As a business innovation in the e-commerce marketplace, the use of live streams to boost sales has become an important strategy for e-tailers on major e-commerce platforms globally. However, little theoretical research has been conducted to understand the role of streamers and products in live streaming commerce. Thus, in this study, to examine consumers' perceived diagnosticity and purchase intention, we adopt a 2 (streamer type) × 2 (product type) × 2 (brand awareness) experimental design and conduct a field experiment at a university in southern China, drawing on stimulus-organism-response (SOR) theory. Our results indicate that when a product is recommended by an influential streamer during an e-commerce live stream or has high brand awareness, consumers perceive a high level of diagnosticity, which improves their purchase intention. However, we find no significant effect of product type on the perceived diagnosticity of viewers watching e-commerce live streams. We also discuss the implications of our findings for both theory and practice.
Indian tax laws contain certain provisions, which are intended to act as an incentive for achieving certain desirable socioeconomic objectives. These provisions are contained in Chapter VIA of Income Tax Act and are in the form of deductions (80C to 80U) from the Gross Total Income. By reducing the chargeable income, these provisions reduce the tax liability, increase the post-tax income and thus induce the tax payers to act in the desired manner. The art of arranging tax matters in such a way that will reduce income tax liability by taking benefits from deductions is known as tax planning/saving. By employing effective tax planning strategies, taxpayer can have more money to save, invest and to spend. The aim of this study is intended to give a broad idea of such deductions and to explore the dimensions of these deductions in respect of saving and investment, health, education, housing and charity.
To assist the tourist in planning a more desirable trip according to his/her preferences, a variety of elements including accommodation, sightseeing locations, cost budget, and time constraints should be considered. To provide such a through planning, the Orienteering Problem with Hotel Selection (OPHS) has been recently introduced in the literature. However the current OPHS variants do not use hotels’ aspects in their modelling thereby are not able to provide a comprehensive planning. In this work, we address this gap by introducing a bi-objective OPHS which maximises the utility of the sightseeing, as well as the total weighted scores of hotel selection and provides multi day tourist trip plans. Moreover, as cities become smarter, colossal data can be used to facilitate tourist trip planning according to the preferences of tourists. In this research, we leverage the power of relevant external data thereby providing effective tourist plans. In particular, a decision-making framework is proposed for the multi-day tourist planning with hotel selection. This Integrated Text-mining Optimisation Framework consists of three modules. First, the accommodations’ scoring is measured by Aspect-Based Sentiment Analysis on existing reviews, looking at fifteen quality aspects of hotels. Second, four different tourist segments are considered, and the priority for each of the hotel’s aspects in each segment is calculated using the Best-Worst Method. The third module is solving the bi-objective OPHS to provide the desired plans for the tourist. To evaluate the applicability of the method, a real-world data set is analysed.
Online health communities (OHCs) are able to facilitate social support exchange among people with similar health concerns, and the relationship between self-disclosure and social support exchange has been widely discussed. Build upon the previous studies, we extend the seminal theory of reasoned action (TRA) in this research, through the adoption of an salient but understudied construct, user role, which captures user inclination toward providing or seeking social support, and further explore how it impacts OHC users’ self-disclosure intention. We adopt a mixed methods approach including manual coding, machine learning algorithms, and econometric analyses, and investigate over 4 million posts over 17 years from a well-developed breast cancer OHC to uncover both direct and indirect effects of user role on OHC users’ intention of future self-disclosure intention. The results reveal both the main and moderating effects of user roles regarding users’ self-disclosure intention. First, user with a more clear intention of social support seeking or providing are more likely to disclose personal information. Second, the relative tendency of support provision magnifies the positive effects of attitudinal and normative factors toward future disclosure. We also discuss the findings and implications of the extended TRA framework to generate some actionable insights of OHC design.
User-centric design within organizations is crucial for developing information technology that offers optimal usability and user experience. Personas are a central user-centered design technique that puts people before technology and helps decision makers understand the needs and wants of the end-user segments of their products, systems, and services. However, it is not clear how ready organizations are to adopt persona thinking. To address these concerns, we develop and validate the Persona Readiness Scale (PRS), a survey instrument to measure organizational readiness for personas. After a 12-person qualitative pilot study, the PRS was administered to 372 professionals across different industries to examine its reliability and validity, including 125 for exploratory factor analysis and 247 for confirmatory factor analysis. The confirmatory factor analysis indicated a good fit with five dimensions: Culture readiness, Knowledge readiness, Data and systems readiness, Capability readiness, and Goal readiness. Higher persona readiness is positively associated with the respondents' evaluations of successful persona projects. Organizations can apply the resulting 18-item scale to identify areas of improvement before initiating costly persona projects towards the overarching goal of user-centric product development. Located at the cross-section of information systems and human-computer interaction, our research provides a valuable instrument for organizations wanting to leverage personas towards more user-centric and empathetic decision making about users.
The online version contains supplementary material available at 10.1007/s10799-022-00373-9.
This study extends the heterogeneous effectiveness of market signals by examining when textual sentiments have the most influence on purchasing decisions. Specifically, we argue that reputation and status, two distinct theoretical constructs, which are difficult to disentangle in practice, may influence the effectiveness of textual sentiments on customers’ decision making process in opposite directions. Reputation refers to the quality trajectory for a product whereas status sets a societal expectation from a product based on the social standing of that product among its peers. In this study, we examine reputation and status as contingencies that affect how electronic word of mouth (e-WoM) is perceived by customers in the context of review platform. To demonstrate the impact of textual sentiments and the moderation effects of reputation and status, we used an online platform to crawl review and reservation data at the same time of everyday over a period of 100 days on 310 hotels located in New York City. We found that customers are more sensitive to the sentiment of textual reviews on hotels of high status but less receptive when reviews are on hotels of high reputation. Our robustness tests and two identification strategies are all consistent with these findings. This research offers a strategic guideline to businesses and platforms in terms of how much they should rely on e-WoM, contingent upon their reputation and status.
