Input control and its signalling effects for complementors' intention to join digital platforms

2023 | Information Systems Journal | Citations: 2

Authors: Adam, Martin; Croitor, Evgheni; Werner, Dominick; Benlian, Alexander; Wiener, Martin

Abstract: Existing information systems (IS) research on platform control has largely focus ... Expand

Abstract: Existing information systems (IS) research on platform control has largely focused on examining how input control (i.e., the mechanisms used to control platform access) affects complementors' intentions and behaviours after their decision to join a digital platform. Yet, our understanding of how input control is perceived before this decision and how such perceptions influence prospective complementors' intention to join a platform is still nascent. In this regard, our study views input control as a salient signal that shapes prospective complementors' expected benefits and costs (i.e., their performance and effort expectancy), and ultimately their decision to join a digital platform. Drawing on signalling theory and the antecedent-benefit-cost (ABC) framework, we conducted a randomized online experiment in the context of donation-based crowdfunding. The experiment results offer empirical support for this view by showing that input control has distinct and complex signalling effects for prospective complementors. In particular, our findings reveal curvilinear and competing signalling effects, with perceived input control increasing both performance expectancy (at a decreasing rate) and effort expectancy (at an increasing rate). Also, we find that performance expectancy linearly increases prospective complementors' intention to join a platform, whereas effort expectancy linearly decreases their intention to do so. These findings imply that the overall relationship between perceived input control and intention to join follows an inverted U-shape curve, which means that neither a low nor a high, but a moderate degree of perceived input control maximizes prospective complementors' intention to join. In sum, the results of our study provide novel and important insights into the signalling role that perceived input control plays in shaping prospective complementors' decision to join a digital platform. Collapse

Topics: digital platform crowdsourcing crowdfunding behavioral intention digital ecosystem

Methods: experiment mediation analysis survey survey design theory development

Theories: signaling theory

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

Differential Impact of Content in Online Communication on Heterogeneous Candidates: A Field Study in Technical Recruitment

2023 | Information Systems Research | Citations: 0

Authors: Aggarwal, Rohit; Lee, Michael J.; Midha, Vishal

Abstract: Recruitment is a critical activity for companies, and the research community has ... Expand

Abstract: Recruitment is a critical activity for companies, and the research community has also shown significant interest in examining technology recruitment. Companies often communicate how they value their employees along with job requirements to potential candidates in a bid to attract them. However, there is an overall lack of understanding of how candidates react to such information and how their motivation toward the job changes with such online communication. Although there is substantial work that examines the decision-making process of managers who do technical hiring, to the best of our knowledge, there is a paucity of work that investigates the decision-making process of technical candidates. The broad research question studied is how including certain content in online communication about a technical job opportunity may (de)motivate heterogeneous candidates differently in applying for the job. We theorize the underlying process of how including certain content in online job communication may influence candidates’ propensity to apply and their minimum acceptable salary increase if they were to join the company. Additionally, we capture mediating variables that influence the effect of different online content on candidates’ propensity to apply and on candidates’ minimum acceptable salary increase. By testing actual job application behavior in a field study, we confirmed the content that can attract high performers while discouraging low performers from applying.History: Yong Tan, Senior Editor; Yan Huang, Associate Editor.Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1120. Collapse

Topics: social media electronic mail IS economics economic surplus IT workforce

Methods: experimental group survey field study factor analysis descriptive statistic

Theories: motivation theory

An Empirical Investigation on Business Analytics in Software and Systems Development Projects

2023 | Information Systems Frontiers | Citations: 0

Authors: Ahmad, Muhammad Ovais; Ahmad, Iftikhar; Rana, Nripendra P.; Khan, Iqra Sadaf

Abstract: To create competitive advantages, companies are leaning towards business analyti ... Expand

Abstract: To create competitive advantages, companies are leaning towards business analytics (BA) to make data-driven decisions. Nevertheless, users acceptance and effective usage of BA is a key element for its success. Around the globe, organizations are increasingly adopting BA, however, a paucity of research on examining the drivers of BA adoption and its continuance is noticeable in the literature. This is evident in developing countries where a higher number of systems and software development projects are outsourced. This is the first study to examine BA continuance in the context of software and systems development projects from the perspective of Pakistani software professionals. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data were analyzed using partial least squares - structural equation modelling techniques. Our structural model explains 45% variance on BA continuance intention, 69% variance on technological compatibility, and 59% variance on perceived usefulness. Our results show that confirmation has a direct impact on BA continuance intention in software and systems projects. The study has both theoretical and practical implications for professionals in the field of business analytics. Collapse

Topics: perceived usefulness IT development project IT project systems development IT workforce

Methods: survey partial least squares path modeling machine learning conceptual modelling data transformation

Theories: expectation confirmation theory

Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare

2023 | Information Systems Frontiers | Citations: 1

Authors: Al-Dhaen, Fatema; Hou, Jiachen; Rana, Nripendra P.; Weerakkody, Vishanth

