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With the proliferation of big data and business analytics practices, data storytelling has gained increasing importance as an effective means for communicating analytical insights to the target audience to support decision-making and improve business performance. However, there is a limited empirical understanding of the relationship between data storytelling competency, decision-making quality, and business performance. Drawing on the resource-based view (RBV), this study develops and validates the concept of data storytelling competency as a multidimensional construct consisting of data quality, story quality, storytelling tool quality, storyteller skills, and storyteller domain knowledge. It also develops a mediation model to examine the relationship between data storytelling competency and business performance, and whether this relationship is mediated by decision-making quality. Based on an empirical analysis of data collected from business analytics practitioners, the results of this study reveal that the data storytelling competency is positively linked to business performance, which is partially mediated by decision-making quality. These results provide a theoretical basis for further investigation of possible antecedents and consequences of data storytelling competency. They also offer guidance for practitioners on how to leverage data storytelling capabilities in business analytics practices to improve decision-making and business performance.

This study aims to examine and compare the mechanisms through which social learning processes influence the knowledge contribution behavior of lurkers and contributors in open innovation communities (OICs). Based on social learning theory and stimulus–organism–response (SOR) framework, this study developed a model of knowledge contribution formation mechanism from environmental stimuli (observational learning, reinforcement learning), organism cognition (self-efficacy, outcome expectancy) to behavioral response (initial contribution, continuous contribution). The model was tested using structural equation modeling based on a dataset collected through a questionnaire from an OIC of business intelligence and analytics software. The empirical results showed that, at the initial participation stage, observational learning had a significant effect on the organism’s cognition of the lurkers, and indirectly influenced the initial knowledge contribution behavior through self-efficacy and outcome expectancy. At the continuous participation stage, observational learning had a significantly lower impact on the organism’s cognition of contributors and only indirectly influenced continuous knowledge contribution behavior through outcome expectancy. In contrast, reinforcement learning influenced the organism’s cognition of contributors and partially influenced their continuous knowledge contribution behavior through the mediating effects of self-efficacy and outcome expectancy. However, self-efficacy had a more pronounced effect on contributors’ continuous knowledge contribution behavior than outcome expectancy. These findings provide practical guidance for the management of OICs to reduce knowledge contributor attrition and induce lurkers to evolve into knowledge contributors for sustainable community development.

Enterprises seeking to enhance their innovation capabilities are increasingly turning to open innovation communities (OICs), which allow them to leverage the collective knowledge and collaborative potential of external users, providing a powerful source of new and innovative ideas. Despite their potential for value co-creation, recent research suggests that value co-destruction can also occur within OICs. However, the mechanisms underlying value co-destruction in OICs have not yet been fully explored or empirically examined. To address this gap, this study employs expectancy disconfirmation theory and psychological contract theory to investigate the relationship between user expectancy disconfirmation and value co-destruction in OICs. Drawing upon data collected from a questionnaire survey of business analytics OICs, this study reveals that self-interest expectancy disconfirmation has a positive effect on value co-destruction, which is mediated by the transactional psychological contract breach. In addition, social interaction expectancy disconfirmation is found to have a positive impact on value co-destruction, which is mediated by the relational psychological contract breach. The study further reveals that self-worth expectancy disconfirmation of community users positively influences value co-destruction, which is mediated by the ideological psychological contract breach. Moreover, the study demonstrates the crucial role of perceived organizational status in moderating the ideological psychological contract breach resulting from self-worth expectancy disconfirmation. Collectively, these findings contribute valuable insights into the phenomenon of value co-destruction in OICs, and provide practical guidance for enterprises seeking to enhance the development and performance of these innovation paradigms.

Digital platform business model innovation is a rapidly evolving field, yet the literature on resource, complementary, and ecological boundaries remains limited, leaving a significant gap in our understanding of the factors that shape the success of these platforms. This paper explores the mechanisms by which digital platforms enable business model innovation, a topic of significant theoretical and practical importance that has yet to be fully examined. Through a review of the existing literature and an examination of the connotations of digital platforms, the design of platform boundaries, and the deployment of boundary resources, the study finds that (1) the uncertainty of complementors and complementary products drives business model innovation in digital platforms; (2) the design of resource, complementary, and ecological system boundaries is crucial to digital platform business models and manages complementor and complementary product uncertainty while promoting value co-creation; and (3) boundary resources establish, manage, and sustain cross-border relationships that impact value creation and capture. Based on these findings, four research propositions are proposed to guide future research on digital platform business model innovation and provide insights for effectively innovating business models and influencing value creation and capture.

