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Artificial intelligence: The light and the darkness
Abstract
That artificial intelligence (AI) has the potential to provide significant benefits is generally accepted by both practitioners and scholars. However, the dark side of AI is less discussed, and less understood. In this paper, the authors first classify the wellspring of AI benefits in both B2C and B2B settings. In B2C settings AI benefits are primarily via customized experiences, while B2B AI benefits are manifested via business efficiencies. Next, guided by the relationship marketing literature, the authors identify the drivers of the dark side of AI - lack of trust and power asymmetries, with lack of trust being a stronger factor in B2C settings and power asymmetries being a stronger factor in B2B settings. Finally, the authors provide an organizing framework for understanding both the bright side and the dark side of AI, in both B2C settings and B2B settings. This paper is differentiated from prior work by its focus on B2B settings (most focus on B2C settings), and by its focus on the dark side of AI (most focus on the bright side of AI).
... Artificial Intelligence (AI) is a general-purpose technology that enables machines to simulate human cognitive processes, such as learning, reasoning, problem-solving, and decision-making (Haenlein and Kaplan). By leveraging advanced algorithms, vast datasets, and computational power, AI can identify patterns, generate insights, and automate complex tasks with unprecedented speed and accuracy (Grewal et al., 2021). These capabilities make AI a powerful driver of innovation, transforming industries by optimizing processes, enhancing decision-making, and enabling entirely new business models (Magistretti et al., 2019). ...
... AI is emerging as an outstanding digital technology which is gaining a growing interest from both academics and practitioners due to the potential impacts on process optimization, innovation in business practices, and reconfiguration of whole industries (Grewal et al., 2021;Paschen et al., 2020). Specifically, AI is a transformative technology unlike any other in history (Bughin, 2018) due to its ability to "self-scale, self-learn, and self-propagate" (Gupta et al., 2021, p. 3). ...
... Economically, a paradox arises between "black-box AI" and "explainable AI" due to the lack of transparency in AI functioning. While black-box AI's workings are unclear, leading to trust issues, explainable AI is more transparent and trusted, but may be seen as less impactful in terms of performance compared to black-box AI (Grewal et al., 2021). ...
This paper explores the bright and dark sides of artificial intelligence (AI) innovation for sustainable development. While most research has concentrated on the positive impacts of AI innovation, this study examines the paradoxical tension that arises between sustainable value creation and sustainable value destruction when managing AI innovation to achieve sustainable development. We conceptualize a model explaining the antecedents and the nature of this tension, and we discuss seven illustrative cases that exemplify the practical applicability of the model's elements. Our findings show that conflicting sub-objectives across environmental, social, and economic domains, along with the divergent interests of stakeholders, are key antecedents of the paradoxical tension. Furthermore, our model illustrates that sustainable value can be created by: (i) reducing grand challenges through automation in defining problems' root cause, and (ii) mitigating grand challenges through augmentation of firms' capabilities. However, we argue that sustainable value can be also destroyed when failing to address grand challenges or introducing new grand challenges. According to our study, this is due to predictable or unpredictable issues that arise during the design, development, or deployment of AI innovation. Finally, the discussion of our model further explores, through five theorical propositions, the specific elements of AI innovation that enable and amplify this paradoxical tension.
... Indeed, the motivation for this exploration is the realization that not all AIs are created equal. They are fundamentally different, with completely different implications for both ethics and efficacy (Grewal et al., 2021). Even better, the proliferation of popular press essays on the potential and the prospective negative consequences of AIs is not always helpful precisely because they often mishandle the issue of AI's heterogeneity. ...
... This situation limits the richness of services, the levels of privacy, and user representation, ultimately undermining the democratic, ethical, and fair use of these resources (Marcus & Teuwen, 2024;Ojha et al., 2024). The gap between what should be available and what is actually delivered by AI is defined by what manufacturers, providers, technologists, and policymakers call the unfair fatigue problems, data and model biases, language and communication biases, accessibility and scalability biases, technology and infrastructure biases, privacy and data utilization biases, and data and model contamination biases (Grewal et al., 2021;Sun et al., 2022). ...
... However, these extremely young individuals might not yet have distinct online intellectual datasets to fully exploit beyond the pre-informed questions typically advanced by teens, whose transcripts, focused attention, creative scientific curiosities, and discoveries are potentially passed on to AI autonomous systems for inference content use in scientific projects (Cao et al., 2023). • By advancing the metaverse functionalities and design to continually optimize scientific inquiry, reasoning, processing, access, accomplishments, casual science learning, and community building, future metaverse individuals, groups, and focused community events might arrive intellectually more capable, interested, and motivated to conjoin with AI autonomous entities to resolve urgent planetary challenges collectively (Grewal et al., 2021;Shafik, 2024f). • Additional inter-metaverse and metaverse-world collaborations for AI scientific inquiry and science-based learning are also likely to be seen. ...
This chapter explores the ethical, legal, and societal risks of Artifificial Intelligence (AI) and the Metaverse in scientific research and publishing. While AI aids data analysis and peer review, it risks perpetuating biases that could distort findings and compromise research integrity. The Metaverse, as a new digital space for academic engagement, introduces challenges like data privacy, intellectual property concerns, and opportunities for scientific fraud. Furthermore, algorithmic biases in digital publishing amplify visibility disparities, creating a digital divide. To address these issues, this chapter advocates for robust governance, ethical guidelines, and collaborative frameworks to ensure fairness, integrity, and trust in the evolving digital research landscape. It is imperative to know that AI is dangrous more than what we can stress in terms of its abilities, applications and services, the human race creating playing on self destraction trigger beyond research horizons.
... enefits for consumers (Ahmad et al., 2023) in order to know how do they feel about their AI products to market them better (Haleem et al., 2022). Perceived benefits are beliefs about the positive outcomes associated with a cognitive, affective or behaviour response of consumers to a real or perceived threat (Chandon et al., 2000;Liu et al., 2012). Grewal et. al (2021) suggest that realized and anticipated benefits of AI for consumers based on customized offers achieved through data-led personalization, optimization, and innovation. ...
... According to the majority of researchers, there are a few benefits of AI for consumers: enhances decision-making and problem solving (Sivarajah et al., 2017;Topol, 2019;Bastani et al., 2021;Chen et al., 2019), increases efficiency and productivity, customization (Grewal et al., 2021) as well as enhances consumers' experience (Trawnih et al., 2024), which is relating to the interactions between the consumer and the company during the consumer' journey, and encompasses multiple dimensions: emotional, cognitive, behavioural, sensorial, and social (Puntoni et al., 2021;Lemon and Verhoef, 2016;Brakus et al., 2009). ...
Background/Purpose
This article explores consumers’ perception of the benefits of intelligent service robots (ISR) in the purchasing process, their trust in artificial intelligence (AI), their perception of AI-related threats, and the impact of these variables on consumer attitudes toward AI. Additionally, the study examines the moderating effect of perceived AI-related threats on the relationship between perceived benefits and trust on one side and the formation of consumer attitudes toward AI on the other.
Methods
The research was conducted in the first half of 2024 on a judgmental sample of 224 employed consumers in the Republic of Slovenia. Data were collected through a structured online questionnaire. For the empirical analysis, a non-parametric approach using SEM-PLS modelling was applied to examine relationships between the studied research constructs.
Results
The findings indicate that perceived benefits of ISR have a strong and positive impact on consumer attitudes toward AI, while perceived AI-related threats strongly and negatively influence these attitudes. Moreover, the results reveal that perceived AI-related threats significantly and negatively moderate the effect of consumers’ perceived trust in AI on the formation of their attitudes toward AI.
Conclusion
The results of this study contribute significantly to the theoretical understanding of employed consumers’ attitudes toward AI. They also provide practical implications for companies in developing predictive models of consumer behaviour and defining effective marketing strategies to encourage AI adoption in the purchasing process.
... AI has advanced through various stages, employing methods such as deep learning, machine learning, and natural language processing, and is now integrated into fields ranging from robotics and computer vision to content creation, financial modelling, and medical diagnostics. The potential of AI to enhance human development has been widely acknowledged (Grewal et al., 2021), yet its societal and economic implications remain contested. ...
... One line of debate is whether robots and chatbots should be more human-like, or whether this would have the adverse effect of being perceived as disturbing (Pillai & Sivathanu, 2020;Yang et al., 2020). A related argument concerns empathy in service encounters, as AI may help understand guests -or prevent rational decision-making (Grewal et al., 2021;Xu et al., 2020). Notably, the reliability and trustworthiness of service robots are a concern for customers (Tussyadiah et al., 2020). ...
... A systematic review was not conducted because the unique search term that encompasses all the constructs in our framework appears not to exist. Our procedure is similar to other conceptual papers that aim to construct an original theoretical Gastronomy 2025, 3, 6 3 of 18 framework on consumer behavior and marketing (e.g., [34,35]). We primarily relied on the framework of meanings of concepts (evaluation, potency, and activity) for the cognitive responses [36,37] and the circumplex model of affect (Valence and Arousal) [38] for the affective responses. ...