Agile development is known for efficient software development practices that enable teams to quickly develop software to cope with changing requirements. Although there is evidence that agile practices are helpful in such environments, the literature does not inform us as to whether agile practices can also be beneficial in hyper-agile environments. Such environments are characterized by an extremely fast pace of change with fluid requirements. COVID-19 vaccine distribution is one such problem that governments have had to deal with. To solve this problem, governments need to come up with robust responses by formulating teams that have the capability to provide software solutions enabling information visibility into the vaccine distribution process. Such emergent teams need to quickly understand the distribution process, oftentimes define the process itself because it might be non-existent, and build software systems to solve the problem in a matter of days. Not much is known about how systems can be developed at such a fast pace. We adopt a clinical research methodology and employ agile software development practices to develop such a mission-critical system. In the process of building the system, we learn important lessons that can be used to adapt and extend agile methodologies to be used in hyper-agile development environments. We offer these lessons as important first steps to understanding the best practices needed to develop software systems that have the capability to provide visibility into the unprecedented health challenge of distribution of life-saving COVID-19 vaccine.
Distributed agile software development (DASD) has gained much popularity over the past years. It relates to Agile Software Development (ASD) being executed in a distributed environment due to factors such as low development budget, emerging software application markets and the need for more expertise. DASD faces a number of challenges with respect to coordination and communication issues. Task allocation in such an environment thus becomes a challenging task. Adopting proper task allocation strategy is crucial to overcome challenges and issues in DASD. Various studies highlight the challenges being faced by DASD and have proposed solutions in the form of framework or models. Knowledge models in the form of ontologies can help to solve certain issues and challenges by providing a proper representation of data that is shareable among distributed teams. Several ontologies with respect to task allocation exist. However, ontologies incorporating factors and dependencies influencing task allocation process in DASD are limited. An ontology representing the knowledge related to task allocation and coordination is important for proper decision making in organizations. Based on an in-depth literature review and a survey conducted among professionals in industry, this paper proposes an ontology, OntoDASD, that incorporates relevant factors and dependencies to be considered in task allocation and coordination process in DASD environment. The ontology facilitates team coordination through effective communication and task allocation by defining the concepts to share knowledge and information in an appropriate way. OntoDASD has been properly evaluated and validated by professionals in the field.
Stakeholder satisfaction is a significant aspect of component-based product development. Satisfaction level of stakeholder varies due to diverse reviews and perspective about components functionalities. The reviews and perspective create ambiguities and misunderstanding during management of components requirement from specification to linking requirements that lead to product failures. The improper components management increases efforts and errors when component’s stakeholders and development team is located in a globally distributed environment. The major issues of distributed component-based systems, are control, communication, coordination, and semantical analysis of different reviews and perspectives. As requirements of components is elicited and developed at different locations which created ambiguities and irrelevancy during components integration. Therefore, in this study, we proposed a framework to improve the management process of components requirement in a distributed environment. To reduce ambiguities and incompleteness among requirements, aspect based sentiment analysis has been utilized for each stakeholders’ reviews and perceptive individually. On the other hand, to reduce involvement of stakeholder and efforts in components prioritization and linking processes, we adapted cased based reasoning method and decision tree-based classification of requirements, respectively. The performance of the proposed framework has been evaluated through an experimental approach in order to compare it with current practices i.e. Random selection and expert based. The findings described that the accuracy of component management in global development increases with proposed framework. Further, results show that there is an increase in product quality with decrease in irrelevancy and redundancy in stakeholders’ aspects and priority.
This research aims to (1) identify the critical risk factors that influence the governance of enterprise internal control in a big data environment, (2) depict the intertwined and complicated relationships among risk factors, and (3) yield an attainable target for performance improvement over both the short term and long term. To address these challenging issues, we propose an innovative hybrid decision architecture that combines artificial intelligence-based rule generation techniques and a multiple attribute decision making approach, called herein multiple rule-base decision making. Examining real cases, our study shows that the control environment and information technology (IT) control construction are the top dimension and criterion, respectively. This finding can be taken as a reference for managing and controlling risk factors under a big data environment. In an upcoming improvement/advancement on internal control/information technology (IT) governance, the related factors can also be viewed as essential requirements for enterprises when conducting effective internal control and audit inspection, which can help with more audit success and less lawsuit problems.
Knowledge and understanding about system design are very important for the development and maintenance of any software system due to certain deadlines and frequent changes in requirements and environment. However, it is a very difficult task to analyse design automatically. Design patterns give standard solutions to common design problems. It is very helpful to find existence of such patterns in the source code. It will reduce effort and time required in understanding and thus in the maintenance activity. In this paper we propose a tool DPDT for detecting design patterns from system software. We use graph matching process to find exact instances of design patterns mapped to system software. In graph matching structural aspects are considered. After that static facts of software systems and design patterns are used to reduce the number of false positives. We evaluate our result on two well-known open source software: JHotDraw and JUnit and compared the result of DPDT with existing tools (Sempatrec, DPF, SSA, DeMIMA, and Depatos) of design patterns detection. It is found that for proxy design patterns our tool out performs the all other tools. Further, for few design patterns it is giving moderate results while other tools did not consider those design patterns.
