Online Labor Platforms and the Role of Job Security and Compensation (Mis)Fits for Gig Workers

2023 | International Conference on Information Systems | Citations: 1

Authors: Adam, Martin; Wiener, Martin; Benlian, Alexander

Abstract: Online labor platforms (OLPs) use algorithms to manage their workers. Prior res ... Expand

Abstract: Online labor platforms (OLPs) use algorithms to manage their workers. Prior research has examined how OLP management shapes workers' attitudes, implicitly assuming that workers have the same preferences. However, research has largely neglected the role of (mis)fits between what workers need and what they receive from OLPs. Therefore, we conducted a survey with 121 Mechanical Turk workers and used polynomial regression analysis of workers' actual needs and perceived OLP supplies. Our results suggest that higher job security fit decreases perceived exploitation and increases system satisfaction, whereas higher compensation fit decreases perceived exploitation. In addition, we find that job security misfit has decreasing marginal effects on perceived exploitation and increasing marginal effects on system satisfaction. Overall, we contribute to a more nuanced understanding of workers' reactions to the design of OLPs by shedding light on the interplay between what gig workers need and what they perceive to receive from such platforms. Collapse

Topics: IS satisfaction platform worker algorithmic management digital platform ride sharing platform

Methods: survey regression analysis method cross sectional research polynomial regression theoretical contribution

Remote Workers’ Privacy Concerns, Psychological Climate for Face Time, and Organizational Affective Commitment

2023 | HICSS | Citations: 0

Authors: Afota, Marie-Colombe; Cañibano, Almudena; Ollier-Malaterre, Ariane; Provost Savard, Yanick; Léon, Emmanuelle

Abstract: During the Covid-19 pandemic, the shift to high-intensity remote work—three days ... Expand

Abstract: During the Covid-19 pandemic, the shift to high-intensity remote work—three days or more a week—accelerated the digitalization of work processes on platforms such as Slack or Teams and the blurring of boundaries between work and personal life through videoconferencing and the use of personal devices for work. This paper explores the relationships between high-intensity remote workers’ information and communication technologies (ICT) privacy concerns, psychological climate for face time, and organizational affective commitment. Building on organizational support and social information processing theories, we argue that ICT privacy concerns and perceptions that an organization values physical presence in-office may undermine commitment to the organization. Based on a two-wave study of 1065 remote workers in a large multinational bank, we find that ICT privacy concerns and psychological climate for face time reinforce one another and are negatively associated with subsequent affective organizational commitment. Collapse

Topics: information privacy concern organizational commitment remote work IT supported collaboration privacy

Methods: statistical hypothesis test chi squared test longitudinal research SEM tool structural equation modeling

Theories: social information processing theory

Optimal Decision Rules for the Discursive Dilemma

2023 | Group Decision and Negotiation | Citations: 0

Authors: Alabert, Aureli; Farré, Mercè; Montes, Rubén

Abstract: We consider the classical problem in truth-tracking judgment aggregation of a co ... Expand

Abstract: We consider the classical problem in truth-tracking judgment aggregation of a conjunctive agenda with two premisses and one conclusion. We study this problem from the point of view of finding the best decision rule according to a quantitative criterion, under very mild restrictions on the set of admissible rules. The members of the deciding committee are assumed to have a certain probability to assess correctly the truth or falsity of the premisses, and the best rule is the one that minimises a combination of the probabilities of false positives and false negatives on the conclusion. Collapse

Topics: Python anonymity

Methods: machine learning statistical hypothesis test

Accepting the Familiar: The Effect of Perceived Similarity with AI Agents on Intention to Use and the Mediating Effect of IT Identity

2023 | HICSS | Citations: 0

Authors: Alawi, N.; De Vreede, T.; De Vreede, G.

Abstract: With the rise and integration of AI technologies within organizations, our unde ... Expand

Abstract: With the rise and integration of AI technologies within organizations, our understanding of the impact of this technology on individuals remains limited. Although the IS use literature provides important guidance for organization to increase employees' willingness to work with new technology, the utilitarian view of prior IS use research limits its application considering the new evolving social interaction between humans and AI agents. We contribute to the IS use literature by implementing a social view to understand the impact of AI agents on an individual's perception and behavior. By focusing on the main design dimensions of AI agents, we propose a framework that utilizes social psychology theories to explain the impact of those design dimensions on individuals. Specifically, we build on Similarity Attraction Theory to propose an AI similarity-continuance model that aims to explain how similarity with AI agents influence individuals' IT identity and intention to continue working with it. Through an online brainstorming experiment, we found that similarity with AI agents indeed has a positive impact on IT identity and on the intention to continue working with the AI agent. Collapse

Topics: artificial intelligence usage intention behavioral intention brainstorming usability

Methods: chatbot experiment survey descriptive statistic statistical hypothesis test

Theories: social psychology theory

The Role of Followers and Followees in the Adoption of Innovations

2023 | HICSS | Citations: 0

Authors: Atlas, M.; Gavious, A.; Ravid, G.