... term that encompasses all the constructs in our framework appears not to exist. Our procedure is similar to other conceptual papers that aim to construct an original theoretical framework on consumer behavior and marketing (e.g., [34,35]). We primarily relied on the framework of meanings of concepts (evaluation, potency, and activity) for the cognitive responses [36,37] and the circumplex model of affect (Valence and Arousal) [38] for the affective responses. ...
Many consumers today pursue health goals to adopt healthier behaviors, and interest in promoting healthy eating habits in gastronomy is growing. Empirical evidence demonstrates that sensory cues (e.g., food color, food shapes, and background music) influence healthy eating behavior. However, the theoretical understanding of how sensory cues shape healthy food choices remains unclear. Specifically, this study develops the sensory–healthy eating model, a theoretical framework that explains how and when sensory cues influence healthy eating behavior (e.g., food choices and intake). By integrating related theories and empirical findings across interdisciplinary fields, we identify which sensory cues shape healthy eating and the psychological processes through which they operate. The theoretical model proposes that (1) sensory cues evoke cognitive (higher evaluation, lower potency, lower activity) and/or affective responses (positive valence, lower arousal), (2) these responses shape the perceived healthiness of foods based on their characteristics and quantity, and (3) the influence of perceived food healthiness on healthy eating behavior is stronger for consumers with health goals or motives. Our model provides a valuable framework for researchers and practitioners in marketing, food science, and gastronomy to promote healthy eating behavior.
... Second, AI adoption might evoke huge negative side effects. For instance, the technology can create strong power asymmetries between firms (Grewal, Guha, Satornino & Schweiger, 2021). These, in turn, can evoke very opportunistic behavior, especially when firms depend on external AI capabilities for running their business. ...
... In other words, whereas firms should indeed continuously search for business opportunities, it is equally important to critically evaluate them regarding potential negative consequences. For that reason, we encourage scholars to better investigate the dark side of AI for businesses and strategy (Cheng et al., 2022;Grewal et al., 2021). It will be important to determine for which kinds of tasks AI can be adopted and for which not. ...
... Conversely, skepticism about a system's or tool's functionality and doubts about their risks can foster negative attitudes among users (Hajiheydari et al., 2021). For instance, Grewal et al. (2021) found that although individuals appreciate the ability of AI technologies to deliver personalized and engaging experiences, they often avoid them because of privacy concerns. This presents a paradox where the immediate benefits of AI technologies, such as enhanced relevance and user satisfaction, are outweighed by long-term fears of data misuse and distrust. ...
... We explain our result by the characteristics and function of the technology under investigation rather than by the study's setting (i.e., the healthcare). The technology in our study is advanced (e.g., Sestino and De Mauro, 2022) and, crucially, suitable for managerial purposes because it provides concrete, tangible support for decision-making processes, thereby enhancing its appeal and adoption potential (Grewal et al., 2021;Hengstler et al., 2016). ...
Artificial intelligence (AI) is revolutionizing healthcare by introducing novel treatments and applications, thereby transforming the sector. However, the complexity, ambiguity, and inherent risks associated with AI can create tensions for healthcare workers that may result in stress, anxiety, and discomfort when they make decisions. These tensions are paradoxical in nature as they may present conflicting demands that can persist over time and develop into seemingly irrational situations. Understanding how these paradoxical tensions affect healthcare workers' responses to AI is crucial in addressing their concerns. This study investigates the role of paradoxical tensions and the paradoxical mindset in shaping healthcare workers' responses to AI. The study examines how these two factors influence individuals' intention to adopt AI systems and tools and evaluates the users' satisfaction with them. Using a quantitative survey design, data were collected from 357 healthcare workers. The results, based on regression analysis, indicate that paradoxical tensions positively influence both individuals' intention to adopt AI systems and tools and their satisfaction with the current use of AI systems and tools. The results also indicate that the paradoxical mindset positively mediates these relationships.
... At the individual level, the negative effect of AI is reflected in privacy concerns and product and content recommendations. AI gains deep insights into privacy concerns (Grewal, Guha, Satornino & Schweiger, 2021). For example, voice assistants like Alexa could predict key moments by analyzing the customers' voices with AI technology. ...
A new form of human–machine collaborative capabilities has been called to complement traditional capabilities to ensure higher but more responsible performance. We reviewed the extant literature on leadership in the artificial intelligence context to identify the leaders’ essential artificial intelligence-driven capabilities and synthesize the systematic review findings into an integrated conceptual framework to highlight how artificial intelligence-driven organizations could lead more responsibly. We conducted the systematic review and thematic analysis based on 37 papers identified from Emerald Insight, EBSCOhost Business Source Complete, and ScienceDirect databases. We found organizational leaders require technical, adaptive, and transformational capabilities to lead in an artificial intelligence-driven disruptive organizational environment. Our findings contribute to dynamic managerial capability and responsible leadership for performance theories by showing how these three uncovered capabilities enable organizational leaders to deploy dynamic managerial capabilities – sensing, seizing and reconfiguring more responsibly.
... Grewal, D., Guha, A., Satornino, C. B., & Schweiger, E. B. (2021). Artifcial intelligence: The light and the darkness. ...
The Role of Artificial Intelligence (AI) in the Legal Sector: Opportunities and Challenges" examines and explores the transformative impact of artificial intelligence on the legal sector and practice. From revolutionizing legal research and practice to enhancing decision-making capabilities and processes, AI presents both promising opportunities and significant challenges for legal professionals. This book provides a comprehensive overview of the role of AI in law, examining its potential to increase efficiency, improve access to justice, and transform legal practice. This comprehensive book also provides insight into how AI is reshaping legal practices, from increasing efficiency to transforming decision-making processes, and examines the ethical and regulatory considerations that come with the integration of AI into legal frameworks. ABOUT THE EDITORS Dr. B.R. Mourya, Assistant Professor at Moradabad's Teerthanker Mahaveer University His academic achievements includes an LL.M. and LL.D. from Ch. Charan Singh University, Meerut, as well as a Bachelor of Laws from Awadh University, Faizabad. He has over fourteen years of teaching and research experience. He has contributed to and delivered presentations on over 35 papers at conferences, both domestic and international in scope. Additionally, he has contributed to over twenty-five publications in national and international journals and two books on children and domestic violence. He supports social reform as a social activist and composes articles on socio-legal and techno-legal issues.
... However, Gen-AI was seen as lacking a "human touch" regarding communication. Grewal et al. (2021) highlighted the need to balance AI and human input regarding communication in business-to-customer relationships; Du et al. (2021) further explained that social norms play a key role in AI-human interaction. In this study, it was found that emphasis needs to be placed not so much on the balance between AI-human communication, but on developing Gen-AI to recognize various dimensions of the human factor. ...
This research explores the benefits and challenges associated with Gen-AI tools for business improvement from the perspective of business players and academicians. Data comprises of interviews with 7 academicians and 10 European industry players. Several benefits were recognized from the use of Gen-AI in business context, mainly resolution of complex situations and operational improvement of processes. At the optimization level, industry players perceived Gen-AI with more potential than academicians. Advantages of Gen-AI were identified as strongly aligned with data quality. Both academicians and industry players highlight difficulty of identification, collection, and update of relevant data, together with the needed parsimony in decision-making about Gen-AI-produced results. This study corroborates the current awareness of scarcity in trained people to explore the full potential of Gen-AI and highlights the resistance towards its adoption by top management, especially in SMEs, which due to their scale may lack structured data and act more risk averse.
... As a result, customers are likely to continue using the AI-powered services, as they are satisfied with the experience (H1b). Regarding the relationship between customisation and brand experience, AI enables customised services at scale, providing customers with tailored recommendations, content, and experiences based on their individual preferences and behaviours (Grewal et al., 2021). Customisation creates a sense of delight and satisfaction for the customer, contributing to a positive brand experience (H1c). ...
Purpose
Culture plays a pivotal role in influencing the customer journey when artificial intelligence (AI) is used, helping to foster consumer–brand interactions. Based on the stimulus–organism–response (SOR) model, this study examined the role of culture on AI–customer–brand interactions, comparing Belt and Road countries.
Design/methodology/approach
Structural equation modelling was used to analyse 300 responses from participants in Hong Kong and 398 responses from participants in Turkey to a questionnaire.
Findings
The findings indicated that AI affected brand experience and brand preference in both Hong Kong and Turkey. Multi-group analysis revealed that customisation exerted stronger influences on brand experience for the Hong Kong group, while interaction had a stronger effect on brand experience in the Turkey group. Most importantly, the effect of AI marketing efforts on brand experience was found to be moderated by cultural differences.
Research limitations/implications
The study’s findings advance knowledge of the crucial role of cultural factors in AI–consumer–brand relationships. These theoretical implications highlight the necessity of integrating cultural intelligence into AI-driven branding strategies.