Agile approaches being practised by multiple teams operating remotely are widely adopted for large software development efforts these days. An agile setting is typically characterized by flexibility, to accommodate changing customer demands for continuous delivery of business value. A distributed setting brings about multiple demands for stability, in terms of a push for clear specification of requirements and design, and a big picture product definition. Therefore, implementing agile distributed development (ADD) projects results in an inherent conflict that must be reconciled. This article attempts to provide nuanced clarity on the notion of conflict between flexibility and stability and its management across variants of an ADD setup. Through multiple case studies, our findings suggest that the specific mode of agile project engagement and distributed team configuration drives the response to flexibility and stability respectively. Leveraging ambidexterity as a theoretical lens, this study contributes to the literature by providing insights beyond the earlier conceptualization of flexibility-stability conflict for the ADD setting. It considers contextual elements to understand the dynamics of conflicting forces. An empirical contribution of this research is the managerial framework that should assist practice in future implementations.
Digital nudging is attracting increasing attention to online decision-making processes and digital choice environments. Thus, understanding how digital nudging affects the acceptance of social media is essential for successful market segmentation. Using a modified technology acceptance model and 270 participants, this paper examines how digital nudging as a mediator influences the acceptance of social media among Chinese customers. By means of a structural equation model based on the partial least squares technique, this study aims to develop an intriguing model based on the TAM. Perceived usefulness and perceived ease of use influence the acceptance of social media less, as previously expected. All objective factors, i.e., social benefits, trust and ubiquitous connectivity, had a positive effect when nudging was used as a mediator. The models and the accompanying commentary will add to the debate over the scope of factors that affect the adoption of social marketing in terms of digital nudging. The results of the survey in this study should be interpreted as speculative and should not be relied upon as an accurate depiction of behavior in the surveyed communities.
This study investigates the relationship between project employee mindfulness and project success using innovative work behaviour as a mediator and the project manager’s inclusive leadership style as a moderator. Project Manager with high inclusive behaviour will strengthen the relationship of employee’s mindfulness and innovative work behaviour. The data were collected in three-time intervals from a total of 347 information technology project employees. The study findings validated the proposed model wherein employee personality traits, such as mindfulness, have a key impact on the initiation of project employees’ innovative work behaviour. Information Technology projects require innovation due to rapid technical improvements. The study confirms that innovative work behaviour adds to the project’s success. Furthermore, inclusive leadership helps mindful employees become innovative. Thus, the leadership roles should also be emphasised in IT projects.
IT users are increasingly experienced at adapting technologies to their needs; resulting in the widespread use of workarounds and shadow IT. To ascertain the impact of job characteristics on this behavior, a survey was conducted among 415 IT users. The collected data underwent Reliability Analysis and Exploratory Factor Analysis in SPSS software. Subsequently, Confirmatory Factor Analysis and Structural Equation Modeling were conducted with the SmartPLS software. The main results indicate that autonomy is strongly related to workaround behavior and shadow IT usage. Workaround behavior and shadow IT use have also been proven to be strongly related. However, the level of skill variety and task identity do not seem to significantly affect workaround behavior and shadow IT usage. Finally, this study’s findings demonstrate that both workaround behavior and shadow IT use are positively related to individual performance. Organizations are therefore encouraged to increase job autonomy in order to achieve enhanced individual performance by presenting workers with opportunities to adapt technologies in the form of workarounds and shadow IT. The use of such alternative solutions provides for faster and more dynamic communication and thus boosts collaboration among co-workers, external partners, and clients.
In this study, we build an economic model to explore the user-generated content (UGC) subsidy issue in the context of two-sided UGC platforms. Most UGC platforms subsidize content providers to encourage them to provide more content with higher quality. However, are these subsidies effective? Which type of subsidy is more effective? Here, we examine and compare the effectiveness of different types of subsides for UGC platforms. First, although the underlying reasons are different, both quantity and attention subsidies can induce users to provide more content with higher quality. Second, given the same level of small subsidy, the attention subsidy is more effective in encouraging better UGC quantity and quality. Third, the optimal subsidy level positively depends on the content provider’s sensitivity to the subsidy. In most cases, the magnitude of the optimal quantity subsidy should be larger than that of the optimal attention subsidy. Finally, to maintain or improve UGC quality, the platform should set a threshold to restrict the amount of content accessible to viewers. Doing so can prevent content providers from offering more content at the expense of content quality.
Social commerce has significantly enlarged consumer power through the convenience of posting and accessing online customer reviews. While positive comments may help to boost an online retailer’s reputation, the visibility of negative comments may significantly damage the retailer’s reputation. The purpose of this paper is to study trust violations caused by negative comments and corresponding retailer response strategies for trust repair thereby determining which response strategies should be chosen to repair consumer trust most effectively. Structured equation modeling method is applied to test how negative comments affect user trust, and a trust repair model to test how the company’s responses affect trust repair. The results show that perceived retailer service problems including quality problem, attitude problem and fulfillment problem reflected in negative reviews have significant negative impacts on three dimensions of consumer trust beliefs (benevolence, integrity and competence), and consumer trust beliefs have significant impacts on consumer purchase intentions. The results also show that different response strategies tend to reduce the degree of the perceived problems differently, an apology strategy was only effective in the recovery of attitude problem perceptions, while an explanation strategy had the most significant impact on the recovery of all three service problem perceptions, and a compensation strategy was effective in the recovery of quality problem perceptions. These response strategies change consumers’ perception of service problems, then further repair customer trust and hence purchase intentions.
