National digital identity systems (NDIDs) are increasingly important for users’ authentication and secure access to e-government services. However, there is insufficient research on human-centric cybersecurity (HCCS) that impacts the use of NDIDs. Drawing on the theory of planned behavior and technical formal informal model, this paper proposes and validates a research model that depicts how HCCS affect the use of NDIDs. Data were collected from 203 Australian residents and analyzed using structural equation modeling and multiple linear regression analysis. The findings revealed that security, privacy, perceived risk, usability, flexibility, and cultural and social interference significantly impact the use of NDIDs. Considering HCCS in NDIDs usage, especially in risk-conscious cultures, is crucial. Low cybersecurity awareness and trust impede NDIDs adoption, emphasizing the need for cybersecurity education and awareness. The insights benefit policymakers, governments, and cybersecurity practitioners, providing a valuable understanding of human-centric cybersecurity influence on the use of NDIDs.

This paper establishes the current state of human-centric cybersecurity factors that influence users’ adoption of national digital identity systems (NDIDs). NDIDs are national-level security systems that provide digital identity management services for secure authentication and access to online government services. Advances in NDIDs have raised concerns about human-centric cybersecurity factors. These concerns motivated researchers to explore the human aspects of cybersecurity. This paper critically synthesizes the literature on human-centric cybersecurity factors to enrich our knowledge of why users adopt or reject NDIDs. This paper identifies a combination of trust, privacy, perceived risk, usability, flexibility, cultural and social interference, and security factors that influence the adoption of NDIDs. This study builds a multi-level conceptual framework to contextualize human-centric cybersecurity factors influencing NDIDs adoption. This paper contributes to current literature and recommends that future research should consider non-technical aspects of cybersecurity that affect NDIDs adoption.

Supply chain management (SCM) faces a critical security issue because of the asymmetry of information delivered to various parties in the ecosystem and the lack of corresponding supervision. In response, we propose the use of blockchain technology to address the SCM security issues and put forward a blockchain-based SCM framework. We apply design science paradigm to guide the blockchain-based SCM framework development and implementation of a proof-of-concept prototype. We use Hyperledger Fabric and Composer to develop the prototype artifact. Performance evaluation results issued from Hyperledger Caliper prove the superiority and robustness of the proposed blockchain-based framework in terms of security and efficiency requirements, and performance metrics including throughput and latency. Also, the evaluation results show that the IT artifact is stable, and the high stability can reduce the risks of system vulnerabilities and breakdown.

Blockchain has transitioned beyond the hype to reality, as evidenced by the amount of research it has attracted and by its commercial applications. One popular application of blockchain is in cybersecurity, which is the focus of this paper. Specifically, we performed a systematic literature review of blockchain use cases for cybersecurity, while focusing on articles published over the past decade. Based on our analysis of 111 articles, we developed a classification framework using the thematic analysis approach. This classification framework is designed to offer readers a comprehensive perspective of the potential of blockchain to enhance cybersecurity in different contexts. The findings have implications for research and practice.

This article introduces a large-scale virtual hackathon where we observed the way participants found collaborators and undertook innovation processes entirely in the virtual world. As an emerging social-technical practice, the virtual hackathon leverages the power of familiar strangers, the improvisation of low-cost digital services, and the crowdsourcing mechanism to enable open innovation under the constraint of physical distancing. This study contributes to the research by introducing and conceptualizing a modified artifact – virtual hackathon. The implication of and the lessons learnt from the virtual hackathon are applicable and generalizable to organizations when managing virtual collaborations, digital infrastructure, and open innovation.

Phishing attacks are costly for both organizations and individuals, yet existing academic research has provided little guidance on how to strategize and implement a combined phishing awareness and training campaign. Drawing on operant conditioning theory, we conduct an in-depth case study on a large phishing awareness campaign and reveal that phishing awareness is a learning process through which individuals’ behavior can be strengthened by reinforcement and punishment. Based on the case study findings, we present several propositions for cybersecurity stakeholders. This study contributes to the phishing awareness literature and has implications for research and practice. This paper is useful for organizations planning or in the process of implementing or reviewing a phishing awareness and education program.