Practical implications
Acknowledging this cultural embeddedness can help market practitioners for brand building and policymakers craft more informed and impactful guidelines governing AI–marketing practices and AI–brand management among Belt and Road countries.
Originality/value
Existing studies on AI–brand interactions have each focused on a single country. There is a glaring lack of research on cross-cultural differences. To fill the gap, this study adopted a cross-cultural perspective to investigate differences in AI–brand interactions between two Belt and Road countries.
... Based on the above, the extant literature highlights a gap in our current understanding of how businesses can strategically incorporate and leverage the capabilities of AI to develop and support customer relationships, avoiding associated issues such as lack of trust, power asymmetry, and privacy and ethical concerns (Chatterjee et al., 2024;Chaturvedi et al., 2024;Grewal et al., 2021). The importance of this research is further underscored by fast-paced and frequently changing market conditions (Pillai et al., 2022;Zhang et al., 2021) where customer preferences undergo frequent shifts throughout the customer journey, posing significant challenges for RM (Dubey et al., 2021). ...
This study explores the interlinkages between artificial intelligence (AI), dynamic capabilities, and relationship marketing (RM) outcomes. Drawing upon insights from dynamic capabilities and RM theory, this study de-lineates the strategies and initiatives organizations can adopt using machine learning (ML) and AI to enhance their adaptability to changing market dynamics and customer preferences, in order to develop and maintain stronger relationships with their customers. Based on qualitative data from 67 interviews with managers in different organizations in India, this study contributes to existing theoretical knowledge and managerial practices , as it proposes a comprehensive research framework that demonstrates how AI technologies can enhance customer relationships throughout the entire customer journey. More specifically, it adopts a dynamic capabilities lens to extend our understanding of the marketing applications of AI by conceptualizing the dual role of AI as (a) a distinct organizational capability and (b) an enabler of dynamic capabilities, improving firms' position to sense, seize, and transform organizational resources and fortify customer relationships. Our findings also highlight several facilitators and barriers to the adoption of AI, both as a dynamic capability and as an enabler for RM.
... They have managed to gain extensive attention from practitioners as well as academicians, due to its promise of unparalleled benefits (Davenport et al., 2020). These tools possess the power of offering entertaining, personalized services accompanied by social interaction (Grewal et al., 2021). The application of these tools has had a resounding impact on consumer experience, bringing about a process revolution in multifarious sectors like healthcare, education, retailing, tourism, healthcare, banking and telecommunications (Forbes, 2022). ...
Purpose
This study aims to delve into the cultural differences between developing and developed countries pertaining to the negative behavioral fallouts of adopting anthropomorphized humanoids or robots. The underlying motivation (for the study) lies in the fact that these countries are at the vanguard of artificial intelligence development and deployment, albeit with varying levels of development and acceptance.
Design/methodology/approach
The research framework used in this study is guided by the computers as social actors framework, expectancy disconfirmation theory, and is supported by the uncanny valley theory. The data was collected in two contexts using a probabilistic sampling technique, N= 782 (n1 = 393 respondents: developed country, i.e. USA, and n2 = 389 respondents: developing country, i.e. India). The partial least square analysis was carried out for the proposed model’s validation and hypotheses testing.
Findings
This study shows that in developed countries, the consumers have high preinteraction expectations while they express comparatively more dark behavior than respondents from developing countries. Consumers in developed countries focus on anthropomorphic knowledge and design cues, while in developing countries, they pay attention to utility and functionality. Finally, the results also suggest that female respondents from developed countries exhibit more resilience toward anthropomorphized agents in adopting and expressing dark behavior.
Originality/value
The present research makes essential contributions to anthropomorphism literature. First, this study explored the impact of the interaction effect on the dark side, a rather under-explored domain in regret literature. Second, this study provides evidence for cross-cultural variations pertaining to the dark side impacts. Finally, this study adds to the impact of demographic variables, showing that gender played a significant role in moderating relationships in the proposed model.
... When used in conjunction using suitable applications, successful ST produces a competent, self-assured sales team that can close deals, have profitable discussions, and increase income (Gurram et al., 2023). In alongside instruction, AI evaluates consumer behavior, finds possible prospects, and improves sales tactics (Grewal et al., 2021). Automating routine operations frees up salespeople to concentrate on developing partnerships. ...
AI-driven strategies are changing training and performance within the ever-changing sales environment. Our quantitatively-based empirical research in Pakistan explores the impact of AI upon salespersons. Data was collected using Simple random sampling from 178 pharma representatives along with managers via email, telephone, and physical questionnaires. SEM-PLS was the analytical instrument we used. The findings provide important new information. Training solutions with AI-driven techniques provide customized instructional methods by evaluating the efficiency information for particular sales representatives. On the basis of one’s abilities and areas for development, particular training materials, programs, and activities are suggested. Sales representatives can quickly adjust approaches with immediate reaction. AI adapts training materials constantly according to achievement, preserving a rigorous and fruitful educational setting. Role-playing exercises are made easier by AI-generated authentic selling situations. Representatives acquire meaningful expertise above typical scenarios by practicing managing difficult clients and negotiating complex scenarios. AI improves creating strategies and making decisions by analyzing sales encounter information. Strategies that work and those that could use better are made clear. Irrespective of staff size, based on artificial intelligence training scalable effortlessly to provide identical standards throughout sales departments. Organizations need to implement these game-changing strategies as AI proceeds to alter sales in order to survive in a cutthroat industry. Lastly, this paper provide practical implications for different stakeholders and future research directions related to this study.
... It leads to power asymmetries. (De Bruyn et al., 2020;Grewal et al., 2021;Schultz et al., 2024). Another problem is algorithmic bias. ...
The digital age has brought alternative ways for consumers to do their everyday tasks. Searching for information, sharing opinions, and buying products is now easier than ever. However, technological advancements and digital transformation drastically altered consumer behavior and marketing activities. In order to satisfy consumers' needs and wants better and more effectively, companies increasingly use digital marketing tools. This shift from traditional marketing methods to modern marketing techniques is growing momentum in using digital technologies such as artificial intelligence. A literature review reveals increasing studies covering artificial intelligence in digital marketing. Therefore, this study aims to analyze this field's thematic evolution and identify trends and patterns in artificial intelligence in digital marketing. With this purpose, a bibliometric analysis was conducted on articles written in English and published in SSCI and SCI-E-indexed journals in the Web of Science database. This study comprehensively investigates research in this field, visualizes the literature through a scientific mapping approach, and intends to shed light on future studies to be conducted in this research area.
... The connection between self-discrepancy and trust is particularly significant since trust in both human and AI agents is a key driver of positive customer perceptions (e.g., Chi et al. 2021;Noble and Mende 2023). Indeed, the absence of relational human-like characteristics and uniqueness may have adverse results on customer perceptions of trust during service encounters (Grewal et al. 2021;Robinson et al. 2020). Moreover, there is also a negative association between bragging and trust perceptions (Chan, Reese, and Ybarra 2021), since negative trust cues can signal self-interest and impede persuasion (Packard 2015). ...
As companies actively invest in self‐promotion of Artificial Intelligence (AI) empowered services to sustain their competitive advantage, there is a growing potential for such promotional activities to backfire. Bridging signaling theory with the resource‐based view, this research reveals that companies’ self‐promotion of AI resources can reduce customers’ willingness to engage with AI‐based (vs. human‐based) services. Four studies, including text mining and experiments, demonstrate that companies’ self‐promotion of AI‐based resources has a detrimental effect on willingness to engage, and concurrently perceived as exaggeration. In contrast, companies’ self‐promotion about human‐related resources yields beneficial outcomes, since such promotional signals contribute to the enhancement of human capital. The findings suggest that self‐discrepancy and trust are the key underlying factors driving the effects as customers may experience a discrepancy between their expectations of human‐like service interactions and actual AI capabilities. Additionally, findings reveal the moderating effect of honest (vs. self‐promotional) framing on the relationship between service type (AI vs. human) and willingness to engage. Customer perceptions of AI appear less influenced by presentation style compared to perceptions of human resources. This research provides valuable insights into how customers respond to companies’ self‐promotion of AI resources and emphasizes the need for promotional alignment with customers’ expectations about AI.
... However, the specific effectiveness and extent of policy implementation remain uncertain, considering the accuracy of AI-generated text (Hu, 2024). Academics across disciplines have been quick to adopt new technologies and tools to assist with research and practice (Grewal, Guha, Satornino, & Schweiger, 2021). The ongoing discussions include considerations about listing ChatGPT as a co-author, requirements for disclosing AI usage in the acknowledgments section, and the potential necessity of submitting a separate declaration document to journals specifying AI-generated content (Polonsky & Rotman, 2023). ...
