While it is clear that business analytics (BA), on average, adds value to firms, it is less clear why some firms perform better than others in leveraging their data assets. To a large degree, prior research has attributed such differences to the human factor. Nevertheless, there is still limited knowledge concerning what impact competencies of key personnel have on generating value from BA initiatives, and to what extent such outcomes complement a firm’s investment in BA. To examine these questions, this study explores the complementary relationship between BA capabilities and π-shaped skills. It posits that the concurrent presence of both indirectly influences innovation outcomes by fostering a data-driven culture. We build on a sample of 185 individuals with supervisory positions in Turkey to unpack this process. The findings revealed that BA capabilities and π-shaped skills influence a firm’s data-driven culture, which in turn impacts the firms’ marketing innovation, and subsequently its innovative performance. That is, data-driven culture and firms’ marketing innovation are relevant underlying serial mechanisms by which BA capabilities and π-shaped skills influence innovative performance.

1. There has been a very significant number of paper submissions to EJIS in recent years on the topic of artificial intelligence (AI), with many of these focusing in some way on responsibility. Thi...

Trolling on social media has a profound impact on its victims, yet existing literature offers a limited understanding of the factors driving this behavior. This study applies deindividuation and contagion theories to explore the phenomenon, surveying 337 Facebook users and 275 Instagram users and analyzing the data using SEM-PLS and fsQCA methods. The SEM results indicate that digital anonymity and dispersed collectivity both directly and indirectly impact trolling behavior, mediated by a loss of self-consciousness and a diffused sense of responsibility. The fsQCA analysis reveals four distinct equifinal configurations that predict trolling behavior, one for each platform, providing new insights into the research on trolling. This study contributes to the theoretical understanding of trolling and offers practical implications for addressing this issue.

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

• Synthesizes empirical studies on responsible AI and the underlying principles. • Differentiates between principles and governance of AI in a responsible way. • Analyzes through a critical lens existing studies and uncovers underlying assumptions. • Defines the notion of responsible AI governance based on the synthesis and critical reflection. • Develops a research agenda and identifies important areas for future research within the IS domain. The widespread and rapid diffusion of artificial intelligence (AI) into all types of organizational activities necessitates the ethical and responsible deployment of these technologies. Various national and international policies, regulations, and guidelines aim to address this issue, and several organizations have developed frameworks detailing the principles of responsible AI. Nevertheless, the understanding of how such principles can be operationalized in designing, executing, monitoring, and evaluating AI applications is limited. The literature is disparate and lacks cohesion, clarity, and, in some cases, depth. Subsequently, this scoping review aims to synthesize and critically reflect on the research on responsible AI. Based on this synthesis, we developed a conceptual framework for responsible AI governance (defined through structural, relational, and procedural practices), its antecedents, and its effects. The framework serves as the foundation for developing an agenda for future research and critically reflects on the notion of responsible AI governance.

Although analytics have become a widespread practice, we still have minimal knowledge about how dashboards influence decision-makers and through what mechanisms they enhance decision making. In this study, we built on an experiment-based approach with mock-up visualizations and recruited 524 participants, who were divided into two groups (A and B) with variations in their visualizations. We found that the format, currency, and completeness of information indirectly affect decision making quality by reducing the perceived task complexity and enhancing information satisfaction. Our results contribute to a better understanding of the role of visual representation of information quality on dashboard visualizations.

The specifics of why and how network relationships influence digital transformations have not yet been fully understood. We address this gap by drawing on the relational view of organisations to conceptualise network relationships as the source of requisite external non-generic complementarities for the development of dynamic capabilities (absorptive capacity, integration effort and big data analytics) essential for effective digital transformation outcomes. We follow a positivist research design to test the proposed hypotheses by collecting survey responses from informants from 183 Australian healthcare organisations. The statistical findings indicate that social capital affects the digital transformation outcomes through full individual mediations of absorptive capacity (0.13*), integration effort (0.13*) and big data analytics capability (0.05*) and a full serial mediation (0.01*). This empirical evidence provides two significant advancements to both theory and practice: a) by linking development of dynamic capabilities required for digital transformations to external non-generic complementarities embedded in network relationships; and b) by delineating specific pathways through which the dynamic capabilities of absorptive capacity, integration effort and big data analytics influence digital transformation outcomes.

IT vendors’ promises are likely to meet sound skepticism from prospective clients. If a particular technology is in vogue and seen as a “hype,” clients are under pressure to buy the technology while, at the same time, skepticism about its claimed benefits might be reinforced. Finding a balance between optimism and skepticism is essential. In this qualitative case study, we examine how an oil and gas supplier company in Norway deals with different pressures when adopting and subsequently implementing digital twin (DT) technologies. DTs offer the promise of creating a digital representation of the physical assets that can keep production facilities operating efficiently and optimally and, as such, have been heralded as enabling the next frontier of productivity improvements. Our results reveal a set of different pressures promoting the decision to adopt the hyped technology. Yet, when descended to the local context, the hype status of DTs evokes multilevel perception segmentation that hype interpreters maneuver by building trust. Based on this analysis, we propose a framework of trust-building mechanisms that contribute to a more nuanced understanding of adoption of hyped technologies and enable practitioners to deal with hype-induced perception obstacles.

