Exploring Parental Influence on Children's Cyber Hygiene

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Armeen, Inaiya; Yang, Ning; Abbaraju, Pranith

Abstract: In today's digital age, children are highly vulnerable to various cybersecurity ... Expand

Abstract: In today's digital age, children are highly vulnerable to various cybersecurity threats, highlighting the importance of instilling good cyber hygiene. While several factors contribute to children's cyber hygiene, parents in particular play a crucial role. This study examines how parents influence their children's cyber hygiene. Drawing on role model theory and parental mediation theory, we propose that parental factors, such as their cyber hygiene practices, guidance through open conversations about online safety, restrictions on device and internet usage, and digital safety measures, can positively impact their children's cyber hygiene. We plan to use a mixed-methods design that includes semi-structured interviews and surveys to test our hypotheses. The findings of this study will contribute to the literature on cyber hygiene and aid in developing effective strategies to promote good cyber hygiene among children. Collapse

Topics: information system use

Methods: mixed method survey personal interview qualitative interview conceptual modelling

Theories: psychological theory social learning theory

Influencer is the New Recommender: insights for Theorising Social Recommender Systems

2023 | Information Systems Frontiers | Citations: 0

Authors: Bawack, Ransome Epie; Bonhoure, Emilie

Abstract: Designing theory-driven social recommender systems (SRSs) has been a significant ... Expand

Abstract: Designing theory-driven social recommender systems (SRSs) has been a significant research challenge for over a decade. This study aims to identify behavioural factors that could improve the persuasiveness and quality of recommendations made by SRSs. Given both research streams’ striking similarity, it uses the recent yet rich research on social media influencers (SMI) to inform SRS research. Drawing on 72 publications, we classified 52 independent variables into 12 categories regrouped into three broad categories that characterise the relationships between the consumer and the (i) recommender system, (ii) product or brand, and (iii) and advert. The metanalysis results determined the relative importance of each category in predicting purchase intentions, placing recommender credibility and attitude towards the recommended product or brand at the top of the charts. Our findings are expected to facilitate more refined theory-building efforts and theory-driven designs in SRS research and practice. Collapse

Topics: advertising management database system Twitter TikTok IS discipline

Methods: literature study theory development computational algorithm descriptive statistic sensitivity analysis

Theories: social learning theory behavioral theory theory of planned behavior

Coparticipant effect in group buying: How coparticipant response speeds affect consumer postpurchase regret?

2023 | Decision Support Systems | Citations: 0

Authors: Guan, Mengcheng; Li, Jianbin; Zhang, Yicheng; Liu, Yi

Abstract: The introduction of consumer-initiated models with small group size has increase ... Expand

Abstract: The introduction of consumer-initiated models with small group size has increased the popularity of group buying. With lower size requirements for forming buying groups, more impulsive buying is facilitated while more postpurchase regret is spawned. Consumer postpurchase regret not only poses challenges to the coordination of group buying but may also eventually lead to merchant losses. Therefore, understanding the factors that may affect consumer postpurchase regret in group-buying contexts is crucial. Drawing on observational learning theory and expectancy disconfirmation theory, the present study focuses on how the response speeds of groupbuying campaign coparticipants intertwined with publicly available information of group-buying deals (i.e., the deal popularity and expected savings) to affect campaign initiators’ postpurchase regret. Through secondary data analysis and a laboratory experiment, we find that a lower coparticipant response speed is associated with a higher likelihood of the initiator experiencing postpurchase regret. In addition, when deals have a higher level of popularity, this effect is amplified. Our findings demonstrate the potential importance of considering the coparticipant effect to discourage negative consumer behavior in group-buying contexts. Collapse

Topics: mobile application consumer behavior digital product electronic commerce digital platform

Methods: survey laboratory experiment experiment descriptive statistic archival research

Theories: social learning theory

Introduction to the Minitrack on Innovative Behavioral IS Security and Privacy Research