This study investigates the use of AI tools in academic writing through analysis of AI usage declarations in journals. Using a mixed-methods approach combining content analysis, statistical analysis, and text mining, this research analyzed 168 AI declarations from 8,859 articles across 27 categories. Results show that ChatGPT dominates academic writing assistance (77% usage), with significant differences in tool usage between native and non-native English speakers (p = 0.0483) and between international and non-international teams (p = 0.0012). The study reveals that improving readability (51%) and grammar checking (22%) are the primary purposes of AI tool usage. These findings provide insights for journal policy development and understanding the evolving role of AI in academic writing.
... A distinct insight from the findings is the detailed exploration of not only the skills gap but also the misunderstanding and mistrust that exist among retail employees and customers regarding AI. Extant research provides a broader understanding of AI adoption readiness, perceived power asymmetry, and fear of the unknown (Bonetti et al., 2023;Grewal et al., 2021;Yam et al., 2023). The findings of this study, however, illustrate a more granular perspective on these emotion-laden aspects, emphasizing the gaps in people's understanding of working with AI. ...
In a complex business environment, the quest for intelligent solutions has placed artificial intelligence (AI) at the center of many operational business decisions, particularly within the retail sector. As retailers’ investment in AI disrupts value chains, they must adapt to organizational changes, yet limited research exists on how to address organizational challenges when integrating AI into the retail value chain. Using 23 expert interviews with retail
executives and AI vendors, this study investigates the socio-technical challenges retailers encounter when integrating AI into their value chain and offers strategies to address these organizational hurdles. We identify micro-, meso-, and macro-level factors impacting AI implementation in retail and propose an AI Implementation Compass to address the change management process. This framework serves as a guide to navigate the complex landscape of AI adoption, emphasizing a holistic approach that considers not only internal organizational dynamics,
but also external market forces.
... The benefits of AI, e.g., enhanced efficiency, effectiveness, and decision-making accuracy, are well-documented [7][8][9][10]. Still, many individuals and organizations remain issues, while algorithmic biases erode perceptions of fairness and equity. ...
The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxiety, reflecting existential concerns about human identity and autonomy. We demonstrate a U-shaped relationship between AI anxiety and usage, where moderate engagement reduces anxiety, and high or low levels increase it. Perceived utility, interest, and attainment significantly correlate with AI engagement, while frequent AI usage is linked to high dependency but not to anxiety. These findings highlight the dual role of psychological factors in hindering and alleviating AI usage. This study enriches the understanding of emotional and motivational drivers in AI adoption and highlights the importance of balanced implementation strategies to foster sustainable and effective AI integration while mitigating the risks of over-reliance.
... Moreover, within the scope of scholarly, media, and policy analysis that does engage with business imperatives, the focus has largely been on Business-to-Consumer (B2C) platforms, leaving Business-to-Business (B2B) dynamics unexplored [29]. The expansion of uses of AI and computing systems for non-consumer facing purposes, especially following the increase of Software-as-a-Service, makes understanding responsible innovation practices in this domain critical [45]. Feike and Rosch [29] provide a helpful comparison between B2B and B2C platform, which serves as a launching point for B2B-centered responsible innovation frameworks. ...
Technology development practices in industry are often primarily focused on business results, which risks creating unbalanced power relations between corporate interests and the needs or concerns of people who are affected by technology implementation and use. These practices, and their associated cultural norms, may result in uses of technology that have direct, indirect, short-term, and even long-term negative effects on groups of people and/or the environment. This is especially critical in B2B (business-to-business) settings due to the potential for responsibility gaps to emerge in such contexts where technologies are sold to one or more third party company obfuscating downstream impacts. This paper contributes a formative framework -the Responsible and Inclusive Technology Framework- that orients critical reflection around the social contexts of technology creation and use; the power dynamics between self, business, and societal stakeholders; the impacts of technology on various communities across past, present, and future dimensions; and the practical decisions that imbue technological artifacts with cultural values. The framework and its components were iteratively developed based on observations of 10 internal exploratory workshops conducted with a total of 49 participants across the company. We expect that the use of the Responsible and Inclusive Technology framework, especially in B2B industry settings, will serve as a catalyst for more intentional and socially-grounded practices, thus bridging the responsibility and principles-to-practice gap.
... However, consumers tend to lack trust in automation (e.g. Grewal et al., 2021) and largely prefer person-to-person interactions (e.g. Mays et al., 2022), particularly in medical (Longoni et al., 2019) and symbolic (Granulo et al., 2021) consumption contexts. ...
Purpose
The marketplace is becoming increasingly automated, with consumers frequently expected to interact with machines. Not all consumers are receptive to this trend. We examine how the individual difference of speciesism impacts consumer reactions to automation in the marketplace.
Design/methodology/approach
We conducted three studies, including an exploratory correlational survey and two two-factor studies.
Findings
Study 1 provides survey evidence of a positive relationship between one’s level of speciesism and their belief that customer service automation is justified. Study 2 finds that speciesists have more favorable attitudes toward brands using automated (vs human) customer service. Study 3 finds that the more speciesists perceive that tasks they are required to perform at their own work are illegitimate (i.e. unreasonable), the more favorable their reactions to automation, which provides support for our theorizing that speciesists appreciate automation’s ability to relieve humans of such work tasks.
Practical implications
We recommend that marketers target speciesists as early adopters of chatbots. Further, brands targeting customers likely to be high on speciesism can benefit from adopting chatbots for routine tasks, as this can improve this segment’s brand attitudes.
Originality/value
This research identifies that speciesists, people who strongly ascribe to the belief that humans are superior to other species, are particularly receptive to automation in customer service (in the form of chatbots). We provide evidence suggesting that speciesists appreciate that automation relieves their fellow humans of automatable tasks.
... Unlike static survey data, AI models handle real-time data, yielding dynamic predictions. AI is a very powerful tool in climate change research and interventions; it offers a more flexible approach than forecasting public engagement with climate policies (Grewal et al., 2021). ...
This study is trying to assess the ability of AI to predict the opinion of people about climate change by giving importance to the psychological and behavioral facets of such opinions. The survey was conducted on 300 people in Pakistan by a self-administered questionnaire. The data analysis done in regard to these factors included the implementation of multiple statistical methods such as correlation, regression, and post-hoc analysis that would consider the complex interplay between several psychological factors in question. Some of the factors involved cognitive biases, emotional responses, and what the layperson in society thinks about climate change. The findings presented with this analysis clearly showed that the models in this study, driven by artificial intelligence, were far better than the traditional survey methods used for a long time. In this regard, the AI-driven models had an astonishing accuracy rate of 87%, whereas the accuracy rate of only 72% came with traditional approaches used in this study and was proven to be highly statistically significant, as indicated by the p-value being below 0.05. The regression analysis results showed that some psychological factors such as cognitive bias with a beta value of 0.75 and emotional responses at a beta value of 0.65 strongly and significantly predict the attitude of the people regarding climate change. Apart from the above primary analysis, post-hoc analysis brought a number of other insights to light, especially regarding the important role of social identity in forming a public perception related to climate change issues. The above findings depict promising scope for AI technology in refining and improving the strategy of climate change communication through an appropriate application of psychological knowledge within the predictive model.
... Moreover, certain forecasts indicate that, in the upcoming future, AI technologies might surpass human medical professionals in surgical procedures. Moreover, AI has been integrated into the nursing sector, posing challenges for nursing staff in terms of providing personalized patient care at a reduced cost [10,41,42]. ...
Background: Digitalization and artificial intelligence technologies are navigating and strengthening human labour practices and organizational performance in healthcare. Research has shown that digitization and AI can help healthcare professionals through real-time insights and recommendations derived from extensive datasets. These modern technologies are advancing beyond being mere instruments in the health sector, now acting as partners to aid healthcare professionals in making better predictions and decisions by offering timely insights and suggestions derived from extensive datasets, as well as pinpoint potential health issues with greater accuracy and speed. It is estimated also that significantly can be supported the management of workplace well-being. Objective: This article delves into how AI and digitalization help healthcare professionals by boosting efficiency, meeting their professional and personal needs, and showcasing how they can enhance employee mental health and well-being. Results: It is crucial to recognize that issues arise from the intrinsic complexity and opaque nature of AI, the risk of job loss, and the disruption of the conventional interaction between physicians and patients. Nonetheless, AI in the healthcare facilities should not be seen as a danger to human employees. Instead, AI strive to support healthcare employees, enabling them to allocate more time to complex and crucial tasks. By automating tasks that are repetitive and mundane, these new technologies can lessen the burden on healthcare professionals, allowing them to dedicate more time to caring for patients and engaging in valuable interactions. Conclusions: The integration of AI and digitalization technologies into healthcare presents both opportunities and challenges for employee motivation and job performance. Although it can improve
effectiveness and lower stress levels, it is important to carefully address worries about employment stability and maintaining personal connections in healthcare. Organizations need to prioritize creating a workspace that encourages and assists employees in adjusting to new technological changes.