The heterogeneity and diversity of users and external knowledge resources is a hallmark of open innovation communities (OICs). Although user segmentation in heterogeneous OICs is a prominent and recurring issue, it has received limited attention in open innovation research and practice. Most existing user segmentation methods ignore the heterogeneity and embedded relationships that link users to communities through various items, resulting in limited accuracy of user segmentation. In this study, we propose a user segmentation method in heterogeneous OICs based on multilayer information fusion and attention mechanisms. Our method stratifies the OIC and creates user node embeddings based on different relationship types. Node embeddings from different layers are then merged to form a global representation of user fusion embeddings based on a semantic attention mechanism. The embedding learning of nodes is optimized using a multi-objective optimized node representation based on the Deep Graph Infomax (DGI) algorithm. Finally, the k-means algorithm is used to form clusters of users and partition them into distinct segments based on shared features. Experiments conducted on datasets collected from four OICs of business intelligence and analytics software show that our method outperforms multiple baseline methods based on unsupervised and supervised graph embeddings. This study provides methodological guidance for user segmentation based on structured community data and semantic social relations and provides insights for its practice in heterogeneous OICs.

The importance of business analytics (BA) in driving knowledge generation and business innovation has been widely discussed in both the academic and business communities. However, empirical research on the relationship between knowledge orientation and business analytics capabilities in driving business model innovation remains scarce. Drawing on the knowledge-based view and dynamic capabilities theory, this study develops a model to investigate the interplay between knowledge orientation and BA capabilities in driving business model innovation. It also explores the moderating role of industry type on this relationship. To test the model, data were collected from a cross-sectional sample of 207 firms (high-tech and non-high-tech industries). Descriptive and structural equation modeling (SEM) were used to test the hypotheses. The findings showed that knowledge orientation and BA capabilities are significantly and positively related to business model innovation. Knowledge commitment, shared vision, and open-mindedness are significantly and positively related to BA perception and recognition capabilities and BA integration capabilities. BA capabilities mediated the relationship between knowledge orientation and business model innovation. The path mechanism of knowledge orientation → BA capabilities → business model innovation shows that industry type has a moderating effect on knowledge orientation and BA capabilities, as well as BA capabilities and business model innovation. This study provides empirically proven insights and practical guidance on the dynamics and mechanisms of BA and organizational knowledge capabilities and their impact on business model innovation.

This study presents a systematic approach that integrates the information adoption model (IAM) with topic modeling to analyze the digital voice of users in online open innovation communities (OOICs) and empirically examines the usefulness of UGC with large amounts of redundant information and varying content quality across two dimensions: information quality and information source credibility. A total of 61,227 bug comments were collected from the OOIC of Huawei EMUI and analyzed using binary logistic regression. The results show that information timeliness and completeness have a positive effect on the usefulness of UGC in OOICs; conversely, information semantics have a negative effect on the usefulness of UGC. Prior user experience has no influence on the usefulness of UGC in OOICs, while active user contribution has a positive effect on the usefulness of UGC. The results of this study offer several implications to researchers and practitioners, and thus could serve as a pivotal reference source for further investigation of potential determinants of UGC usefulness in OOICs.

Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency.

Entrepreneurship is a multifaceted and constantly evolving process that is often marked by various challenges, such as environmental uncertainty, resource constraints, intense competition, and ambiguous roles. These complexities can give rise to ethical dilemmas, including conflicts of interest and unethical behavior, which can further be compounded by the incorporation of digital technology in entrepreneurship. Despite the growing recognition of the significance of entrepreneurial ethics, research in this area remains limited and fragmented. Therefore, this study aims to navigate the complexity of entrepreneurial ethics and address knowledge gaps by conducting a systematic review of the extant literature in the field of entrepreneurship, ethics, and management between 2003 and 2023 using the PRISMA protocol. The review focuses on three key aspects: (1) factors that shape entrepreneurial ethical perception and climate, (2) factors that influence entrepreneurial ethical decision making and behavior, and (3) the consequences of entrepreneurial ethical decisions and behavior. This study proposes future research avenues that can deepen our understanding of the interplay between digital technology and entrepreneurial ethics, stakeholder influence on ethical decision making, and the relationship between ethical leadership and entrepreneurial performance. Ultimately, the findings from this study provide a comprehensive framework for examining and comprehending the critical domain of entrepreneurial ethics, which can effectively address ethical dilemmas and establish socially conscious ventures that positively impact both the economy and society.