper theorizes how leadership affects ERP implementation by fostering the desired organizational culture. We contend that ERP implementation success is positively related with organizational culture along the dimensions of learning and development, participative decision making, power sharing, support and collaboration, and tolerance for risk and conflicts. In addition, we identify the strategic and tactical actions that the top management can take to influence organizational culture and foster a culture conducive to ERP implementation. The theoretical contributions and managerial implications of this study are discussed.
We conducted a quantitative meta-analysis of previous research on the technology acceptance model (TAM) in an attempt to make well-grounded statements on the role of subjective norm. Furthermore, we compared TAM results by taking into account moderating effects of one individual-related factor (type of respondents), one technology-related factor (type of technology), and one contingent factor (culture). Results indicated a significant influence of subjective norm on perceived usefulness and behavioral intention to use. Moderating effects were found for all three factors. The findings yielded managerial implications for both intra-company and market-based settings.
In spite of many theoretical models, the role of exogenous factors in accepting object-oriented technology has not been satisfactorily demonstrated. By comparing two competing models, our study examined the role and location of exogenous variables in explaining user acceptance of object-oriented technology. Based on the results, we developed a new model that combined the key ideas of both TAM and TPB and showed that both models are necessary in understanding the unique role of each exogenous variable.
The technology acceptance model (TAM) is one of the most influential research models in studies of the determinants of information systems/information technology (IS/IT) acceptance. In TAM, perceived usefulness and perceived ease of use are hypothesized and empirically supported as fundamental determinants of user acceptance of a given IS/IT. A review of the IS and psychology literature, however, suggests that perceived usefulness can be of two distinct types: near-term usefulness and long-term usefulness. This paper reviews the concept of perceived usefulness and modifies TAM to include the two types of perceived usefulness. Data collected from nearly 285 administrative/clerical staff in a large organization were tested against the modified model using the structural equation modeling approach. The results of the study showed that, even though perceived near-term usefulness had the most significant influence on the behavioral intention to use a technology, perceived long-term usefulness also exerted a positive, though lesser, impact. No significant, direct relationship was found between ease of use and behavioral intention to use a technology. Implications of the findings and future research areas are discussed.
Past research in the area of information systems acceptance has primarily focused on initial adoption under the implicit assumption that IS usage is mainly determined by intention. While plausible in the case of initial IS adoption, this assumption may not be as readily applicable to continued IS usage behavior since it ignores that frequently performed behaviors tend to become habitual and thus automatic over time. This paper is a step forward in defining and incorporating the "habit" construct into IS research. Specifically, the purpose of this study is to explore the role of habit and its antecedents in the context of continued IS usage. Building on previous work in other disciplines, we define habit in the context of IS usage as the extent to which people tend to perform behaviors (use IS) automatically because of learning. Using recent work on the continued usage of IS (IS continuance), we have developed a model suggesting that continued IS usage is not only a consequence of intention, but also of habit. In particular, in our research model, we propose IS habit to moderate the influence of intention such that its importance in determining behavior decreases as the behavior in question takes on a more habitual nature. Integrating past research on habit and IS continuance further, we suggest how antecedents of behavior/behavioral intention as identified by IS continuance research relate to drivers of habitualization. We empirically tested the model in the context of voluntary continued WWW usage. Our results support the argument that habit acts as a moderating variable of the relationship between intentions and IS continuance behavior, which may put a boundary condition on the explanatory power of intentions in the context of continued IS usage. The data also support that satisfaction, frequency of past behavior, and comprehensiveness of usage are key to habit formation and thus relevant in the context of IS continuance behavior. Implications of these findings are discussed and managerial guidelines presented.
Individual beliefs about technology use have been shown to have a profound impact on subsequent behaviors toward information technology (IT). This research note builds upon and extends prior research examining factors that influence key individual beliefs about technology use. It is argued that individuals form beliefs about their use of information technologies within a broad milieu of influences emanating from the individual, institutional, and social contexts in which they interact with IT. We examine the simultaneous effects of these three sets of influences on beliefs about usefulness and ease of use in the context of a contemporary technology targeted at autonomous knowledge workers. Our findings suggest that beliefs about technology use can be influenced by top management commitment to new technology and the individual factors of personal innovativeness and self-efficacy. Surprisingly, social influences from multiple sources exhibited no significant effects. Theoretical and practical implications are offered.