... When interacting with an AI-based system for a service, consumers tend to be skeptical about its ability to assist in decision-making and accurately infer their preferences (Gaczek et al., 2023;Longoni et al., 2019;Schmidt et al., 2020). Their resistance stems from concerns about the perceived loss of autonomy in their decisions (Husairi and Rossi, 2024) and the perceived opacity of AI-based systems (European Data Protection Supervisor, 2023;Grewal et al., 2021). This skepticism, especially among nonexpert consumers, limits the effectiveness of AIbased services (Hair and Sarstedt, 2021;Hasan et al., 2021;Osburg et al., 2022), particularly in high-credence service sectors where additional information is needed to evaluate service quality but is often unavailable (Girard and Dion, 2010;Mitra et al., 1999). ...
Purpose
This study explores how the format of explanations used in artificial intelligence (AI)-based services affects consumer behavior, specifically the effects of explanation detail (low vs high) and consumer control (automatic vs on demand) on trust and acceptance. The aim is to provide service providers with insights into how to optimize the format of explanations to enhance consumer evaluations of AI-based services.
Design/methodology/approach
Drawing on the literature on explainable AI (XAI) and information overload theory, a conceptual model is developed. To empirically test the conceptual model, two between-subjects experiments were conducted wherein the level of detail and level of control were manipulated, taking AI-based recommendations as a use case. The data were analyzed via partial least squares (PLS) regressions.
Findings
The results reveal significant positive correlations between level of detail and perceived understanding and between level of detail and perceived assurance. The level of control negatively moderates the relationship between the level of detail and perceived understanding. Further analyses revealed that the perceived competence and perceived integrity of AI systems positively and significantly influence the acceptance and purchase intentions of AI-based services.
Practical implications
This research offers service providers key insights into how tailored explanations and maintaining a balance between detail and control build consumer trust and enhance AI-based service outcomes.
Originality/value
This article elucidates the nuanced interplay between the level of detail and control over explanations for non-expert consumers in high-credence service sectors. The findings offer insights into the design of more consumer-centric explanations to increase the acceptance of AI-based services.
The growth of Artificial Intelligence (AI) has important implications for business in general and innovation in particular. Ideation is the start of the innovation process. The authors review three fields of AI in ideation: identification and analysis of new opportunities, idea generation, and idea screening and idea selection. The results of the review are as follows. First, whereas in the past researchers highlighted the importance of industry characteristics and market stability, the authors now emphasize the importance of firm culture in driving innovation. AI will mediate this relationship. Second, across all stages, AI will improve efficiency, speed, and cost of ideation. Third, in opportunity identification, considerable progress has occurred in analyzing text and image; research on video and audio is relatively scarce. Fourth, in idea generation, AI increases the average creativity of ideas; however, the effect of AI on the generation of top ideas is conflicting. Fifth, AI assists very well in idea screening, but does not do a good job yet in idea selection. Sixth and most importantly, research remains in the early stages and will rapidly improve in the future. Thus, AI has the potential to radically transform ideation.
The core concept of relationship-oriented sustainability marketing involves integrating ecological, social, and economic considerations into the design of a company's relationships with both external and internal stakeholders. Consequently, it seeks to generate sustainable value for both current and future stakeholders by balancing ecological, social, and economic concerns across stakeholder relationships at the individual, organizational, and societal levels. Over the past five decades, scholarly interest in the concept has evolved through a discernible progression, transitioning from traditional marketing to relationship marketing, and subsequently to sustainability marketing. Within the latter, various subfields have emerged, including ethical marketing and ecological (eco-) marketing. This chapter aims to explore the trends and future directions in relationship-oriented sustainability marketing, offering a guiding framework for both researchers and marketing practitioners. Various propositions are outlined that could be further narrowed down and tested by researchers who are interested in contributing scientific knowledge in this area.
Purpose
AI technology is now intensively used by banks to create value for bank customers. Therefore, this study endeavors to synthesize and track a decade of research emphasizing AI and value co-creation in the banking sector.
Design/methodology/approach
A bibliometric analysis of published research between 2013 and 2023 was conducted based on data obtained from Scopus, yielding a sample of 41 papers for further analysis. Performance analysis and science mapping are conducted as roots for the bibliometric analysis using VOSviewer software and the Biblioshiny package. The bibliometric analysis was incorporated with a literature review.
Findings
The contribution and the theoretical foundation of AI and value co-creation in the banking sector were outlined. In addition, the thematic structure, which is beneficial in uncovering the research gap, is analyzed. Moreover, the systematic literature review helped to clarify the clusters’ content emerging from the bibliometric analysis.
Research limitations/implications
By investigating the “who,” “what,” “where,” “when” and “how” of AI and value co-creation in the banking sector, the study contributes to theory and practice by advancing the understanding of the underlying topic.
Practical implications
The findings benefit marketers and policymakers to effectively use AI to facilitate value co-creation in the banking sector.
Originality/value
This paper is a pioneering bibliometric analysis and systematic literature review of AI and value co-creation in the banking sector and thus has the potential to provide valuable insights for scholars and decision-makers in banks.
Purpose
The legal sports betting market in the United States has rapidly grown into a multi-billion-dollar industry. As a result, a secondary industry for sports betting recommendations has emerged. Because sports betting is widely considered as skill-based gambling, consumers may turn to experts for recommendations. While many sports betting recommendations come from humans, technological advances have enabled recommendations from artificial intelligence (AI). Therefore, we investigate how an AI versus human recommendation source affects consumer perceptions of expertise and subsequent likelihood to follow the sports betting recommendation.
Design/methodology/approach
We conducted three lab experiments, with three distinct undergraduate samples. We manipulated the recommendation source as either AI or human. We examined the impact of recommendation sources on consumer perceptions of expertise and subsequent likelihood to follow the sports betting recommendation via ANOVA, ANCOVA and the PROCESS macro. The collection of human data was performed according to the ethics standards established by the Declaration of Helsinki 2013 and was approved by the local Institutional Review Board for Human Subjects at the authors’ respective institutions. All participants gave informed consent prior to completing any experiment.
Findings
Across all lab experiments, we found that an AI (vs human) recommendation source decreased consumer perceptions of expertise and subsequent likelihood to follow the sports betting recommendation.
Originality/value
As the legal sports betting market continues to grow, both firms and consumers are investing in sports betting recommendations—including recommendations from AI. However, we are not aware of any research that examines sports betting recommendations from AI. We found that an AI (vs human) recommendation source decreased consumer perceptions of expertise and subsequent likelihood to follow the sports betting recommendation.
Background
Despite extensive research into technology users’ privacy concerns, a critical gap remains in understanding why individuals adopt different standards for data protection across contexts. The rise of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR), and big data has created rapidly evolving and complex privacy landscapes. However, privacy is often treated as a static construct, failing to reflect the fluid, context-dependent nature of user concerns. This oversimplification has led to fragmented research, inconsistent findings, and limited capacity to address the nuanced challenges posed by these technologies. Understanding these dynamics is especially crucial in fields such as digital health and informatics, where sensitive data and user trust are central to adoption and ethical innovation.
Objective
This study synthesized existing research on privacy behaviors in emerging technologies, focusing on IoT, AI, AR, and big data. Its primary objectives were to identify the psychological antecedents, outcomes, and theoretical frameworks explaining privacy behavior, and to assess whether insights from traditional online privacy literature, such as e-commerce and social networking, apply to these advanced technologies. It also advocates a context-dependent approach to understanding privacy.
Methods
A systematic review of 179 studies synthesized psychological antecedents, outcomes, and theoretical frameworks related to privacy behaviors in emerging technologies. Following established guidelines and using leading research databases such as ScienceDirect (Elsevier), SAGE, and EBSCO, studies were screened for relevance to privacy behaviors, focus on emerging technologies, and empirical grounding. Methodological details were analyzed to assess the applicability of traditional privacy findings from e-commerce and social networking to today’s advanced technologies.
Results
The systematic review revealed key gaps in the privacy literature on emerging technologies, such as IoT, AI, AR, and big data. Contextual factors, such as data sensitivity, recipient transparency, and transmission principles, were often overlooked, despite their critical role in shaping privacy concerns and behaviors. The findings also showed that theories developed for traditional technologies often fall short in addressing the complexities of modern contexts. By synthesizing psychological antecedents, behavioral outcomes, and theoretical frameworks, this study underscores the need for a context-contingent approach to privacy research.
Conclusions
This study advances understanding of user privacy by emphasizing the critical role of context in data sharing, particularly amid ubiquitous and emerging health technologies. The findings challenge static views of privacy and highlight the need for tailored frameworks that reflect dynamic, context-dependent behaviors. Practical implications include guiding health care providers, policy makers, and technology developers toward context-sensitive strategies that build trust, enhance data protection, and support ethical digital health innovation.