Abstract Richer forms of online reviews such as videos or follow‐on reviews convey ‘additional information and can attract consumers’ attention. However, prior studies focused mostly on the relationship between aggregated online reviews and sales. This paper investigates the impact of online review richness (i.e., reviews containing videos or follow‐on reviews) on sales. Leveraging attribute substitution theory, we conjecture that online review richness can provide heuristic cues in the online shopping environment to help consumers make better purchase decisions. Using data from JD.com , we found that reviews containing either videos or follow‐on reviews positively affect sales. In addition, different product types can also serve as heuristic cues to replace target cues, which can further affect how different forms of online review richness affect sales. We found that the impact of online review richness on sales is stronger for utilitarian products than for hedonic products, and stronger for negatively commented products than for positively commented products. Moreover, we conducted two online experiments and confirmed that the causal relationship is from online review richness to sales. The research findings offer practical implications for online retailers and constitute one of the first steps toward a better understanding of the relationship between online review richness and sales.

This paper investigates the drivers of the use of knowledge-sharing tools through the lens of task–technology fit (TTF), the role of social factors, and the cognitive and affective mechanisms. Data were collected from 294 knowledge workers and analyzed using partial least squares. This study found that cognition (i.e., perceived usefulness) and positive affect play an important role in mediating the effect of TTF, social influence, and trust on users’ behavioral intention. Interestingly, the role of negative affect as a mediator between sociotechnical factors and behavioral intention is considered not significant by the knowledge workers. This research has practical implications for organizations that are planning or reviewing their knowledge-sharing initiatives.

This paper presents a classification framework and a systematic analysis of literature on answer aggregation techniques for the most popular and important type of crowdsourcing, i.e., micro-task crowdsourcing. In doing so, we analyzed research articles since 2006 and developed four classification taxonomies. First, we provided a classification framework based on the algorithmic characteristics of answer aggregation techniques. Second, we outlined the statistical and probabilistic foundations used by different types of algorithms and micro-tasks. Third, we provided a matrix catalog of the data characteristics for which an answer aggregation algorithm is designed. Fourth, a matrix catalog of the commonly used evaluation metrics for each type of micro-task was presented. This paper represents the first systematic literature analysis and classification of the answer aggregation techniques for micro-task crowdsourcing.

When a crowd’s motivations are not triggered, they may not necessarily commit their best efforts, even if they have the knowledge to answer an open call. Drawing on the incentive theory, we introduce a top-down process model for an online crowdsourcing campaign that addresses the crowd’s motivations. This model is in contrast to the traditional bottom-up process model, where the crowd self-selects an open call based on their knowledge. We adopt a longitudinal case study method and examine two online crowdsourcing campaigns that represent both models. The findings suggest that the campaign that follows the top-down model generated high-quality ideas, while the bottom-up case was considered a failure. We further enrich the top-down model by developing a four-stage guidance model that addresses the crowd’s differing motivations in each stage. This research contributes to the crowdsourcing literature and helps better attract the qualifying crowd, thereby leading to greater campaign success likelihood.

Business intelligence (BI) technologies have received much attention from both academics and practitioners, and the emerging field of business analytics (BA) is beginning to generate academic research. However, the impact of BI and the relative importance of BA on corporate performance management (CPM) have not yet been investigated. To address this gap, we modeled a CPM framework based on the Integrative model of IT business value and on information processing theory. Data were collected from a global survey of senior managers in 337 companies. Findings suggest that the more effective the BI implementation, the more effective the CPM-related planning and analytic practices. BI effectiveness is strongly related to BA, planning and to measurement. In contrast, BA effectiveness is strongly related to planning but less so to measurement. The study suggests that although both BI and BA contribute to corporate management practices, the information needs are different based on the level of uncertainty versus ambiguity characteristic of the management practice.