Teleworking refers to the utilization of information and communication technologies for work done outside the workplace. The Covid-19 crisis led to increased utilisation of social networking tools within enterprises, especially when working remotely. The aim of their use is often to improve situational awareness, coordination, and collaboration amongst employees. Online social transparency, typically done through social networks or enterprise social software, refers to the voluntary sharing of personal and contextual information such as those relating to their own and team status, intentions, motivation, capabilities, goal priorities besides updates on the physical and social context, with other colleagues. An ad-hoc practice of social transparency can introduce risks such as information overload, social loafing and peer pressure. Despite recognising its adverse effects, there is a lack of systematic methods that identify and assess the risks of online social transparency. In this paper, we present a method to identify and evaluate these within enterprises. We present the method’s workflow, stakeholders, the novel artefacts and techniques devised to use and the outcomes to produce. We evaluate our proposed method by applying it in a real organisational context and assess applicability, efficiency, and effectiveness in identifying risks and supporting managers in risk assessment. The results showed that the method gives a framework of thinking and analysis and helps recognize and identify risks in a specialized manner.
Software as a Service (SaaS) provided by cloud computing has recently gained widespread adoption. Because of increased competition in the SaaS market, it is essential for a SaaS provider to properly design its computing system. Significant gains can be achieved by efficiently clustering software applications. This paper focuses on the application grouping problem encountered in computer clustering in SaaS networks. We present integer programming formulations and propose an efficient solution procedure based on the column generation technique applied to the problem. The results of a comprehensive computational study show that our column generation-based approach performed very well for large problem instances with optimality gaps varying between 0.00 and 3.02% with an average of 0.98% compared to optimality gaps varying between 0.00 and 230.64% with an average of 99.08% using a standard branch and bound technique as implemented by a state-of-the-art commercial solver.
Technological innovation capability (TIC) refers to a fundamental ability owned by an organization to invest and reorganize production factors in technology innovation for gaining competitive advantages. It has attracted increasing attention to evaluate enterprises’ TIC in a competitive market. However, the existing methods have some limitations in the accuracy and efficiency of assessing TIC. This paper proposes a novel approach of evaluating enterprises’ TIC, which combines the entropy weight method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on patent information. Entropy can determine the weights of indicators that help improve the rationality of the weighting. Besides, TOPSIS may reasonably rank the calculating results, which helps solve poor results in a fuzzy comprehensive evaluation. The paper takes seven enterprises in the solar cell technology field as samples to illustrate the proposed method. The results show that the Entropy-TOPSIS method can effectively evaluate enterprises’ TIC and is more suitable for small samples.
The purpose of this paper is to explore the role of WeChat mobile-payment (m-payment)-based smart technologies in improving the retail customer experience and to develop an integrated framework of the smart retail customer experience including antecedents, consequences, and moderators. Based on the stimulus-organism-response (SOR) paradigm, we investigated the relationships among socio-technical stimuli, the smart retail customer experience, and relationship quality. We also developed hypotheses regarding the moderating role of customer lifetime value (CLV), which is considered an important customer characteristic. The proposed framework was empirically tested based on transaction and survey data of 462 WeChat m-payment retail customers. The results showed the following. (1) WeChat m-payment-based smart retail technology can enhance the customer experience by improving customer-perceived relationship orientation, employee-customer interaction, and communication effectiveness. (2) CLV has a positive moderating effect on the relationship between socio-technical stimuli and the customer experience. (3) The customer experience has a positive influence on relationship quality in the retail industry. Retail managers should make full use of smart retail technologies to improve the customer experience. In addition, they should emphasize the increase in CLV, as this increase enhances the positive relationship between socio-technical stimuli and the customer experience, making customer experience management more efficient.
Users who seek results pertaining to their queries are at the first place. To meet users’ needs, thousands of webpages must be ranked. This requires an efficient algorithm to place the relevant webpages at first ranks. Regarding information retrieval, it is highly important to design a ranking algorithm to provide the results pertaining to user’s query due to the great deal of information on the World Wide Web. In this paper, a ranking method is proposed with a hybrid approach, which considers the content and connections of pages. The proposed model is a smart surfer that passes or hops from the current page to one of the externally linked pages with respect to their content. A probability, which is obtained using the learning automata along with content and links to pages, is used to select a webpage to hop. For a transition to another page, the content of pages linked to it are used. As the surfer moves about the pages, the PageRank score of a page is recursively calculated. Two standard datasets named TD2003 and TD2004 were used to evaluate and investigate the proposed method. They are the subsets of dataset LETOR3. The results indicated the superior performance of the proposed approach over other methods introduced in this area.
The software industry has widely adopted global software development (GSD) to gain economic benefits. Organizations that engage in GSD face various challenges, the majority being associated with requirements change management (RCM). The key motive of this study is to develop a requirement change management and implementation maturity model (SRCMIMM) for the GSD industry that could help the practitioners to assess and manage their RCM activities. A systematic literature review (SLR) and questionnaire survey approach are used to identify and validate the critical success factors (CSFs), critical challenges (CCHs), and the related best practices of the RCM process. The investigated CSFs and CCHs are classified into five maturity levels based on the concepts of the existing maturity models in other domains, practitioners’ feedback, and academic research. Every maturity level comprises different CSFs and CCHs that can help assess and manage a firm's RCM capability. To evaluate the effectiveness of the proposed model, four case studies are conducted in different GSD firms. The SRCMIMM has been developed to assist GSD organizations in improving their RCM process in efficiency and effectiveness.
This study highlights information networks for COVID-19 according to race/ethnicity by employing social network analysis for Twitter. First, this study finds that racial/ethnic groups are differently dependent on racial/ethnic key players. Whites and Asians show the highest number of racial/ethnic key players, Hispanics have a racial/ethnic key player, and blacks have no racial/ethnic key player in the top 20. Second, racial/ethnic groups show different characteristics of information resources for COVID-19. Whites have the highest key player group in news media, politicians, and researchers, and blacks show the highest key player group in news media. Asians demonstrate the highest key player group in news media, and Hispanics exhibit institutes as the highest key player group. Lastly, there are some differences in group communications across the race/ethnicity. Whites and blacks show open communication systems, whereas Asians and Hispanics reveal closed communication systems. Therefore, governments should understand the characteristics of communications for COVID-19 according to the race/ethnicity.