2023 | HICSS | Citations: 0

Authors: Vance, Anthony; Warkentin, Merrill; Johnston, Allen

Abstract: ... Expand

Abstract: Collapse

Topics: IT security information security policy compliance cybersecurity behavior security policy personality

Theories: social learning theory

Security is Local: The Influence of the Immediate Workgroup on Information Security

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Wang, Dawei; Durcikova, Alexandra; Dennis, Alan

Abstract: Information security is a multilevel phenomenon with employee security decision ... Expand

Abstract: Information security is a multilevel phenomenon with employee security decisions being influenced by macrolevel factors (e.g., organizational policies), mesolevel factors (e.g., one's immediate workgroup-IW), and microlevel factors (e.g., individual personalities). We argue that an employee's local IW (i.e., immediate supervisor and coworkers) has a strong effect on security. This paper focuses on the effects of these mesolevel factors in the presence of macro-and microlevel factors. Drawing on the social structure and social learning framework as well as workgroup research, we hypothesize that the security behavior of an employee's IW supervisor and coworkers moderated by the nature of these relationships influences information security decisions. Our research, based on a sample of 217 full-time employees, reveals that the IW significantly affects security decisions, over and above the micro-and macrolevel factors. These effects are moderated by the nature of the relationship between employees and their IW supervisor (leader-member exchange) and coworkers (team-member exchange). A post hoc analysis shows that the mesolevel factors alone had the same explanatory power as the micro-and macrolevels combined. Our research suggests that future theory and research should include the IW and that organizations should share security responsibilities with line managers and help them understand their substantial impact on information security. Security training programs should ask employees about the behaviors of their IW supervisor and coworkers and, where needed, deliver anti-neutralization training to mitigate the effects of the IW's noncompliance behaviors.Alexandra Durcikova is an associate professor of MIS at the Price College of Business, University of Oklahoma. Her research focuses on knowledge management, knowledge repositories, and human compliance with security policy and characteristics of successful phishing attempts within the area of computer security, and health information systems. Collapse

Topics: data security IT security threat IT security password self efficacy

Methods: survey theory development descriptive statistic data transformation factor analysis

Theories: big five model social learning theory leader–member exchange theory

Deep Learning-Based Imputation Method to Enhance Crowdsourced Data on Online Business Directory Platforms for Improved Services

2023 | Journal of Management Information Systems | Citations: 0

Authors: Xu, Da; Hu, Paul Jen-Hwa; Fang, Xiao

Abstract: Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, ... Expand

Abstract: Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method's greater imputation effectiveness relative to prevalent methods. To illustrate the method's practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services. Collapse

Topics: missing data crowdsourcing data completeness wireless LAN healthcare data

Methods: data imputation autoencoder data transformation deep learning multivariate imputation with chained equations

Theories: institutional theory social learning theory

Effect of Market Information on Bidder Attrition in Online Auction Markets

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Ghasemkhani, Hossein; Goes, Paulo; Tripathi, Arvind

Abstract: Information generated in online markets can affect both buyers’ and sellers’ exp ... Expand

Abstract: Information generated in online markets can affect both buyers’ and sellers’ expectations and therefore their choices. In this research, we investigate the effect of market information, generated in online auction markets, on buyers’ expectations and choices. To clear large inventories, sellers often conduct many auctions selling identical items over time, which creates an online auction market where competition dynamics spill over from one to auction to another. In these markets, bidders can participate in many auctions over a period of time, observe market information (supply, demand, and competition), and gain experience to increase their payoffs. We observe that despite having an opportunity to compete and win in future auctions, many bidders stop participating in these auction markets. We argue that observed market information affects their choices. We explore how bidders form expectations about market supply, demand, and competition based on information from two sources—market design parameters and behavior of market participants. By employing a hierarchical Bayesian latent attrition model, we empirically detect and investigate the effect of bidders’ expected market supply, demand, and competition on their attrition in these markets. Our study shows that the effect of market information on attrition is nuanced by bidders’ value heterogeneity. Through the lenses of behavioral economics theories, we show that the attrition behavior of high-value bidders is completely opposite that of low-value bidders. We discuss the practical implications of our findings. Collapse