This article examines the “chicken‐egg” two‐way relationships between online consumer reviews and sales, and assesses the dual influencer and indicator roles of online consumer reviews in relation to purchase behavior. Considering the time factor, we adopt the methodology of Granger causality test and track 3,390 products on Amazon.com over a 2‐month period. The results reveal that a causality loop exists between online consumer review volume and sales. Specifically, our findings indicate that the volume of negative consumer reviews drive consumers' purchasing decisions, but the volume of positive consumers reviews only marginally affects purchasing decisions. Also, consumers generate more positive reviews than negative reviews after sales. Our results highlight the importance of negative consumer reviews; negative reviews not only lead to sales, but sales, in turn, lead to higher volume of negative reviews. The findings suggest an alternative strategy for practitioners to address negative online consumer reviews and highlight the awareness effect of online consumer review postings that can later convert into purchase behaviors.

Extant studies suggest implementing a business intelligence (BI) system is a costly, resource‐intensive and complex undertaking. Literature draws attention to the critical success factors (CSFs) for implementation of BI systems. Leveraging case studies of seven large organizations and blending them with Yeoh and Koronios's (2010) BI CSFs framework, our empirical study gives evidence to support this notion of CSFs and provides better contextual understanding of the CSFs in BI implementation domain. Cross‐case analysis suggests that organizational factors play the most crucial role in determining the success of a BI system implementation. Hence, BI stakeholders should prioritize on the organizational dimension ahead of other factors. Our findings allow BI stakeholders to holistically understand the CSFs and the associated contextual issues that impact on implementation of BI systems.

This paper presents an integrated framework that comprises an automatic weighting method for assessing data quality (DQ) of the framework so as to better support the business intelligence (BI) usage. Specifically, we utilize business process modeling (BPM) notation and information product map and frame them into a hierarchical mapping structure. Furthermore, we develop and demonstrate an automatic weight-assignment method for evaluating critical dimensions (i.e., completeness and accuracy) of DQ of the integrated framework. Through a design science paradigm, the effectiveness of the framework and the associated DQ weighting method has been rigorously validated by faculty management users of a university. The framework together with the DQ weighting method builds user confidence by enhancing the traceability of a BI product. The automatic DQ weight assignment also provides better time efficiency because the weight of each data attribute is determined automatically based on its usage on the BI dashboard.

Corporate performance management (CPM) systems using business intelligence technologies can help enterprises monitor and manage business performance. In this research, we explored and presented empirical evidence on the key benefits of, and barriers to, the use of CPM systems through a survey of 283 organisations across North America and China. We identified three key benefits (strategy execution, process efficiency, and fact-based decision-making) and ten inhibiting barriers under respective project and organisational dimensions. Moreover, we found that in regard to the use of CPM systems, Chinese organisations perceived higher benefits, as well as higher barriers, than did their counterparts in North America. The socio-cultural differences between the two regions explain these issues. The research findings are useful for multinational organisations that are planning, or are in the process of implementing or reviewing their CPM systems, as well as for consulting companies that are assisting with such systems implementation in different regions.

A Business Intelligence (BI) system provides users with multi-dimensional information (a so-called ‘BI product') to support decision-making. However, existing BI systems overlook the lineage metadata which supports individual data quality dimensions such as data believability and ease of understanding. Using a design science research paradigm, this paper proposes and develops an integrated framework (known as BI Product and Metacontent Map - ‘BIP-Map') to facilitate the traceability and accountability of BI products. Specifically, the business workflow layer of the integrated framework is modelled using business process modelling notation, and an information product map is used to model the second layer's information manufacturing process, whilst the third layer represents the metacontent detail of the data validation stage, from source system through to ETL, to the data warehousing stage. Also, the authors develop a BIP-Map informed prototype in collaboration with an online job advertising firm, the framework then being validated by key BI stakeholders of the firm. The integrated framework addresses individual-related data quality issues and builds user confidence by enhancing the traceability and accountability of a BI product.