Since most of today’s consumers make purchase decisions based on online reviews, managers and researchers have been keen to determine how best to present review information in an online shopping context to maximize their persuasive power. Most online reviews are presented post-by-post, whereby individual reviewers express their respective opinions but lack group dynamism. As a result, it is worth asking what would happen if individual reviews are presented as a group? Drawing on social presence theory and information adoption literature, we propose a research framework to investigate the influences of two alternative presentation forms of review information (i.e., individual-based vs. group-based) on multiple-facet consumer evaluation of reviews, as well as their adoption of review information. By conducting two experiments (Study 1: N = 319; Study 2: N = 101), we find that, when given the same review information, consumers presented with the grouped review information rated higher review quality and credibility, but lower understandability, than consumers who were presented with individual review information. In addition, review quality, credibility, and understandability mediated the influence of review presentation forms on the consumer adoption of review information. Both theoretical and practical implications are discussed.
Reward-based crowdfunding is an emerging business model whereby entrepreneurs raise money from several small investors. These investors, in turn, receive products/services as a reward. Investors’ participation is the key to success for this business model. We use the means-end theory to examine the success of the reward-based crowdfunding mechanism. We present an integrated model by considering two platform-based service attributes—platform characteristics (institutional mechanisms and information transparency) and entrepreneur efforts (content, embeddedness, and interaction)—and explore their influence on an investor’s perceived value (functional value, price value, emotional value, and social value), and participation intention. We test the model using survey data of 218 responses from 65 crowdfunding projects on a leading crowdfunding platform. The results show that all perceived values are positively associated with an investor’s future participation intention. An entrepreneur’s efforts to provide high-quality content and encourage interactions and the platform’s characteristics of institutional mechanisms improve an investor’s perceived functional, price, emotional, and social values. In addition, information transparency positively influences an investor’s perceived price, functional and emotional values. This study contributes to crowdfunding research by providing a comprehensive understanding of the success of a reward-based crowdfunding project. At the same time, the results of this study will be helpful for crowdfunding entrepreneurs and platforms.
This paper aims to identify and understand factors affecting insiders’ intention to disclose patients’ medical information and to investigate how these factors affect the intention to disclose. Based on the literature review on deterrence theory and health information security awareness (HISA), we identify relevant factors and develop a research model explaining insiders’ intention to disclose patients’ health information. We collect data (N = 105) through scenario-based experiments. Results show that two personal factors, collectivism, and IT proficiency, play a significant role in the model. While collectivism affects two components (health information security regulation awareness and punishment severity awareness) of HISA which influences intention to disclose, IT proficiency moderates the relationship between HISA and intention to disclose. In addition, HISA negatively affects reporting assessment and intention to disclose. This paper aims to fill a research gap in understanding factors affecting insiders' intentions to disclose protected health information. We identify and investigate factors (e.g., collectivism, HISA, reporting assessment, and IT proficiency) that may affect insiders' disclosing intentions. We find that collectivism affects two components of HISA which influence reporting assessment and disclosing intention. We also discover that IT proficiency moderates the relationship between HISA and intention to disclose. Our findings suggest that we need to carefully consider personal factors such as collectivistic nature and IT proficiency in managing insiders' security breaches.
In today's online market, recommendation systems have become universal and are an aspect of any online shopping portal. The traditional approach uses the subscriber's historical knowledge, and this technique is not adequate for resolving problems with a cold start. These issues include recommendations for non-registered users or newly added customers and new items added. Session-based recommendations based on recurrent neural networks are gaining popularity for product recommendations. This is due to recurrent neural networks' ability to record sequential feature data more effectively throughout the current session, which results in more similarity between consumer behaviour sequences. Nevertheless, most state-of-the-art recurring neural networking systems completely ignore the long-term details of multiple sessions and concentrate solely on short-term communication in a single session. This paper presents a hybrid time-centric prediction model to address research issues that learn the customers' short and long-term behaviours. Experiments on the recsys challenge data set are carried out to assess the efficiency of the hybrid time-centric prediction models over the existing hybrid models in terms of HitRate and Mean-Reciprocal Rate.
This paper investigates the deterministic factors for e-commerce adoption and the factors that affect e-commerce performance after adoption in manufacturing firms. By using a large survey dataset on small and medium-sized enterprises (SMEs) in Jiaxing city, we find that (1) e-commerce adoption by manufacturing firms is negatively correlated with the firm age and positively correlated with firm size, their experience of supplying for Internet firms, and their own online shopping experience; (2) the e-commerce performance of the manufacturing firms is positively correlated with firms’ experience with e-commerce and firms’ size in e-commerce business; (3) all the above findings are similar for retailing firms, but manufacturing firms’ online performance relies more on their online firm size, and the effect of location choice is more salient for retailing firms. These findings complement the existing literature on e-commerce adoption and have important implications for the development of e-commerce adoption in developing countries.