Trial Registration
PROSPERO CRD420251037954; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251037954
Today's commercial sector widely recognises AI and generative AI as catalysts and game-changers. AI and generative AI are empowered to assist organisations in developing new products and can also completely transform commercial processes in an organised way without sacrificing standards and quality. While AI and generative AI are indeed beneficial and efficient, they do come with a few inherent problems. Furthermore, successfully implementing AI and generative AI is a costly endeavor. Therefore, authorities must ensure the cost-effective availability of these technologies, allowing small-scale firms to integrate them into their operations. In general, we are optimistic that this research will provide valuable insights to the whole business community on how to effectively manage their organisations.
Artificial intelligence (AI) powered marketing has been gaining the attention of global marketers and academicians. Contemporary marketing now views AI not merely as a cutting-edge technology, but also a valuable channel to create value. Extant research has extensively evaluated and qualitatively substantiated the significance of AI’s proliferation in marketing. However, research based on real-life practitioner data is limited in this domain. The present study addresses this research gap with a two-pronged approach: one, using grey-DEMATAL, it quantitatively evaluates the impact of AI on contemporary marketing; and two, it establishes industry validation to the driving factors of AI in marketing derived from the extant research. The scope of the study was limited to financial services. The results indicate that enhancing customer experience by personalization at scale, increasing marketing efficiency, and driving positive brand engagement were the most powerful causal factors driving the adoption of AI in marketing. As the global digital divide continues to expand between global south and north, the present study is relevant by highlighting, for researchers, practitioners and policymakers, the need to make digital and AI discourse an integral part of their research efforts.
A new form of human-machine collaborative capabilities has been called to complement traditional capabilities to ensure higher but more responsible performance in artificial intelligence (AI)-driven organizational environments. Dynamic Managerial Capabilities (DMC) allow organizations to use their resources to neutralize threats and harness opportunities in competitive environments. Using DMC as our theoretical framework, we conducted 20 semi-structured interviews to explore the dimensions of leaders’ capabilities in AI environments with two specific aims. First, to identify the leaders’ essential capabilities in the AI environment and second, to develop a novel AI-driven digital leadership capability framework to lead AI-driven organizations effectively. Our findings show that leaders who dynamically manage AI tools and processes are better equipped to succeed in organizations where AI is a key factor. Our findings have theoretical implications for DMC theory and leadership as well as practical implications for effectively managing ethical issues of emerging technologies. We conclude by outlining research opportunities on how AI-driven leader capabilities can make digital leadership more effective.
Purpose
The artificial intelligence-based recommender system (AIRS) provides personalization and convenience for enhancing customer experience but triggers customers’ resistance arising from privacy concerns. Few studies have investigated what privacy concerns arise from the AIRS in e-commerce as well as how these privacy concerns influence consumers’ resistance to the AIRS in e-commerce. This study aims to conceptualize privacy concerns and explore their influence on consumers’ resistance to the AIRS in e-commerce.
Design/methodology/approach
A mixed-methods approach is used. Study 1 identifies the context-specific privacy concerns related to the AIRS in e-commerce by interviewing 23 consumers. Study 2 examines the influence of context-specific privacy concerns on consumers’ resistance to the AIRS in e-commerce by surveying 557 consumers.
Findings
Study 1 reveals that privacy concerns related to the AIRS in e-commerce manifest in perceived surveillance, perceived identity theft and perceived unauthorized secondary use. Study 2 finds that three privacy concerns have three effects on resistance intention: direct, completely mediated by resistance attitude and partially mediated by resistance attitude. Additionally, prior privacy invasion experience strengthens the positive attitude–intention relationship.
Originality/value
This study contributes to the literature in three key aspects. Firstly, this study advances the knowledge about privacy concerns by identifying context-specific privacy concerns related to AIRSs in e-commerce. Secondly, this study extends the belief-attitude-intention framework to explain the linkage among the privacy concerns related to AIRSs in e-commerce, resistance attitude and resistance intention. Thirdly, this study adds new insight to the literature on user resistance by examining consumers’ resistance to e-commerce AIRSs from a privacy perspective.
The prospect of artificial intelligence (AI) matching and surpassing human intelligence continues to intrigue. On the foundations of the exceptional advances in AI technologies in the last decade, the potential for competitive advantages makes AI in general and Generative AI in particular one of the most promising technologies for marketing. However, while there are robust theoretical advances in the domain of AI for marketing, how AI impacts marketing entities is poorly understood. Further, how AI potentially makes marketing entities ineffective and inefficient is rarely addressed in research. Therefore, in this research, we begin with the articulation of a theory toolkit relevant to AI for marketing. Second, we discuss different types of AI and introduce a new perspective on approaching AI for marketing entities and purposes. Specifically, we conceptualize three new types of AI: enabling AI, engaging AI, and ersatzing AI (artificial, but inferior intelligence that make marketing entities less capable). Third, using our typology, we explicate the enormous potential of the three specific types of AI for marketing. Finally, toward actualizing the potential of AI for marketing, we conclude with a discussion of the contributions of our research and a research agenda.
Purpose
Digitalization in hospitality and tourism (DHT) is undergoing a profound revolution, yet its actual effect remains overlooked. This paper aims to delve into the actual effect of DHT and uncover the underlying mechanisms that trigger its subtle success.
Design/methodology/approach
Triangulation was used by leveraging complementary multisource data and mixed methods to explore DHT’s challenges. First, regression and statistical analysis revealed intricate outcome of DHT at the regional level. Then, DistilBERT and nonnegative matrix factorization were used to cluster the tourists’ complaints about DHT. Finally, based on a modified diffusion innovation system and stakeholder theory, this study proposed the Integrated Framework for Digitalization Mismatch in Hospitality and Tourism (IFDMHT).
Findings
The quantitative techniques identified the subtle contributions of digitalization on demand. The qualitative technique explored the strategy–practice and demand–supply mismatches as the core of the failure.
Originality/value
This study synthesizes macroregional and micropsychological perspectives to examine the actual effect of DHT. An IFDMHT model is established to investigate the underlying factors influencing the subtle successes of DHT. The findings provide targeted recommendations to assist stakeholders in developing sustainable DHT.
Artificial Intelligence (AI) revolutionizes education by supporting personalized learning, critical thinking, and problem solving through tools like intelligent tutoring systems and adaptive platforms. Visionary educators recognize AI’s potential in the classroom. According to IBM, AI will not completely replace humans, but those who use AI will replace those who do not. This statement emphasizes the importance of integrating AI in K-12 education. The purpose of this study is to provide an overview of AI tools and platforms for K-12 education, with the hope of serving as a practical reference for teachers to use these tools. This review discusses the definitions and types of AI for education. It further identifies and reviews currently available and popular AI tools and their applications in K-12 education. It offers quick references for teachers on how they can harness various AI tools for different teaching and learning purposes, along with addressing ethical concerns such as data privacy and algorithmic biases. This article classifies AI tools on the basis of instructional, administrative, and analytical usage to address diverse needs, enhance teaching and learning, provide personalized instruction, and predict student outcomes. This review also provides information for teachers to choose appropriate AI tools for specific purposes. The use of AI tools and associated concerns in the K-12 classrooms are also discussed, encouraging teachers to create more dynamic, inclusive, and effective learning environments while preparing students for the future. This article also offers future directions for researchers and product developers regarding the use of AI in K-12 education.
This chapter presents a bibliometric study of the country-of-origin field in academic articles published in research journals included in the Scopus academic database during the period 2014–2023. Consumers evaluate product attributes such as quality and performance through proxies, and country of origin COO becomes salient with products from foreign origin, and this effect may be shifting due to regionalization of economic relationships. The social, intellectual, and conceptual structures of academic literature were analyzed using bibliometric analysis, performance analysis, and scientific mapping. The trend of deglobalization leading to the emergence of regional economic blocks suggests that the COO may gain new relevance in light of political polarization, active regional conflicts, and product and brand boycotts. The results suggest that the literature does not yet include these phenomena; hence, a research agenda is proposed to push forward research on COO.
Artificial intelligence (AI) will substantially impact retailing. Building on past research and from interviews with senior managers, we examine how senior retailing managers should think about adopting AI, involving factors such as the extent to which an AI application is customer-facing, the amount of value creation, whether the AI application is online, and extent of ethics concerns. In addition, we highlight that the near-term impact of AI on retailing may not be as pronounced as the popular press might suggest, and also that AI is likely to be more effective if it focuses on augmenting (rather than replacing) managers’ judgments. Finally, while press coverage typically involves customer-facing AI applications, we highlight that a lot of value can be obtained by adopting non-customer-facing applications. Overall, we remain very optimistic as regards the impact of AI on retailing. Finally, we lay out a research agenda and also outline implications for practice.
Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: (1) data capture, (2) classification, (3) delegation, and (4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations’ investments into AI and to lay out an agenda for future research.