Artificial Intelligence (AI) has been argued to offer a myriad of improvements in how we work and live. The notion of AI comprises a wide-ranging set of technologies that allow individuals and organizations to integrate and analyze data and use that insight to improve or automate decision-making. While most attention has been placed on the positive aspects companies realize by the adoption by the adoption and use of AI, there is a growing concern around the negative and unintended consequences of such technologies. In this special issue we have made a call for research papers that help us explore the dark side of AI use. By adopting a dark side lens, we aimed to expand our understanding of how AI should be implemented in practice, and how to minimize or avoid negative outcomes. In this editorial, we build on the notion of responsible AI, to highlight the different ways in which AI can potentially produce unintended consequences, as well as to suggest alternative paths future IS research can follow to improve our knowledge about how to mitigate such occurrences. We further expand on dark side theorizing in order to uncover hidden assumptions of current literature as well as to propose other prominent themes that can guide future IS research on AI adoption and use.

A question of central importance for researchers and practitioners is how information technology (IT) can help firms survive and thrive in turbulent and constantly changing business environments. To address this issue, this study develops the idea that IT architecture flexibility helps sustain competitive performance by driving the formation of IT-enabled dynamic capabilities, and that IT governance decentralisation strengthens this relationship. IT architecture flexibility and IT governance decentralisation, therefore, develop complementary effects. We argue that IT-enabled dynamic capabilities are a core antecedent for competitive performance gains, particularly under uncertain external environmental conditions. Tests of the proposed model using survey data from 322 international firms support these ideas. Our research also shows that, under conditions of high environmental heterogeneity, the value of IT architecture flexibility and IT governance decentralisation is increased, while the impact of IT-enabled dynamic capabilities on competitive performance is amplified.

Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance.

Although big data analytics have been claimed to revolutionize the way firms operate and do business, there is a striking lack of knowledge about how organizations should adopt and routinize such technologies to support their strategic objectives. The aim of this research is to explore how different inertial forces during deployments of big data analytics hinder the emergence of dynamic capabilities. To do so, we follow a multiple-case study design approach of 27 European firms and examine the different forms of inertia that materialize during big data analytics diffusion. The findings contribute to the growing body of knowledge on how big data analytics can be leveraged effectively to enable and strengthen a firm’s dynamic capabilities. By disaggregating dynamic capabilities into the underlying capabilities of sensing, seizing and transforming, findings indicate that different combinations of organizational inertia including economic, political, socio-cognitive, negative psychology, and socio-technical hamper the formation of each type of capability.

The potential of big data analytics in enabling improvements in business processes has urged researchers and practitioners to understand if, and under what combination of conditions, such novel technologies can support the enactment and management of business processes. While there is much discussion around how big data analytics can impact a firm’s incremental and radical process innovation capabilities, we still know very little about what big data analytics resources firms must invest in to drive such outcomes. To explore this topic, we ground this study on a theory-driven conceptualisation of big data analytics based on the resource-based view (RBV) of the firm. Based on this conceptualisation, we examine the fit between the big data analytics resources that underpin the notion, and their interplay with organisational contextual factors in driving a firm’s incremental and radical process innovation capabilities. Survey data from 202 chief information officers and IT managers working in Norwegian firms are analysed by means of fuzzy set qualitative comparative analysis (fsQCA). Results show that under different combinations of contextual factors the significance of big data analytics resources varies, with specific configurations leading to high levels of incremental and radical process innovation capabilities.

The age of big data analytics is now here, with companies increasingly investing in big data initiatives to foster innovation and outperform competition. Nevertheless, while researchers and practitioners started to examine the shifts that these technologies entail and their overall business value, it is still unclear whether and under what conditions they drive innovation. To address this gap, this study draws on the resource-based view (RBV) of the firm and information governance theory to explore the interplay between a firm’s big data analytics capabilities (BDACs) and their information governance practices in shaping innovation capabilities. We argue that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship. To examine our research model, we analyzed survey data collected from 175 IT and business managers. Results from partial least squares structural equation modelling analysis reveal that BDACs have a positive and significant effect on both incremental and radical innovative capabilities. Our analysis also highlights the important role of information governance, as it positively moderates the relationship between BDAC’s and a firm’s radical innovative capability, while there is a nonsignificant moderating effect for incremental innovation capabilities. Finally, we examine the effect of environmental uncertainty conditions in our model and find that information governance and BDACs have amplified effects under conditions of high environmental dynamism.

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.