Cognitive Decision-Making Algorithms in Data-driven Retail Intelligence: Consumer Sentiments, Choices, and Shopping Behaviors

Abstract

The ongoing COVID-19 pandemic is upending our lives and the global economy in ways unimaginable until recently. While the overall impacts are still difficult to quantify, ramifications are sure to be felt for decades to come. Providing secure, reliable, and affordable resources for all without causing devastating environmental consequences is perhaps the greatest challenge of the 21st century. But the pandemic has significantly altered dynamics and changed priorities. How is this impacting the quest for sustainability? This paper analyzes these challenges by focusing on the plastic industry. There is no doubt that plastic has molded society in many ways that make our lives easier and safer, but it has also created a global environmental and sustainability crisis. To curb our addiction to plastic, the world had been trying to move away from virgin plastic, but the pandemic has turned an enemy into a much-needed ally. The following paper uses a mixed-methods approach of surveys, empirical analysis, and literature review to answer a fundamental question: How can we leverage the advantages of plastic without contributing to the world's environmental crisis? This dilemma poses a significant challenge but also opens an opportunity to address sustainability at a systemic level through circularity and the transition to low-carbon alternatives to petroleum-based plastics.

How do popularity cues drive impulse purchase in live streaming commerce? The moderating role of perceived power

“…On the basis of consumer segments, firms can ask some macro- and micro-influencers who charge considerably less to act as streamers to interact with high-power consumers, ultimately leading to greater reputation perception and more impulse purchase. By embracing the data-driven retail intelligence ( Klieštik et al, 2022 ), firms are able to maximize the effect of streamer popularity, optimizing the investment in streamers.…”

Predicting review helpfulness in the omnichannel retailing context: An elaboration likelihood model perspective

“…Future studies thus are encouraged to retest our research model by collecting the data from different omnichannel retailers in other countries to validate the generalizability of the present study. Finally, as our main research purpose is to examine the factors that affect review helpfulness in the omnichannel retailing context, we did not examine the impacts of factors, such as consumer engagement ( Kliestik et al, 2022b ), expectations ( Hopkins, 2022 ), purchasing habit ( Watson, 2022 ), and behavioral intentions ( Kliestik et al, 2022a ) on review helpfulness. Therefore, future studies are encouraged to explore the effects of these factors on review helpfulness in the omnichannel retailing environment.…”

The Impact of Data Vulnerability in Online Health Communities: An Institutional Assurance Perspective

“…Future studies could consider other possible mitigating factors in the context of OHCs from another perspective. In addition, future research could explore the effects of data vulnerability in other research contexts, especially in relation to data-driven machine learning algorithms (e.g., Kliestik et al, 2022a , b ) and cognitive decision-making algorithms (e.g., Andronie et al, 2021 ; Pelau et al, 2021 ), which involve a large amount of personal data.…”