This paper, employing a deductive research methodology approach, explores the concept of social badging for organizations from employees’ rating perspective in social media job portals (SMJP). This research attempts to identify the impact of monetary and non-monetary benefits as the antecedents and to explore the moderating effect of management support, organizational culture, and employee-perceived firm image on social badging in SMJPs. Relying on the signaling and person-environment fit theories, this study analyses more than 100,000 employee reviews from social media using text analytics techniques. The findings highlight the essential organizational traits to enhance favorable social badging for organizations. This study also has practical implications by underlying the critical drivers for reputational branding in social media.

Modern information technology (IT) features aimed at helping users can also increase the complexity of IT. The impact of this emergent complexity on employee behavior remains unknown. Using the transactional theory of stress, we propose that people cope with IT complexity by cutting corners. An experimental study involving 130 data analysts revealed (1) data analytics tools’ complexity increases distress, (2) distress fully mediates the impact of data analytics tools’ complexity on cognitive dissonance, (3) cutting corners negatively moderates distress–cognitive dissonance relationship, and (4) cognitive dissonance reduces perceived decision quality. These findings illuminate how employees navigate challenges using modern, complex IT.

Unjust algorithmic recommendations can lead decision makers to discriminatory choices, risking harm to individuals or groups. This study addresses this concerning phenomenon and examines its implications. In an experimental study involving 122 managers, we found that algorithmic injustice causes discriminatory decisions without heightened guilt perception. Additionally, trust in data analytics outcomes moderates the impact of algorithmic injustice on discrimination and marginally influences the impact of discriminatory decision making on guilt perception. However, displacement of responsibility has no moderating effect on either relationship. These findings highlight the potential negative consequences of algorithmic decision making, showing a need for caution and awareness.

A meta-analysis consisting of 42 studies was conducted to investigate the relationship between big data analytics capability (BDAC) and firm performance, as well as the existence of potential contextual moderators, including performance type (global or operational), country of origin (western or eastern), and respondent type (managers or non-manager) on this relationship. The results of our analysis indicate that while performance type moderates the relationship between BDAC and firm performance in the hypothesized direction, country of origin moderates this relationship in the opposite direction, and respondent type shows no moderation effect. The theoretical and practical contributions, limitations of this meta-analysis, and suggestions for future research are explained.

If data analysts’ do not share the insights they obtain through the use of data analytics tools, firms may not be able to enhance the quality of their decisions. Therefore, it is critical to understand the factors that encourage data analysts to share their knowledge. In this study, we utilize the theory of planned behavior to understand whether the data analysts’ knowledge sharing beliefs form their behavior. We also utilize contingency and task-technology fit theories to understand the factors that influence the impact of data analysts’ knowledge sharing beliefs on their behaviors. Survey data collected from 226 data analysts U.S.-based companies and the findings showed that: data analysts’ knowledge sharing belief is formed by subjective norm and perception of control; task interdependency does not moderate the effect of data analysts’ knowledge sharing belief on their knowledge sharing behavior; analytical skills positively moderate this association; and tool comprehensiveness negatively moderates it.

The purpose of this paper is to test a model that captures the duality of big data in explaining decision-making quality in firms. Whereas existing literature has examined the impact of big data on firm decision-making quality, the findings have not been consistent. In this study, we examine key conditions that help translating the availability of big data into improvements in decision-making quality and hence resolve existing tensions. While the availability of big data affords data analytics use and indirectly contributes to increased decision-making quality, it also increases work stress and indirectly diminishes decision-making quality. We examine the model with data from 299 managers. The findings show that big data is a double-edged sword that can indirectly increase and reduce decision-making quality; and that these effects depend in part on the levels of autonomy and employee skills. The results were further validated and enriched with applicability checks.

Healthcare is an area that has benefitted from the developments in wearable device technology. Seniors, who usually suffer from multiple comorbidities, are among the target users of these devices, and research has shown potential health benefits for seniors when they use these devices effectively. However, the adoption rate of wearable devices is low, especially among seniors, preventing the full utilisation of their data in healthcare. In this study, we interviewed forty-four seniors across North America and collected data from their wearable devices to develop a theoretical affordance network-based model to explain seniors’ effective use of wearable devices. Our model indicates that despite the apparent simplicity of wearable devices, they have multiple affordances that help seniors achieve several goals, including activity monitoring, activity planning, and activity improvement. Furthermore, we identified factors that enable seniors to actualise the affordances of wearable devices and achieve their goals. The results of this study suggest a strong relationship between seniors’ mental and physical capabilities and their willingness to use and benefit from wearable devices. We join other researchers in their call for a contextual study on consumer technology use.

As firms are moving towards data-driven decision making, they are facing an emerging problem, namely, algorithmic bias. Accordingly, algorithmic systems can yield socially-biased outcomes, thereby compounding inequalities in the workplace and in society. This paper reviews, summarises, and synthesises the current literature related to algorithmic bias and makes recommendations for future information systems research. Our literature analysis shows that most studies have conceptually discussed the ethical, legal, and design implications of algorithmic bias, whereas only a limited number have empirically examined them. Moreover, the mechanisms through which technology-driven biases translate into decisions and behaviours have been largely overlooked. Based on the reviewed papers and drawing on theories such as the stimulus-organism-response theory and organisational justice theory, we identify and explicate eight important theoretical concepts and develop a research model depicting the relations between those concepts. The model proposes that algorithmic bias can affect fairness perceptions and technology-related behaviours such as machine-generated recommendation acceptance, algorithm appreciation, and system adoption. The model also proposes that contextual dimensions (i.e., individual, task, technology, organisational, and environmental) can influence the perceptual and behavioural manifestations of algorithmic bias. These propositions highlight the significant gap in the literature and provide a roadmap for future studies.

The number of firms that plan to invest in big data usage has been reduced as many of them are still trying to understand the necessary conditions needed to improve their performance through the processing and use of big data. In this study, we leverage the resource-based view to investigate the role of tools sophistication, big data utilization, and employee analytical skills in improving organizational performance. The research model is validated empirically from 140 senior IT professionals using survey data. The findings show that when firms process big data, organizational performance is at its highest when firms use sophisticated tools, while this is not the case when firms do not process big data. Furthermore, findings show that, interestingly, at the lower levels of employee analytical skills, there is no significant impact of big data utilization on organizational performance, suggesting important implications for theory and for the guidance of business action.