In an online social networking services (SNS) community, a group of users that have great influence on the other users are called opinion leaders. They have received much research attention because they are capable of influencing other people’s purchasing behaviors. In this research, we investigate various motivational factors that influence SNS users’ electronic word-of-mouth (e-WOM) intention. In addition, we examine the role of opinion leadership in e-WOM intention. By using survey methodology approach based on prior research, we collected data from 405 SNS users in the U.S. This research utilized the partial least squares technique for analysing empirical data. We find that intrinsic motivational factors embracing altruism, self-efficacy, and self-expression universally influence SNS users’ e-WOM intention regardless of opinion leadership. However, extrinsic motivational factors comprising economic rewards, reputation feedback, and social ties only influence SNS users’ e-WOM intention through opinion leadership. This research fills a gap in the literatures by establishing a comprehensive research framework including intrinsic and extinct driving forces on E-WOM intention. It emphasizes the significance of opinion leadership in the context of SNS users’ purchasing behaviour. This study constructs a research model to discern the differences in the magnitude of impact between intrinsic and extrinsic motivational factors on e-WOM intention via opinion leadership. Our results confirm that both intrinsic and extrinsic motivations positively affect SNS users’ perceived opinion leadership. Our research findings imply that firms need to strengthen their efforts in fostering users’ perceived opinion leadership to utilize e-WOM in SNS community if they want to use extrinsic motivational factors to promote e-WOM behavior.
There is a lack of clarity in information systems research on which factors lead people to use or not use technologies of varying degrees of perceived legality. To address this gap, we use arguments from the information systems and political ideology literatures to theorize on the influence of individuals’ political ideologies on online media piracy. Specifically, we hypothesize that individuals with a more conservative ideology, and thus lower openness to experience and higher conscientiousness, generally engage in less online media piracy. We further hypothesize that this effect is stronger for online piracy technology that is legally ambiguous. Using clickstream data from 3873 individuals in the U.S., we find that this effect in fact exists only for online media piracy technologies that are perceived as legally ambiguous. Specifically, more conservative individuals, who typically have lower ambiguity intolerance, use (legal but ambiguously perceived) pirated streaming websites less, while there is no difference for the (clearly illegal) use of pirated file sharing websites.
Solvers’ continuance participation intention is central to the survival and development of online crowdsourcing platforms. This study integrates sense of belonging and social beliefs (i.e., perceived fairness and platform trust) to understand continuance intention. This study proposes that perceived fairness and platform trust are helpful to build solvers’ sense of belonging, which is assumed to be positively associated with sustained intention. Another core contribution points to the complementary relationships, that is, perceived fairness and platform trust help solvers derive meaningfulness from their attachment that encourages sustained intention. Using a sample of 290 solvers obtained from an online crowdsourcing platform, the empirical testing provides support for the significant and positive effect of sense of belonging, which can be derived from high levels of procedural fairness and platform trust. Interestingly, results further support the complementaries between sense of belonging and its antecedents on continuance intention. Some new knowledge and implications can be contributed by this study.
With the rapid development of information technology (IT) and information systems (IS), the emergence of information silos has become a severe impediment to their development. Silos have made IS usage inconvenient and inefficient, impeding enterprises’ innovation and development. Thus, the present study aims to resolve these issues by helping understand how to encourage information-resource sharing within the enterprise. A new concept, consensus perception, is proposed based on blockchain characteristics and advantages. A conceptual model is then developed based on principal-agent theory to investigate how to promote information-resource sharing and whether blockchain technology positively promotes information-resource sharing. This research uses structural equation modeling (SEM) to study the influence of consensus perception on information-resource sharing intention. The results show that information security concern and openness directly and significantly influence the intention to share, and that trust has an insignificant influence. However, the impact of privacy concerns is not supported. The findings provide valuable contributions to the literature on BT adoption, Information management (IM), and IS usage.
As individuals are the actual agents of knowledge management (KM) activities, they are influenced by the technical and social aspects of an organization. The effects of social and technical aspects on KM, however, have either been studied separately, or one aspect has been emphasized over the other. This study used the multilevel approach to investigate the interaction between technical and social systems within the work system of KM by examining how the social system moderates the effects of the technical system on KM activities. The social system is operationalized as a team climate, which is the socially shared perception among members within a team, whereas the technical system is operationalized as the perceived value of the KM systems (KMS), which is the technical information system that deals with organizational knowledge and is realized in the work setting in the form of the perception of individuals. We conducted a field study that involved 80 teams of 419 individuals from three knowledge-intensive companies. A hierarchical linear model was employed to analyze the multilevel structure: individual-level KMS perceptions for operational support and strategic decision support, and KM activities with the team-level affective and innovative climates. Our findings show that the innovative team climate magnifies the effect of the perceived KMS value of individuals for strategic decision support on their knowledge adoption; whereas, the affective climate strengthens the effect of the perceived KMS value of individuals for operational support on their knowledge transformation.
The recommendation systems plays an important role in today's life as it assist in reliable selection of common utilities. The code recommendation system is being used by the code databases (GitHub, source frog etc.) aiming to recommend the more appropriate code to the users. There are several factors that could negatively impact the performance of code recommendation systems (CRS). This study aims to empirically explore the challenges that could have critical impact on the performance of the CRS. Using systematic literature review and questionnaire survey approaches, 19 challenges were identified. Secondly, the investigated challenges were further prioritized using fuzzy-AHP analysis. The identification of challenges, their catego-rization and the fuzzy-AHP analysis provides the prioritization-based taxonomy of explored challenges. The study findings will assist the real-world industry experts and to academic researchers to improve and develop the new techniques for the improvement of CRS.
This paper investigates two competitive strategies from two-sides of the e-commerce platform, that is, innovation investment on seller side and product subsidy investment on consumer side. We take competition intensity on seller side into account and analyze how consumer behaviors affect the platform’s strategy under three scenarios: (1) single purchase on single platform(S); (2) single purchase on multi-platforms(M); (3) repeat purchase on single platform (R). The results revel that the innovation investment for sellers is better off in S scenario. However, when the transfer cost is low, taking subsidy strategy is more profitable for the platform in R scenario. If the internal price competition is not sufficiently fierce, subsidy strategy is an efficient approach to reduce the price in M scenario. It is surprising that if the seller’s innovation capability is sufficiently high, the innovation investment strategy dominates no matter what consumer behaviors are. Moreover, how much the platform invests on the seller’s innovation is independent on the consumer’s behavior. These findings have practical managerial insights for the manager of platforms.