Abstract: An online social network is a key platform through which innovation diffuses. T ... Expand

Abstract: An online social network is a key platform through which innovation diffuses. To learn about innovativeness, we simultaneously investigate two Twitter networks, the relationships network, following-follower relationships, and the activity network, the flow of tweets. Specifically, the innovativeness relations to the networks' indegree and outdegree, the volume of platform use, and the profile's age. The more active and central the user, the earlier the adoption. Innovativeness increases with the number of followers only when at least several of them adopt the innovation. Surprisingly, having more followees is linked to later engagement with the innovation. This association is mediated by the number of adopters' followees. Those who created a Twitter profile later are also more likely to adopt innovations later. This study is novel in distinguishing between the two networks and analyzing their interactions. Its contribution lies in identifying the innovativeness of users in an online social network platform diffusion. Collapse

Topics: Twitter mindfulness social network innovation management user acceptance

Methods: descriptive statistic regression analysis method

How commitment and platform adoption drive the e-commerce performance of SMEs: A mixed-method inquiry into e-commerce affordances

2023 | International Journal of Information Management | Citations: 0

Authors: Ballerini, Jacopo; Herhausen, Dennis; Ferraris, Alberto

Abstract: To cope with the digital transition exacerbated by the COVID-19 pandemic, manage ... Expand

Abstract: To cope with the digital transition exacerbated by the COVID-19 pandemic, managers of manufacturing small and medium enterprises (SMEs) need to adopt innovative practices to face uncertain scenarios and create longterm value, identified as transformational entrepreneurship practices. Among emerging digital technologies, digital platforms are shaping and outgrowing the e-commerce channel representing a potential opportunity for manufacturing SMEs to embrace digital transformation. Drawing on affordance theory, this research uses a mixed method approach to investigate how manufacturing SMEs’ e-commerce commitment and digital platform adoption stimulate the actualization of three e-commerce affordances: consumer knowledge generation, inter­ nationalization, and customer diversification. Based on survey responses from 165 manufacturing SME man­ agers, we find that direct selling through owned websites actualizes consumer knowledge generation and internationalization, indirect selling actualizes customer diversification and internationalization, and agency selling through third-party platforms actualizes all three affordances. The relationship between e-commerce commitment and ecommerce performance is mediated by consumer knowledge generation and internationali­ zation but not by customer diversification. A fsQCA analysis outlines seven configurations actualizing these ecommerce affordances by pairing different ecommerce approaches with degrees of e-commerce commitment. Finally, an analysis of open-ended questions from 24 respondents complements the study and deeply interprets the seven unique configurations outlined. Collapse

Topics: electronic commerce affordance user experience entrepreneurship usability

Methods: survey qualitative interview structural equation modeling data transformation personal interview

Theories: theory of affordance

Telepresence in live-stream shopping: An experimental study comparing Instagram and the metaverse

2023 | Electronic Markets | Citations: 0

Authors: Barta, Sergio; Gurrea, Raquel; Flavián, Carlos

Abstract: Advances in digital platforms allow influencers to use live shows to showcase pr ... Expand

Abstract: Advances in digital platforms allow influencers to use live shows to showcase products, which has given rise to live-stream shopping. Moreover, new communication forms have emerged, such as the metaverse. The social interactions that occur in these immersive environments foster influencer-follower interactions. This research compares the impact of live-stream shopping on followers’ experiences and behaviors on two communication channels (live shows on Instagram and in the metaverse) and analyses the moderating role of content involvement in telepresence. Data were collected in a between-subjects quasi-field experiment. Two scenarios that differed in degree of telepresence were created, a live show on Instagram and a live show in the metaverse. The results showed that telepresence is an important part of the user experience and in the success of live shows. Telepresence was seen to have a major influence on experiential value, but not on instrumental value. Both values provided followers with satisfaction with the experience. This satisfaction generated stickiness intention and intention to recommend the influencers’ live shows. The effect of telepresence on experiential value was greater for followers less involved with the influencer’s content. This research is pioneering in comparing an established platform, Instagram, with the metaverse. Despite the greater experiential value provided by the metaverse, to increase its instrumental value metaverse spaces needs to be further developed. Collapse

Topics: video conference virtual reality Instagram social network social media

Methods: survey field experiment statistical power analysis statistical hypothesis test experimental group