Advances in robotics, automation, and artificial intelligence increasingly enable firms to replace human labor with technology, thereby fundamentally transforming how goods and services are produced. From both managerial and societal points of view, it is therefore important to understand demand‐side incentives for firms to employ human labor. We begin to address this question by examining for which products and services consumers are more likely to favor human (vs. robotic) labor. In six studies, we demonstrate that consumers prefer human (vs. robotic) labor more for products with higher (vs. lower) symbolic value (e.g., when expressing something about one's beliefs and personality is of greater importance). We theorize that this is because consumers have stronger uniqueness motives in more (vs. less) symbolic consumption contexts (and associate human labor more strongly with product uniqueness). In line with this account, we demonstrate that individual differences in need for uniqueness moderate the interaction between production mode and symbolic motives and that a measure of uniqueness motives mediates the effect of consumption context on preferences for human (vs. robotic) production.
The B2B sales process is undergoing substantial transformations fueled by advances in information and communications technology, specifically in artificial intelligence (AI). The premise of AI is to turn vast amounts of data into information for superior knowledge creation and knowledge management in B2B sales. In doing so, AI can significantly alter the traditional human-centric sales process. In this article, we describe how AI affects the B2B sales funnel. For each stage of the funnel, we describe key sales tasks, explain the specific contributions AI can bring, and clarify the role humans play. We also outline managerial considerations to maximize the contributions from AI and people in the context of B2B sales.
In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.
Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity toward AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A-3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for their unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.
Artificial intelligence (AI)—defined as a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation—is a topic in nearly every boardroom and at many dinner tables. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. In this article, we analyze how AI is different from related concepts, such as the Internet of Things and big data, and suggest that AI is not one monolithic term but instead needs to be seen in a more nuanced way. This can either be achieved by looking at AI through the lens of evolutionary stages (artificial narrow intelligence, artificial general intelligence, and artificial super intelligence) or by focusing on different types of AI systems (analytical AI, human-inspired AI, and humanized AI). Based on this classification, we show the potential and risk of AI using a series of case studies regarding universities, corporations, and governments. Finally, we present a framework that helps organizations think about the internal and external implications of AI, which we label the Three C Model of Confidence, Change, and Control.
The emergence of online and mobile technologies has necessitated the need to re-examine the viability of relationship marketing in nurturing business-to-business service relationships. By drawing upon the resource-based view (RBV), social exchange (SE) theory and socio-technical systems (STS) theory, this research explores the differences in the process by which local, national and foreign branded banks are able to integrate their online platforms into their relational efforts. Data from a sample of 336 small and medium-sized business customers in the New Zealand banking industry shows that both offline and online service quality affect satisfaction with their e-banking services, which in turn affect the trust and commitment towards the bank and loyalty toward e-banking. In addition, the effect of trust and commitment on loyalty towards e-banking is significantly stronger for the local and national branded banks compared to foreign branded banks. Our findings extend current research on B2B relationship marketing and offer useful
managerial insights for professional B2B services providers.
Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for “lower” (easier for AI) intelligence tasks first. AI first replaces some of a service job’s tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job’s tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.
Purpose
Previous studies have argued that trust and commitment can create value in cooperative relationships. However, this study observed that, in practice, trust and commitment alone may not ensure value creation in asymmetric relationships. Accordingly, this study aims to investigate the mediating role of specific assets in the effects of trust and commitment on value creation in asymmetric buyer–seller relationships.
Design/methodology/approach
Contract manufacturers (CMs) in Asia were sampled to validate the argument proposed by this study. Most Taiwanese CMs are partnered with international brands (original equipment manufacturers [OEMs]) that have stronger bargaining power. This cooperative relationship is characteristically asymmetric. A questionnaire method was applied, and structural equation modeling was performed to verify the proposed hypotheses.
Findings
Specific asset investment (SAI) was a crucial mediator that explained the effects of trust and commitment on the relationship value of an asymmetric cooperative relationship. Past studies have claimed that power asymmetry results in an unequal distribution of benefits. Nevertheless, regarding the relationship between CMs and OEMs, the study revealed that relationship value could still be increased once the congruent goals have been achieved by both parties. This finding contradicts past theoretical predictions.
Practical implications
Characteristically asymmetric CMs–OEMs (seller–buyer) relationships cannot be maintained merely through trust and commitment, particularly in the context of power and resource imbalances in which the stronger party often possesses a wider selection of prospective partners. The results of this study suggested that the CM should unilaterally invest in specific assets conducive to a cooperative relationship as an expression of faith in the relationship with the stronger firm, thereby creating opportunities for value cocreation.
Originality/value
The analysis of the relevance of relationship quality in the context of asymmetric cooperative relationships confirmed the mediating influences of SAI on ensuring value creation and the maintenance of the relationships. Relationship value could still be created despite the highly asymmetry power relationship. The CMs’ SAI is the key mechanism for this achievement.
Research on buyer-supplier relationships (BSRs) has often focused on only one side of the relationship and thus has tended to overlook asymmetries between buyer and supplier. Yet, a buyer (supplier) may often deal with a bigger supplier (buyer) or one that has higher levels of trust, respect and reciprocity. Therefore, we examined how two types of asymmetries, size and relational capital, affect opportunism and performance. We used dyadic data from 106 buyers and their matched suppliers gathered from a survey and an archival database. The results demonstrate first, that the degree and direction of both asymmetries affect the BSR; second, that these two asymmetries have profound effects (both positive and negative) on opportunism; and third, that a buyer perceives lower benefits from the relationship in the presence of size asymmetry or when the supplier perceives higher relational capital. Thus, our research represents a significant step forward in understanding BSRs and asymmetries by (i) bringing attention to two key asymmetries inherent in BSRs and (ii) showing that these asymmetries are not unidirectional in their influence on opportunism and performance.
There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bring- ing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. We thus designed a brief question- naire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high- level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity.
Category management (CM) is challenging for retailers that sell thousands of products across hundreds of categories and often lack the resources and capabilities to manage all of them intensively. Some retailers respond by picking one supplier to be a "category captain" that manages the category-including rivals' brands-on their behalf. Others worry that influential captains will be opportunistic and that the benefits of intensive CM are simply not worth the costs. However, there is little conceptual development or empirical evidence concerning CM best practices. The authors develop a comprehensive model of retail CM based on a synthesis of field interviews and relevant literature, especially work on governance value analysis theory. Their test of the model using category and financial growth data from U.S. supermarket chains shows that more intensive CM improves results. Furthermore, use of a category captain increases CM effort and results, without increasing opportunism or problems with other suppliers. The authors also find that retailers with more resources are less likely to rely on help from a category captain; yet the level of retailer resources is not related to CM intensity. Thus, "go-it-alone" retailers do not deploy their own resources on CM and miss out on the corresponding performance improvements.
Much of the recent literature on interfirm relationships has focused on strategies for controlling opportunism. Somewhat surprisingly, little attention has been paid in this literature to the opportunism construct itself. Specifically, prior research has failed to recognize the different types of behavior that are hidden behind the general opportunism label. As a consequence, the knowledge of strategies for managing opportunism remains incomplete. The authors review the original and emergent conceptualizations of opportunism and illustrate them using actual industry cases. The authors also develop a conceptual framework of governance strategies that can be used to manage different forms of opportunism.
Relationship marketing (RM) has emerged as one of the dominant mantras in business strategy circles, though RM investigations often yield mixed results. To help managers and researchers improve the effectiveness of their efforts, the authors synthesize RM empirical research in a meta-analytic framework. Although the fundamental premise that RM positively affects performance is well supported, many of the authors' findings have significant implications for research and practice. Relationship investment has a large, direct effect on seller objective performance, which implies that additional meditated pathways may explain the impact of RM on performance. Objective performance is influenced most by relationship quality (a composite measure of relationship strength) and least by commitment. The results also suggest that RM is more effective when relationships are more critical to customers (e.g., service offerings, channel exchanges, business markets) and when relationships are built with an individual person rather than a selling firm (which partially explains the mixed effects between RM and performance reported in previous studies).
The inclusion of social media as a communication channel in a vendor's B2B digital marketing strategy is growing in importance. Understanding the effect of such practices upon customer relationships is crucial for firms as they increasingly engage in this way. This paper presents and tests a model that explores the effect of vendor social media communication practices upon trust and loyalty in B2B customer relationships. A study using quantitative data from 196 business customers of a United States life sciences firm is reported. The model indicates that trust and loyalty are influenced by a) the social media shared beliefs between the vendor and the customer; b) the nature of the vendor's social media communication with the customer; and c) the extent to which the vendor's social media communication practices enable effective customer-to-customer communication. Trust is found to have a mediating role between these indicators and loyalty. Managerial implications are discussed.
Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.
This study investigates the driving forces of a firm's assimilation of big data analytical intelligence (BDAI) and how the assimilation of BDAI improve customer relationship management (CRM) performance. Drawing on the resource-based view, this study argues that a firm's data-driven culture and the competitive pressure it faces in the industry motivate a firm's assimilation of BDAI. As a firm resource, BDAI enables an organization to develop superior mass-customization capability, which in turn positively influences its CRM performance. In addition, this study proposes that a firm's marketing capability can moderate the impact of BDAI assimilation on its mass-customization capability. Using survey data collected from 147 business-to-business companies, this study finds support for most of the hypotheses. The findings of this study uncover compelling insights about the dynamics involved in the process of using BDAI to improve CRM performance.