Covid 19 presents a great challenge and opportunity for remote working, highlighting the need for electronically-mediated leadership in team tasks and performance. What is the role of leadership in improving utilization of information communication technologies (ICTs) in teamwork? Framed within the e-leadership and project management literature and employing a longitudinal field observation method over 8 months that involves 52 subjects and 172 observations, this study finds that (1) first, strong leaders employ a consistent and high-level use of ICTs throughout the whole process of group work, especially at the planning and closing stages of a project. (2) Second, strong leaders alternate the use of various ICTs to match specific tasks at different phases of the project. Two media platforms—team discussion forum and document sharing— stand out as the most important for strong leaders to build trust and execute tasks. (3) Finally, in a project management setting with a group of transient members with clearly-defined tasks and time-sensitive responsibilities, trust-building is a continual and highly significant leadership responsibility that precedes other leadership responsibilities. Trust is built largely through alternating the use of two rich ICT media (discussion forum and instant messaging) with two lean ICT media (document sharing and presentation display). These findings highlight a significant role of e-leadership in organizations which see the emergence of ICTs especially during crises like Covid 19.
As an significant element for the continuation and development of modern design, conventional Chinese decorative patterns satisfy people's emotional dependence on nationality and tradition. At present, traditional patterns are designed mainly by professional designers. Due to the high dependence of the entire design process on labor, the design efficiency is untoward to improve to rapidly meet the explosive growth of business needs. However, a large amount of material data on the Internet and the development of artificial intelligence technology provides an opportunity to work out this development bottleneck. This paper mainly studies the design and implementation of the automatic generation of traditional pattern image layout based on the generation of confrontation network and aesthetic evaluation to score the quality of the generated traditional pattern image. And the scoring results are fed back to the traditional pattern layout generation network to improve the quality of the generated traditional pattern layout pictures so that the entire network structure can adapt to disparate scene requirements. The results show that the SSIM value of the structural similarity obtained by the image of the traditional pattern automatically generated by the model in this paper is larger and closer to 1 than the value of the SSIM obtained by other methods, indicating that the image of the traditional pattern automatically generated by the model has the highest aesthetic quality. Furthermore, the research provides assistance for intelligent technology to solve the development bottleneck of conventional pattern design. Finally, the future research of digital image quality evaluation approach is prospected.
This paper proposes a data mining approach for automatic customer targeting based on their expected profitability. The main challenge with customer profitability prediction is asymmetry, i.e., skewness of the distribution, because the number of highly profitable customers is very small compared to others. Although data mining methods are more resistant to sample heterogeneity than statistical ones, due to strong skewness, the accuracy of predictions often decreases as the value of profit increases. These few customers are actually outliers which can make data-driven methods to overestimate predicted amounts, but on the other hand, they contain very important information about the most valuable customers, so it is not advisable to remove them. In this paper, a data mining approach for overcoming these problems is proposed. The results show that the relative error in predicting the absolute amount of the profitability of the most valuable customers is very small and does not differ much from the error for other customers, unlike previously applied methods where predicting high profitability was less accurate. Accordingly, the specific implication of the high accuracy is more efficient identification of the most profitable customers, which ultimately make a greater contribution to the company in terms of revenue. Also, due to the good precision of the model, errors in the assessment of highly profitable and risky customers are reduced, which leads to savings in unnecessary costs for the marketers.
The development of digitization over the globe has made digital security inescapable. As every single article on this planet is being digitalized quickly, it is more important to protect those items. Numerous cyber threats effectively deceive ordinary individuals to take away their identifications. Phishing is a kind of social engineering attack where the hackers are using this kind of attack in modern days to steal the user's credentials. After a systematic research analysis of phishing technique and email scam, an intrusion detection system in chrome extension is developed. This technique is used to detect real-time phishing by examining the URL, domain, content and page attributes of an URL prevailing in an email and any web page portion. Considering the reliability, robustness and scalability of an efficient phishing detection system, we designed a lightweight and proactive rule-based incremental construction approach to detect any unknown phishing URLs. Due to the computational intelligence and nondependent of the blacklist signatures, this application can detect the zero-day and spear phishing attacks with a detection rate of 89.12% and 76.2%, respectively. The true positive values acquired in our method is 97.13% and it shows less than 1.5% of false positive values. Thus the application shows the precision level higher than the previous model developed and other phishing techniques. The overall results indicate that our framework outperforms the existing method in identifying phishing URLs.
Emissions from road traffic contribute to climate change. One approach to reducing the carbon footprint is providing eco-driving feedback so that drivers adapt their driving style. Research about the impact of eco-feedback on energy consumption is the basis for designing a mobile eco-driving feedback information system that supports drivers in reducing fuel consumption. This work develops design knowledge from existing knowledge. Subsequently, we implement a prototypical instantiation based on the derived knowledge. Insights from a field study suggest that our design artifact allows most drivers to decrease fuel consumption by 4% on average. The paper’s theoretical contribution is a set of design principles and an architecture of the proposed mobile eco-driving feedback information system. One recommendation is to provide normative feedback that compares drivers with each other. This feedback appears to encourage drivers to decrease their fuel consumption additionally. The design knowledge may support researchers and practitioners in implementing efficient eco-driving feedback information systems.
The volume of digital text data is continuously increasing both online and offline storage,
which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic text summarization models.