Theories: technology acceptance model

The double side of flow in regret and product returns: Maximizers versus satisficers

2023 | International Journal of Information Management | Citations: 0

Authors: Barta, Sergio; Gurrea, Raquel; Flavián, Carlos

Abstract: While the positive aspects of the flow state have been widely researched, the ef ... Expand

Abstract: While the positive aspects of the flow state have been widely researched, the effects of being aware of this state and the negative consequences still need to be explored. The loss of awareness that flow state may bring can lead to the purchase of products that consumers afterward regret and want to return them. This research aims to understand flow consciousness’s effect on consumer post-purchase regret from a dual perspective (process and outcome regret). The relevance of these relationships is also examined by analyzing the differences between 2 groups: maximizers and satisficers consumers. Through a structural equation model based on covariances tested with EQS 6.4, the results show that flow consciousness generates process regret but not outcome regret. How­ ever, the differences found between the two groups are worth noting. The research sheds light on the effect of flow consciousness on regret and provides insight into the mechanism of product return through post-purchase regret. The theoretical and managerial implications for e-commerce retailers are discussed. Collapse

Topics: consumer behavior frequency of use electronic commerce logistics management

Methods: survey structural equation modeling statistical hypothesis test descriptive statistic Student's t-test

Caught in an Expectations Trap: Risks of Giving Securities Analysts What They Expect

2023 | Organization Science | Citations: 0

Authors: Bascle, Guilhem; Jung, Jiwook

Abstract: Although recent research shows that there is mounting pressure on firms to achie ... Expand

Abstract: Although recent research shows that there is mounting pressure on firms to achieve earnings expectations of securities analysts, firms are far from being passive conformers; many firms proactively manage such pressure, particularly with earnings management tools. Yet why does the pressure to meet analyst expectations persist despite firms’ efforts to reduce it? To address the question, we develop an intertemporal model of the mutually reinforcing relationships between analyst expectations and firms’ strategic response, combining the behavioral theory of the firm and the concept of expectations trap. We argue that firms’ efforts to meet analyst expectations strengthen their salience as a predominant performance benchmark and, in doing so, ironically put them under greater pressure from analysts in three sequentially related steps—escalating future earnings expectations, imposing more severe penalties for failure to meet heightened expectations, and generating compensatory action for missed expectations. Our analysis, using data on more than 700 of the largest listed U.S. firms between 1986 and 2015, supports our arguments. Our study expands the scope of the behavioral theory of the firm, by demonstrating the increasing importance of performance feedback based on analyst expectations. Our study also contributes to the research on earnings pressure, by illuminating why the pressure persists despite firms’ efforts to reduce or evade it, and finally to the literature on strategic management of external expectations, by elaborating its unintended, long-term consequences. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2021.1569. Collapse

Topics: strategic management organizational productivity Generally Accepted Accounting Principles IS downsizing accounting

Methods: longitudinal research statistical hypothesis test Compustat descriptive statistic robustness check

Theories: behavioral theory of the firm institutional theory

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

2023 | Decision Support Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Heart, Tsipi; Klempfner, Robert; Padman, Rema

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of a ... Expand

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of adverse medical events is important considering the prevalence of vast repositories of patient-level medical data, and the quest to reduce healthcare costs while elevating the quality of care. Of particular interest is identifying a set of features that can better predict the risk of unplanned readmissions of chronically ill patients within 30 days post discharge, among them patients with Congestive Heart Failure (CHF). While numerous studies have employed ML algorithms to identify sets of significant features, only a handful have compared features extracted by various methods, and even fewer have compared the predictive power of feature sets selected by human experts to those selected by ML. Hence, this research aimed to elicit a comprehensive feature set selected by four ML algorithms and compare its predictive performance to that of a feature set selected by experts. We then evaluated a merged list of the two feature sets, constructing a human-machine collaborative set to predict the likelihood of 30-day unplanned readmission of CHF patients using data on 10,763 CHF patients hospitalized during the years 2010–2017. Our models achieved Area under the ROC Curve (AUC) above 0.8 and an Accuracy of ~0.89, comparable to the best models in the extant literature. Moreover, the Human-Machine Collaborative model significantly outperformed (best AUC 0.836) other human-only selected models as well as the machine-selected set. This study contributes to a better understanding of the power of using ML for patient risk stratification with special attention to the benefits of human-machine collaboration, even when these two entities work separately or in parallel, for improved clinical decision making. Collapse

Topics: healthcare data decision making big data missing data data warehouse

Methods: machine learning computational algorithm XGBoost descriptive statistic random forest classification