We present a review and analysis of the rich body of research on the adoption and diffusion of IT-based innovations by individuals and organizations. Our review analyzes 48 empirical studies on individual and 51 studies on organizational IT adoption published between 1992 and 2003. In total, the sample contains 135 independent variables, eight dependent variables, and 505 relationships between independent and dependent variables. Furthermore, our sample includes both quantitative and qualitative studies. We were able to include qualitative studies because of a unique coding scheme, which can easily be replicated in other reviews. We use this sample to assess predictors, linkages, and biases in individual and organizational IT adoption research. The best predictors of individual IT adoption include Perceived Usefulness, Top Management Support, Computer Experience, Behavioral Intention, and User Support. The best predictors of IT adoption by organizations were Top Management Support, External Pressure, Professionalism of the IS Unit, and External Information Sources. At the level of independent variables, Top Management Support stands as the main linkage between individual and organizational IT adoption. But at an aggregate level, two collections of independent variables were good predictors of both individual and organizational IT adoption. These were innovation characteristics and organizational characteristics. Thus, we can consistently say that generic characteristics of the innovation and characteristics of the organization are strong predictors of IT adoption by both individuals and organizations. Based on an assessment of the predictors, linkages, and known biases, we prescribe 10 areas for further exploration.Journal of Information Technology (2006) 21, 1–23. doi:10.1057/palgrave.jit.2000056 Published online 10 January 2006
This paper reports on the development of an instrument designed to measure the various perceptions that an individual may have of adopting an information technology (IT) innovation. This instrument is intended to be a tool for the study of the initial adoption and eventual diffusion of IT innovations within organizations. While the adoption of information technologies by individuals and organizations has been an area of substantial research interest since the early days of computerization, research efforts to date have led to mixed and inconclusive outcomes. The lack of a theoretical foundation for such research and inadequate definition and measurement of constructs have been identified as major causes for such outcomes. In a recent study examining the diffusion of new end-user IT, we decided to focus on measuring the potential adopters' perceptions of the technology. Measuring such perceptions has been termed a "classic issue" in the innovation diffusion literature, and a key to integrating the various findings of diffusion research. The perceptions of adopting were initially based on the five characteristics of innovations derived by Rogers (1983) from the diffusion of innovations literature, plus two developed specifically within this study. Of the existing scales for measuring these characteristics, very few had the requisite levels of validity and reliability. For this study, both newly created and existing items were placed in a common pool and subjected to four rounds of sorting by judges to establish which items should be in the various scales. The objective was to verify the convergent and discriminant validity of the scales by examining how the items were sorted into various construct categories. Analysis of inter- judge agreement about item placement identified both bad items as well as weaknesses in some of the constructs' original definitions. These were subsequently redefined. Scales for the resulting constructs were subjected to three separate field tests. Following the final test, the scales all demonstrated acceptable levels of reliability. Their validity was further checked using factor analysis, as well as conducting discriminant analysis comparing responses between adopters and nonadopters of the innovation. The result is a parsimonious, 38-item instrument comprising eight scales which provides a useful tool for the study of the initial adoption and diffusion of innovations. A short, 25-item, version of the instrument is also suggested.
The development of commitment to change is an underresearched area especially in non-western settings. The aim of the present study was to determine whether employability can moderate the negative effects of job insecurity on individuals' commitment to change. A survey method approach was used to collect 149 responses from managers of a large public sector organization in Pakistan undergoing restructuring. Hierarchical multiple regression results suggest that employability is an important coping resource during organizational change as it helps mitigate the negative effects of job insecurity on the most desirable form of commitment to change, namely affective commitment to change. Theoretical and practical implications of the study are discussed. [ABSTRACT FROM AUTHOR] Copyright of Economic & Industrial Democracy is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
This study develops a theoretical framework that integrates institutional and network perspectives on the form and consequences of administrative innovations. Hypotheses are tested with survey and archival data on the implementation of total quality management (TQM) programs and the consequences for organizational efficiency and legitimacy in a sample of over 2,700 U.S. hospitals. The results show that early adopters customize TQM practices for efficiency gains, while later adopters gain legitimacy from adopting the normative form of TQM programs. The findings suggest that institutional factors moderate the role of network membership in affecting the form of administrative innovations adopted and provide strong evidence for the importance of institutional factors in determining how innovations are defined and implemented. We discuss implications for theory and research on institutional processes and network effects and for the literatures on innovation adoption and total quality management.