The internet of things has ushered in a world of possibilities in chronic disease management. Connected to the health information network, a health device can monitor and provide intervention recommendations to patients in real time. However, this new health information system may face the risk of patients not following the system’s recommendations depending on their perception of the system. In this paper, we consider patients’ trust in the system a key factor driving their adherence to the system’s recommendation and develop an analytical model to design the optimal alerting strategy in the context of asthma management. Our method acknowledges that patient’s trust may change over time based on their experience of using the system, which may influence their future adherence behavior. We derive a set of structural properties of our solution and demonstrate that our approach can significantly improve patients’ quality of life compared to the current practice of asthma management. Furthermore, we investigate various real-world scenarios, such as the case that patients may have different level of tolerance for receiving alerts. Based on our findings, valuable insights can be shared with patients, healthcare practitioners, and companies in the technology-enabled healthcare business sector.
We proposed a method employing deep learning (DL) on eye-tracking data and applied this method to detect intentions to use apparel websites that differed in factors of depth, breadth, and location of navigation. Results showed that users’ intentions could be predicted by combining a deep neural network algorithm and metrics recorded from an eye-tracker. Using all of the eye-tracking metric features attained the best accuracy when predicting usage/not-usage intention to websites. In addition, the results suggest that for apparel websites with the same depth, designers can increase usage intention by using a larger number of navigation items and placing the navigation at the top and left of the homepage. The results show that building intelligent usage intention-detection systems is possible for the range of websites we examined and is also computationally practical. Hence, the study motivates future investigations that focus on design of such systems.
Twitter is one of the most popular and renowned online social networks spreading information which although dependable could lead to spreading improbable and misleading rumors causing irreversible damage to individuals and society. In the present paper, a novel approach for detecting rumor-based conversations of various world events such as real-world emergencies and breaking news on Twitter is investigated. In this study, three aspects of information dissemination including linguistic style used to express rumors, characteristics of people involved in propagating information and structural features are studied. Structural features include features of reply tree and user graph. Structural features were extracted as new features in order to enhance the efficiency of the rumor conversations detection. These features provide valuable clues on how a source tweet is transmitted and responds over time. Experimental results indicate that the new features are effective in detecting rumors and that the proposed method is better than other methods as F1-score increased by 4%. Implementation of the proposed method was carried out on Twitter datasets collected during five breaking news stories.
Crowdsourcing has been recognized as a key means to leverage solvers’ expertise for seeker firms’ on-demand value creation. However, the lack of long-term relationship with a specific solver creates uncertainty and few empirical studies considered seeker firms’ adoption decision based on resource and ability provision from solvers. This study fills this gap by proposing a comprehensive model that integrates the resource-based view with the theory of reasoned action. This model captures IT managers’ reasoned action from opportunity-cost evaluation, which in turn is influenced by the value creation from solvers externally. Specifically, solvers’ ability is conceptualized as expertise, service quality, and compatibility in terms of coordination of partners’ capabilities. This study uses survey method for data collection and partial least squares for testing hypotheses. The results show that operational expertise, service quality, and business-process compatibility exert positive impact on opportunity-cost evaluation. This evaluation in turn affects adoption intention.
An important requirement of an intrusion detection system (IDS) is that it be effective and efficient; that is, it should detect a large percentage of intrusions, while still keeping the false alarm rate at an acceptable level. In order to meet this requirement, the model and algorithm used by the IDS need to be calibrated or configured. The optimal configuration depends on several factors. The first factor is the quality profile of the IDS as indicated by its ROC (receiver operating characteristics), curve that relates the detection accuracy and the false alarm rate. The shape of the ROC curve depends on the detection technology used by the IDS. The second factor is the cost structure of the firm using the IDS. The third factor is the strategic behavior of hackers. A hacker’s behavior is influenced by the likelihood that (s)he will be caught, which, in turn, is dependent on the configuration of the IDS. In this article, we present an economic optimization model based on game theory that provides insights into optimal configuration of IDS. We present analytical as well as computational results. Our work extends the growing literature on the economics of information security. The main innovation of our approach is the inclusion of strategic interactions between IDS, firm, and hackers in the determination of optimal configuration and algorithm to do so.
With the development of online retailing on third-party e-commerce platforms such as Taobao, the churn of individual shops is becoming increasingly severe. Platform operators are eager to learn the causes of this eroding trust. Based on Hofstede’s cultural dimensions theory, this paper aims to investigate how masculinity, uncertainty avoidance and long-term orientation impact platform trust and the interaction between these three cultural variables. Based on data collected from sellers on the largest Chinese C2C platform, we found that uncertainty avoidance had a negative effect on platform trust, while masculinity and long-term orientation exerted a positive impact on platform trust. A significant association among the cultural factors was also found.
Product exclusivity is a well-known strategy adopted by agencies to attract buyers. Although some platform firms develop first-party products to improve competitiveness, we show that they do not necessarily benefit from the exclusivity of these products in a competitive market. Platforms may sell their first-party products on their competitive platforms, which we call the non-exclusive strategy. The impact of the non-exclusive strategy on firms’ profits is the result of an interaction between a competition effect and a demand effect. The competition effect is that the non-exclusive strategy reduces the differentiation between firms, and the investment strategy on the first-party product has no impact on the price competition between firms. The competition effect causes firms to intensify price competition and decrease the investment for their first-party product. The demand effect is that, in the presence of the non-exclusive strategy, the firm can obtain a sale profit from the first-party products in the competitive platform. The demand effect causes firms to diminish price competition and increase investments in first-party products. As a result of these two effects, we find that firms are more likely to benefit from the exclusivity of the first-party products only when the unit misfit cost is sufficiently high. We also find that firms implementing the non-exclusivity strategy will reduce the investment in the first-party product if the consumers’ performance sensitivity is low. When platforms develop the targeted investment in first-party products, we can also see that the exclusivity of the first-party product is not always beneficial to the platform.