Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with a significant challenge: how do we understand the decisions suggested by these systems in order that we can trust them? In this report, we focus specifically on data-driven methods—machine learning (ML) and pattern recognition models in particular—so as to survey and distill the results and observations from the literature. The purpose of this report can be especially appreciated by noting that ML models are increasingly deployed in a wide range of businesses. However, with the increasing prevalence and complexity of methods, business stakeholders in the very least have a growing number of concerns about the drawbacks of models, data-specific biases, and so on. Analogously, data science practitioners are often not aware about approaches emerging from the academic literature or may struggle to appreciate the differences between different methods, so end up using industry standards such as SHAP. Here, we have undertaken a survey to help industry practitioners (but also data scientists more broadly) understand the field of explainable machine learning better and apply the right tools. Our latter sections build a narrative around a putative data scientist, and discuss how she might go about explaining her models by asking the right questions. From an organization viewpoint, after motivating the area broadly, we discuss the main developments, including the principles that allow us to study transparent models vs. opaque models, as well as model-specific or model-agnostic post-hoc explainability approaches. We also briefly reflect on deep learning models, and conclude with a discussion about future research directions.

\n The purpose of this study is to distinguish between cognitive, affective and evaluative components of social identity in the organization and to show how the components instigate behaviours that benefit in‐group members. A new scale for measuring cognitive organizational identification (i.e. self‐categorization) is developed and compared to a leading scale. Internal consistency, convergent validity, predictive validity and generalizability of the two scales are established on a sample of Italian (\n N\n =409) and Korean (\n N\n =283) workers. Next, convergent and discriminant validity for measures of organizational identification, affective commitment and group self‐esteem are demonstrated. Then, two antecedents of these components of social identity are examined: organization prestige and organization stereotypes. Finally, the mediating role of the components of social identity are investigated between the antecedents and five forms of citizenship behaviours. The last three analyses are performed on the Italian (\n N\n =409) workers. Among other findings, the results show that affective commitment and self‐esteem are the primary motivators of citizenship behaviours. Moreover, cognitive identification performs as a central mediator between prestige and stereotypes on the one hand, and affective commitment and self‐esteem on the other. Identification is thus an indirect determinant of citizenship behaviours.\n

Artificial intelligence (AI) integration in the workplace yields positive outcomes, yet its impact on employees remains incompletely understood. This study aims to examine employee viewpoints regarding AI and its influence on employee career attitudes, behaviors and skill enhancement. The author examines how employees perceive AI and its impact on their career adaptability within the context of career self-management.

In the context of knowledge-based economy, an organisation’s ability to create knowledge is the most important factor in maintaining its competitiveness. Collective intelligence is posited as a paramount methodology not only for the generation of knowledge pertaining to multifaceted issues but also as a fundamental pillar within the framework of organizational knowledge management. The use of emerging technologies is an important strategy for improving the organisation’s ability to create knowledge through collective intelligence by adding depth and breadth of knowledge. However, excessive use of technology often has a negative impact on organisational knowledge management. Therefore, this study aims to identify the two-sided effect of using emerging technologies (big data analytics (BDA) and online platforms) on organisational knowledge creation according to the complexity of the task. The results of our study suggest that the use of BDA technology for organisational knowledge creation should be maintained at an appropriate level in general, but it is recommended to increase the use of BDA technology for low-complexity tasks. In addition, using online platform technology is difficult to consider as a strategic way to solve high-complexity tasks, but increasing the use of BDA technology can contribute to improving the organisation’s ability to create knowledge.

Selecting the appropriate display format for time-constrained tasks is the focus of the research presented in this paper. The effect of time stress on operator performance was assessed by manipulating the time available to process the display. Twenty people were trained as operators and instructed to identify the state of a system using either a digital display or a polygon display. Participants were required to reach a prespecified criterion in training and were then tested under time-constrained conditions. Time constraints were set at 100%, 50%, and 25% of each person's mean unpaced response times obtained during training. Results showed that response to the time constrained conditions was significantly affected by uncertainty and the type of display format. Discussion focuses on the effects of time stress on performance and the selection of displays for time-constrained tasks.

As individuals' relationships with information technology (IT) grow more complex and personal, our understanding of the problem of resistance to IT continues to evolve. Current approaches to resistance are based on perceived threats to work tasks and social structure. This work enhances our understanding of resistance by developing a definition and measure of the IT Identity Threat, a new construct that integrates social, task-related, and personal factors of resistance. Grounded in identity theory, the IT Identity Threat offers a parsimonious means to explain and predict IT resistance behaviors. Using data from two independent studies conducted among students and faculty at a large university in the Southeastern United States, we validate an operational measure of IT Identity Threat as a second-order construct and demonstrate that it successfully predicts resistance to IT. Our findings provide support for the IT Identity Threat construct as a simple mechanism to study resistance to IT.

