Purpose The global business environment combined with increasing societal expectations of sustainable business practices challenges firms with a host of emerging risk factors. As such, firms seek to increase supply chain transparency, enabling them to monitor operational activities and manage supply chain risks. Drawing on organizational information processing theory, the purpose of this paper is to examine how supply chain analytics (SCA) capabilities support operational supply chain transparency. Design/methodology/approach Using data from 477 survey participants, hypotheses are tested using seemingly unrelated regression. Findings The results reveal that: analytics capability in support of planning functions indirectly affects organizational supply chain transparency (OSCT) via SCA capabilities in source, make, and deliver functions; SCA capabilities in source, make, and deliver positively influence OSCT; and supply uncertainty moderates the relationship between SCA capabilities in make and OSCT. Research limitations/implications This research suffers from limitations inherent in all survey-based research. Nonetheless, the authors found convincing evidence that suggests firms can employ SCA capabilities to meet transparency requirements. Practical implications The findings inform design of SCA systems, noting the importance of linking planning tools with tools that support source, make, and deliver functions. The research also shows how transparency can be increased via employing SCA capabilities. Originality/value This is one of first studies to empirically demonstrate that SCA capabilities can be used to increase supply chain transparency. The research also advances organizational information processing theory by illustrating an analytics capability paradox, where increased levels of certain analytics capabilities can become counterproductive in the face of supplier uncertainty.

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As business analytics (BA) applications permeate across various industry sectors, the workforce needs to be trained and upskilled to meet the challenges of understanding and implementing analytics methodologies. To achieve payoffs from the resource investments in BA training, it is critical for enterprises to understand an individual's learning behavior along with the process and outcome-centric satisfaction associated with a collaborative analytics training task. This study focuses on identifying the factors that influence the process of learning during BA training to entry-level BA users. Drawing on the theories of situated cognition, goal setting, and flow, we propose a model that explains how trainees in a group learn through a process that is influenced by the characteristics of BA training context through context authenticity, the traits of trainees through task motivation and preference towards teamwork. Using an experimental design built on data collection and a unique task of real visits to a historic cemetery, we found that context authenticity and task motivation have significant impact on focused immersion, which in turn significantly impacts process and outcome satisfaction for learning an analytics task. Results of this study extend and validate the theories of situated cognition, goal setting, and flow within the context of business analytics training. Based on these findings, we provide recommendations for practitioners for designing effective analytics tasks for better training outcomes.

PurposeStock price reactions have often been used to evaluate the cost of data breaches in the current information systems (IS) security literature. To further this line of research, this study examines the impact of data breaches on stock returns, information asymmetry and unsystematic firm risk in the context of COVID-19.Design/methodology/approachThis paper employs an event study methodology and examines data breach events released in public databases, spanning pre- and post-COVID settings. This study investigated 283 data breaches of the US publicly traded firms, and the economic cost was measured by cumulative abnormal returns (CARs), trading volume, bid-ask spread and unsystematic risk.FindingsThe authors observe that data breaches during the COVID pandemic make investors react more negatively to data breach announcements, as reflected in the significantly negative difference in CARs between breached firms before COVID and those after COVID. The findings also indicate that, after the disclosure of data breach incidents, information asymmetry is reduced to a lesser extent compared with that in the pre-COVID setting. The authors also find that data breach events lead to an increase in the unsystematic risk of breached companies in the pre-COVID era but no change in the post-COVID era.Originality/valueThis study is the first effort to examine the economic consequences of data breaches by investigating the effects in the form of trading activities and risk measurement in the COVID setting.