Abstract: This paper examines the continuous intention by healthcare professionals to use ... Expand

Abstract: This paper examines the continuous intention by healthcare professionals to use the Internet of Medical Things (IoMT) in combination with responsible artificial intelligence (AI). Using the theory of Diffusion of Innovation (DOI), a model was developed to determine the continuous intention to use IoMT taking into account the risks and complexity involved in using AI. Data was gathered from 276 healthcare professionals through a survey questionnaire across hospitals in Bahrain. Empirical outcomes reveal nine significant relationships amongst the constructs. The findings show that despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT. This study advances the understanding of the role of responsible AI in the continued use of IoMT in healthcare and extends DOI to address the diffusion of two innovations concurrently. Collapse

Topics: usage intention artificial intelligence responsible artificial intelligence innovation management online learning system

Methods: SEM tool survey descriptive statistic structural equation modeling survey design

Too Tired and in Too Good of a Mood to Worry About Privacy: Explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing

2023 | Information Systems Research | Citations: 0

Authors: Alashoor, Tawfiq; Keil, Mark; Smith, H. Jeff; McConnell, Allen R.

Abstract: The confluence of digital transactions, growing cybersecurity threats, and the i ... Expand

Abstract: The confluence of digital transactions, growing cybersecurity threats, and the internet of the future (e.g., web 3.0 and the metaverse) have made information privacy increasingly important to consumers and companies that rely on consumers willingly sharing their personal information. Although information privacy has been of interest to researchers for decades and much has been learned, one thing that perplexes scholars is the privacy paradox, which we define as a mismatch between stated privacy concerns and actual disclosure behaviors. In this paper, we shed light on this phenomenon and show that low-effort information processing triggered by cognitive depletion (Experiment 1), positive mood (Experiment 2), or both (Experiment 3) significantly attenuates the association between stated privacy concerns and disclosure behaviors. These findings do not indicate that individuals do not care about privacy because we find consistent evidence in the three experiments for a significant negative association between stated privacy concerns and disclosure behaviors when individuals have sufficient cognitive capacity (Experiment 1), experience a negative (or neutral) mood (Experiment 2), or have sufficient cognitive capacity coupled with a negative mood state (Experiment 3). Our findings reveal that the paradox is neither an absolute phenomenon nor a myth, but its existence is conditional on contextual factors, including psychological factors related to information processing. We discuss our contribution to privacy theory and provide implications for consumers, companies, and policymakers. History: Alessandro Acquisti, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1182. Collapse

Topics: information privacy concern privacy decision making mobile application task productivity

Methods: experiment descriptive statistic statistical hypothesis test theory development Student's t-test

Theories: elaboration likelihood model

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

D&L: A Natural Language Processing Based Approach for Protecting Sensitive Information from Shoulder Surfing Attacks

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Aldossari, Marran; Zhang, Dongsong

Abstract: Despite the increasing attention and research effort, how to protect sensitive ... Expand

Abstract: Despite the increasing attention and research effort, how to protect sensitive information from shoulder surfing attacks is still under studied. Existing methods for protecting sensitive textual content on users' screens from shoulder surfing attacks have various limitations, including ineffectiveness, insufficient protection of sensitive information, low usability, and high cognitive workload. To address those limitations, this paper proposes, develops, and evaluates a new solution called "detection and labeling" (D&L), which uses NLP techniques to automatically detect and label sensitive information in the textual content. The labeled and hidden sensitive information is then read to users through their headphones upon their clicking a label. Evaluation results demonstrate that D&L improves protection, enhances usability, reduces users' cognitive workload, and allows faster browsing speed compared to the baseline methods. Collapse

Topics: electronic mail laptop computer usability privacy perceived ease of use

Methods: survey parametric test experiment Tukey HSD for post poc analysis ANOVA

Empirical Assessment of Big Data Technology Adoption Factors for Organizations with Data Storage Systems

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Alnafoosi, Ahmad; Steinbach, Theresa A.

Abstract: Many organizations have on-premises data storage systems that are evolving; amo ... Expand

Abstract: Many organizations have on-premises data storage systems that are evolving; among them is Big Data (BD). BD is a data storage system that can analyze large volumes, with velocity, and a variety of data. Per the Economist, data is now the most valuable resource. BD holds the promise of unlocking substantial parts. Many organizations want to implement BD but have not been able to. There is a need to identify key factors affecting adoptions for such organizations. The following factors are identified as (novel): data storage latency, ability to compute, data storage interface compatibilities, open-source software, and enterprisesourced software, or (under-studied): cost, perceived industry pressure, legislation barriers, and market turbulence. These factors were studied empirically with DOI and TOE by conducting a survey and performing quantitative PLS-SEM analysis. This analysis showed that these adoption factors are significant: data storage interface compatibility, open-source, enterprise-source software, cost, industry pressure, and market turbulence. Collapse

Topics: computer data storage decision support system legal context open source social media

Methods: survey partial least squares path modeling partial least squares regression PLS tool survey design