The visualization of information is a widely used tool to improve comprehension and, ultimately, decision-making in strategic management decisions as well as in a diverse array of other domains. Across social science research, many findings have supported this rationale. However, empirical results vary significantly in terms of the variables and mechanisms studied as well as their resulting conclusion. Despite the ubiquity of information visualization with modern software, there is little effort to create a comprehensive understanding of the powers and limitations of its use. The purpose of this article is therefore to review, systematize, and integrate extant research on the effects of information visualization on decision-making and to provide a future research agenda with a particular focus on the context of strategic management decisions. The study shows that information visualization can improve decision quality as well as speed, with more mixed effects on other variables, for instance, decision confidence. Several moderators such as user and task characteristics have been investigated as part of this interaction, along with cognitive aspects as mediating processes. The article presents integrative insights based on research spanning multiple domains across the social and information sciences and provides impulses for prospective applications in the realm of managerial decision-making.

A comprehensive summary of the evidence for the validity and reliability of the Vocational Identity scale is provided for the period 1980-1992. The authors conclude that the Identity scale has substantial construct validity and retest reliability. Contrary to earlier analyses, a recent large scale factor analysis strongly suggests that the scale is best represented by a single factor.

Despite widely highlighting that creative individuals need to be full of vitality to function optimally, previous research neglects the very real possibility that human beings may also need to proactively manage their vitality to ignite creativity. Drawing on the conservation of resources theory, this study explores the impact of proactive vitality management on undergraduates’ creativity through harmonious academic passion, as well as the moderating roles of university creative climate and prevention focus. Evidence from a scenario-based experiment (Study 1) and a multi-wave field survey (Study 2) demonstrated that proactive vitality management positively promoted individual creativity. This relationship was partially mediated by harmonious academic passion. In addition, proactive vitality management enhanced undergraduate students’ creativity via harmonious academic passion in a high university creative climate, whereas the indirect effect was weak when prevention focus was high. Theoretical and practical implications are also discussed, along with study limitations and future research directions.

Since its introduction more than four decades ago, threat-rigidity theory has emerged as a popular managerial theory of threat response used in a wide variety of literature streams. The theory explains that individuals, groups, and organizations revert to familiar responses (i.e., rigidity) in navigating threats, even when doing so may not be ideal. Yet, despite its popularity, fidelity to the theory’s assumptions and core arguments have been missing, and development of the theory has been limited. As organizations continue facing new and unique threats (e.g., advancing technologies, economic downturns, supply chain disruptions, global health crises), a review and synthesis of threat-rigidity theory is necessary to drive new knowledge and allow for better understanding of the conditions around and appropriateness of rigidity. Our assessment of the literature reveals several gaps to address, which inform three primary directions for future research. We encourage future scholars to (a) clarify the nature of threats that elicit rigid responses, (b) explore the contextual factors and boundary conditions of the theory, and (c) utilize advanced methodological approaches to examine rigidity effects and outcomes across levels and over time. We provide guidance and sample research questions in each of the proposed directions for scholars to use in future efforts to refine or enhance threat-rigidity theory.

The study explores uses of algorithmic techniques in journalists’ working environments and investigates newsroom managers’ negotiations of automation as innovation process aimed at ensuring partial or full replacement of human labour with technology. Drawing from 15 qualitative interviews with representatives of newsroom management from legacy news institutions in the United Kingdom, Germany and the United States, the study analyses their (cl)aims to maintain the newsroom as a stable, but dynamic working environment and reveals three dualist propositions when negotiating automation novelties – human journalistic agency stands in contrast to technology, skills are separated from newsworkers, and the creation of news contrasts with its presentation. The results show the interviewees re-articulate the dominance of human agency over technology, re-establish technological innovations as liberating newsworkers rather than subordinating them, and standardise news by re-evaluating the concept as both a civic bond and a commodity. Such considerations are detached from recent concerns about automation of human labour and closer to what we call algorithmic sublime, maintaining the newsroom management’s loyalty to both the professional values of journalism and the corporate goals of management.

Artificial intelligence (AI) is being increasingly integrated into enterprises to foster collaboration within humanmachine teams and assist employees with work-related tasks. However, introducing AI may negatively impact employees’ identifications with their jobs as AI is expected to fundamentally change workplaces and professions, feeding into individuals’ fears of being replaced. To broaden the understanding of the AI identity threat, the findings of this study reveal three central predictors for AI identity threat in the workplace: changes to work, loss of status position, and AI identity predicting AI identity threat in the workplace. This study enriches information systems literature by extending our understanding of collaboration with AI in the workplace to drive future research in this field. Researchers and practitioners understand the implications of employees’ identity when collaborating with AI and comprehend which factors are relevant when introducing AI in the workplace.

This paper aims to provide a comprehensive review of the literature examining the relationship between automation and employment, with a focus on understanding the debates of automation displacement and enablement, and the mediating role of employee augmentation in driving organisational productivity.