Artificial Intelligence (AI) could be an important foundation of competitive advantage in the market for firms. As such, firms use AI to achieve deep market engagement when the firm's data are employed to make informed decisions. This study examines the role of computer-mediated AI agents in detecting crises related to events in a firm. A crisis threatens organizational performance; therefore, a data-driven strategy will result in an efficient and timely reflection, which increases the success of crisis management. The study extends the situational crisis communication theory (SCCT) and Attribution theory frameworks built on big data and machine learning capabilities for early detection of crises in the market. This research proposes a structural model composed of a statistical and sentimental big data analytics approach. The findings of our empirical research suggest that knowledge extracted from day-today data communications such as email communications of a firm can lead to the sensing of critical events related to business activities. To test our model, we use a publicly available dataset containing 517,401 items belonging to 150 users, mostly senior managers of Enron during 1999 through the 2001 crisis. The findings suggest that the model is plausible in the early detection of Enron's critical events, which can support decision making in the market.
B2B buyers are exhibiting an increased preference to transact digitally with vendors. A topic which has yet to receive sufficient academic attention in this modern selling environment is salesperson communication effectiveness. Accordingly, this article: 1) defines digital sales interactions (DSIs) as technology-enabled, face-to-face buyer-seller exchanges; 2) proposes a typology of DSIs to set the stage to examine salesperson communication effectiveness; 3) introduces a framework that reifies the sender's auditory and visual cues that can influence receivers' thoughts (i.e., cognition, affect, intention) and activity (i.e., purchase behavior, advocacy); 4) suggests theoretical lenses that can illuminate various aspects of the salesperson's communication barrage; 5) advances how machine learning can be applied to understand what constitutes effective communication in a digital interaction by asking: to what extent does what a salesperson says (auditory cues) and how s/he says it (visual cues) impact her/his effectiveness in a DSI?; and 6) concludes by noting promising future research directions for B2B marketing researchers.
Relationship marketing—establishing, developing, and maintaining successful relational exchanges—constitutes a major shift in marketing theory and practice. After conceptualizing relationship marketing and discussing its ten forms, the authors (1) theorize that successful relationship marketing requires relationship commitment and trust, (2) model relationship commitment and trust as key mediating variables, (3) test this key mediating variable model using data from automobile tire retailers, and (4) compare their model with a rival that does not allow relationship commitment and trust to function as mediating variables. Given the favorable test results for the key mediating variable model, suggestions for further explicating and testing it are offered.
This paper presents a unifying framework for the analysis of distribution channels which encompasses both economic and sociopolitical determinants of channel member behavior and provides a suitable departure point for comparative work. The framework integrates present approaches to the study of marketing channels and provides an essential, but heretofore missing, basis for comprehensive empirical research in the area.
We develop a flexible methodology to protect marketing data in the context of a business ecosystem in which data providers seek to meet the information needs of data users, but wish to deter invalid use of the data by potential intruders. In this context we propose a Bayesian probability model that produces protected synthetic data. A key feature of our proposed method is that the data provider can balance the trade-off between information loss resulting from data protection and risk of disclosure to intruders. We apply our methodology to the problem facing a vendor of retail point-of-sale data whose customers use the data to estimate price elasticities and promotion effects. At the same time, the data provider wishes to protect the identities of sample stores from possible intrusion. We define metrics to measure the average and maximum loss of protection implied by a data protection method. We show that, by enabling the data provider to choose the degree of protection to infuse into the synthetic data, our method performs well relative to seven benchmark data protection methods, including the extant approach of aggregating data across stores.
Data are available at https://doi.org/10.1287/mksc.2017.1064 .
Relationship marketing (RM) has emerged as one of the dominant mantras in business strategy circles, though RM investigations often yield mixed results. To help managers and researchers improve the effectiveness of their efforts, the authors synthesize RM empirical research in a meta-analytic framework. Although the fundamental premise that RM positively affects performance is well supported, many of the authors’ findings have significant implications for research and practice. Relationship investment has a large, direct effect on seller objective performance, which implies that additional meditated pathways may explain the impact of RM on performance. Objective performance is influenced most by relationship quality (a composite measure of relationship strength) and least by commitment. The results also suggest that RM is more effective when relationships are more critical to customers (e.g., service offerings, channel exchanges, business markets) and when relationships are built with an individual person rather than a selling firm (which partially explains the mixed effects between RM and performance reported in previous studies).
Research on buyer-supplier relationships (BSRs) has often focused on only one side of the relationship and, thus, has tended to overlook asymmetries. Yet, a buyer (supplier) may often deal with a bigger supplier (buyer) or one that has higher levels of trust, respect, and reciprocity. Therefore, we examined how two types of asymmetries-size and relational capital-affect perceived opportunism and performance. We used dyadic data from 106 buyers and their matched suppliers gathered from a survey and an archival database. The results demonstrate that the degree and direction of both asymmetries affect the BSR. Our results also reveal that an imbalance of relational capital in a firm's favor may have the opposite effect from that intended. In other words, the firm's counterpart perceives more, rather than less, firm opportunism. The results also suggest that a buyer observes lower benefits in the presence of size asymmetry, whereas the supplier's perception of benefits is unaffected. Thus, our research represents a significant step forward in understanding BSRs and asymmetries by (i) bringing attention to two key asymmetries inherent in BSRs and (ii) showing that these asymmetries are not unidirectional in their influence on perceived opportunism and performance.
This study investigates the use of private rules in exercising power in asymmetric business relationships. In asymmetric business relationships, the stronger party is likely to be able to dominate and exercise power over the conclusion of contracts and, thereby determine the processes and outcomes of the relationship. The study demonstrates how companies exercise their power in asymmetric relationships through private rules. Private rules are typically expressed in the General Terms and Conditions of Trade (GTCT) of the more powerful actor in a business relationship and are continually adapted to changing business and market requirements. Drawing on an empirical investigation in the German grocery retail business conducted in the years between 2011 and 2013, the present study demonstrates that power is exercised by the stronger parties through intervention–enforcement–sanctioning practices that are codified in private rules. Private rules frame, standardize and legitimize the terms and conditions under which exchanges among counterparts may take place thus institutionalizing the inherent power asymmetry.
Retailers gather data about customers’ online behavior to develop personalized service offers. Greater personalization typically increases service relevance and customer adoption, but paradoxically, it also may increase customers’ sense of vulnerability and lower adoption rates. To demonstrate this contradiction, an exploratory field study on Facebook and secondary data about a personalized advertising campaign indicate sharp drops in click-through rates when customers realize their personal information has been collected without their consent. To investigate the personalization paradox, this study uses three experiments that confirm a firm's strategy for collecting information from social media websites is a crucial determinant of how customers react to online personalized advertising. When firms engage in overt information collection, participants exhibit greater click-through intentions in response to more personalized advertisements, in contrast with their reactions when firms collect information covertly. This effect reflects the feelings of vulnerability that consumers experience when firms undertake covert information collection strategies. Trust-building marketing strategies that transfer trust from another website or signal trust with informational cues can offset this negative effect. These studies help unravel the personalization paradox by explicating the role of information collection and its impact on vulnerability and click-through rates.
Purpose
The purpose of this paper is to investige whether online environment cues (web site quality and web site brand) affect customer purchase intention towards an online retailer and whether this impact is mediated by customer trust and perceived risk. The study also aimed to assess the degree of reciprocity between consumers' trust and perceived risk in the context of an online shopping environment.
Design/methodology/approach
The study proposed a research framework for testing the relationships among the constructs based on the stimulus‐organism‐response framework. In addition, this study developed a non‐recursive model. After the validation of measurement scales, empirical analyses were performed using structural equation modelling.
Findings
The findings confirm that web site quality and web site brand affect consumers' trust and perceived risk, and in turn, consumer purchase intention. Notably, this study finds that the web site brand is a more important cue than web site quality in influencing customers' purchase intention. Furthermore, the study reveals that the relationship between trust and perceived risk is reciprocal.
Research limitations/implications
This study adopted four dimensions – technical adequacy, content quality, specific content and appearance – to measure web site quality. However, there are still many competing concepts regarding the measurement of web site quality. Further studies using other dimensional measures may be needed to verify the research model.
Practical implications
Online retailers should focus their marketing strategies more on establishing the brand of the web site rather than improving the functionality of the web site.
Originality/value
This study proposed a non‐recursive model for empirically analysing the link between web site quality, web site brand, trust, perceived risk and purchase intention towards the online retailer.
CCPA for Marketers: What you really need to know
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Virginia passes the Consumer Data Protection Act
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10 Ways AI is Improving Manufacturing in 2020
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Transforming pricing for oil & gas wholesales: Personalized pricing for petroleum products
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Facebook v Apple: the looming showdown over data tracking and privacy
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How IoT is changing B2B Business models
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