Modern organizations frequently expect their employees to exhibit high creativity while facing considerable job insecurity. Although it is often assumed that job insecurity diminishes individual creativity, the empirical findings on this relationship are not consistent. The present study aims to scrutinize this assumption and to address such inconsistencies. To do so, we cast both job insecurity and creativity as multidimensional constructs, and we examine key mechanisms and boundary conditions of their linkages. Results from both a longitudinal (Study 1) and a time-lagged multisource survey study (Study 2) illustrated a negative indirect relation between quantitative job insecurity and radical creativity via diminished career commitment. For qualitative job insecurity, by contrast, there was a negative indirect relation with incremental creativity via reduced organizational identification. Moreover, Study 2 revealed distinct moderators for these associations. Employees’ turnover risk propensity buffered the indirect connection between quantitative job insecurity and radical creativity, whereas Confucian traditionality mitigated the indirect relation between qualitative job insecurity and incremental creativity. These findings shed new light on the important role of job insecurity for individual employees’ creative performance and advance our theoretical understanding of the complexities underlying these linkages.

Transparency is a design principle intended to make the inner workings of autonomous agents visible to end-users such that humans can evaluate the reasoning behind its decisions and actions. To test the effect of agent transparency on situation awareness, mental workload, and task performance, an experiment was performed where 34 nautical navigators were tasked with interpreting the information provided by an autonomous collision and grounding avoidance system. Sixteen traffic situations were created with two levels of complexity. Four levels of transparency varied the amount and type of information in terms of the system’s decisions, planned actions, reasoning, and input parameters. The results show that increased transparency improves SA without increasing mental workload. However, the time to comprehend the system’s decisions and planned actions increased when its reasoning was depicted. Traffic complexity impaired SA, mental workload, and time-to-comprehension regardless of transparency level. However, for level 2 SA, transparency was found to negate the influence of complexity, resulting in improved comprehension of the agent’s reasoning despite high traffic complexity. These outcomes demonstrate the merits of agent transparency as a design principle in supporting human supervision of autonomous agents. However, developers should take care when extending these principles to time-critical applications.

In this article, we discuss how ‘action research’ as an experiential research approach allows us to address challenges encountered in researching a converged and digital media landscape. We draw on our experiences as researchers, co-developers and marketeers in the European Union-funded Innovation Action project ‘INnovative Journalism: Enhanced Creativity Tools’ (INJECT) aimed at developing a technological tool for journalism. In this media innovation process, as in other media practices, longstanding delineations no longer hold, due to converging professional disciplines and blurring roles of users and producers. First, we discuss four features of innovation in the current ‘digital’ media landscape that come with specific methodological requirements: (a) the iterative nature of innovation; (b) converged practices, professions and roles; (c) the dispersed geographic nature of media production and innovation processes and (d) the impact of human and non-human actors. We suggest action research as a possible answer to these requirements of the digital media landscape. Drawing on our experiences in the INJECT project, we illustrate how adopting an action research approach provides insight into the non-linear, iterative and converged character of innovation processes by highlighting: (a) how innovation happens at various moments, in various places and by various people; (b) how perceptions and enactments of professions change over time and (c) how roles are (re)combined and expanded in such a way that clear delineation is not easy. Ultimately, we argue that experiencing convergence through action research enables us to do justice to the complexity of the current media landscape.

Contemporary decision support systems are increasingly relying on artificial intelligence technology such as machine learning algorithms to form intelligent systems. These systems have human-like decision capacity for selected applications based on a decision rationale which cannot be looked-up conveniently and constitutes a black box. As a consequence, acceptance by end-users remains somewhat hesitant. While lacking transparency has been said to hinder trust and enforce aversion towards these systems, studies that connect user trust to transparency and subsequently acceptance are scarce. In response, our research is concerned with the development of a theoretical model that explains end-user acceptance of intelligent systems. We utilize the unified theory of acceptance and use in information technology as well as explanation theory and related theories on initial trust and user trust in information systems. The proposed model is tested in an industrial maintenance workplace scenario using maintenance experts as participants to represent the user group. Results show that acceptance is performance-driven at first sight. However, transparency plays an important indirect role in regulating trust and the perception of performance.

In the contemporary media environment, media managers have been forced to reassess everything from editorial workflows to business models to technological platforms. Amid such challenges, legacy news media are encouraged to innovate. Contemporary scholarly literature on media innovation typically adopts a relatively narrow approach when defining and studying the agents involved in shaping media innovations. Ultimately, many studies focus on individual parts of the organization rather than the complete system. There is thus a need to theorize and conceptualize the agents of media innovations in order to understand and improve activities of media innovations. This article presents the AMI approach (Agents of Media Innovations) as a holistic theoretical construct for understanding the agents of media innovation activities. It conceptualizes this approach through a systematic discussion of four interlinked factors: actors, actants, audiences, and activities. These are used to compose and outline what we call the 4A Matrix, encompassing seven distinct and typological ways in which actors, actants and audiences might intersect in the activities of media innovation. In mapping this interplay, the 4A Matrix serves as a heuristic for the scholarly study of media innovations, as well as a conceptual tool for envisioning, at a practical level, how media managers might act strategically.