Topics: online auction decision making electronic market willingness to pay consumer behavior

Methods: descriptive statistic hidden Markov model Markov chain Monte Carlo conceptual modelling theoretical contribution

Theories: economic theory endowment effect behavioral economics lemon market theory social learning theory

What’s in a “Username”? The Effect of Perceived Anonymity on Herding in Crowdfunding

2022 | Information Systems Research | Citations: 0

Authors: Jiang, Yang; Ho, Yi-Chun (Chad); Yan, Xiangbin; Tan, Yong

Abstract: This research examines the role of perceived anonymity in shaping herding behavi ... Expand

Abstract: This research examines the role of perceived anonymity in shaping herding behavior in online crowdfunding markets. Drawing on theories from social psychology literature, we argue that a lender forms different credibility perceptions toward preceding peers based on their perceived anonymity state; the lender then uses such perceptions to adjust the lender’s herding momentum toward them. Using data collected from a leading debt-based crowdfunding platform, we classify an individual’s username as either anonymous or real-seeming, with the latter referring to as a user identification that seems to reveal one’s real name. The results show that successors demonstrate weaker herding momentum toward predecessors who are presented with real-seeming usernames than anonymous ones. This finding, which we attribute to a lower extent of perceived credibility derived from a nonconforming behavior, challenges the conventional wisdom that considers anonymity a negative factor for source credibility. We further show that the uncovered positive effect of perceived anonymity on herding is accentuated in the early stage of the fundraising period; nevertheless, we find no such discrepancies between listings that are assigned with high-risk and low-risk credit grades. Our study contributes to the literatures dealing with anonymity and herding in online environments. Collapse

Topics: anonymity crowdsourcing crowdfunding online review privacy

Methods: robustness check hierarchical linear modeling longitudinal research statistical hypothesis test descriptive statistic

Theories: social learning theory

The Impact of Digital Citizenship on Gendered Cyberbullying

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Macharia, Mary; Dunaway, Mary

Abstract: The Internet's ubiquity and accessibility have opened up possibilities for limi ... Expand

Abstract: The Internet's ubiquity and accessibility have opened up possibilities for limitless user interaction and collaboration; however, it has also expanded the likelihood for negative online behaviors, including cyberbullying and online harassment. Cyberbullying manifests as gendered and non-gendered behaviors. This research seeks to center on this notion of gendered and non-gendered cyberbullying and offer digital citizenship as a mechanism to mitigate the negative behaviors. We draw on social Learning theory (SLT) and a set of two complementary theories: the online disinhibition effect and Bandura et al.'s theory of moral disengagement to explore digital citizenship and its impact on gendered and non-gendered cyberbullying. We examine the role of digital citizenship as a behavior modification for non-gendered and gendered cyberbullying. This research-in-progress is proposed to enhance our understanding of how digital citizenship can play a role in gendered and non-gendered cyberbullying behaviors. Collapse

Topics: cyberbullying

Theories: social learning theory

How Employees Learn Information Security Policy Compliance Behavior: Toward a Social Learning Perspective

2022 | European Conference On Information Systems | Citations: 5

Authors: Hengstler, Sebastian; Pryazhnykova, Natalya; Kühnel, Stephan

Abstract: Information security attacks typically exploit the weakest link in the chain, w ... Expand

Abstract: Information security attacks typically exploit the weakest link in the chain, which is in most cases is the IT end user at the workplace. While great strides have been made in understanding and explaining information security behavior, little is known about how such behavior is acquired by individuals in the first place. This research approaches the phenomenon through the lens of social learning theory. We argue that a new employee's behavior is initially learned through differential associations within the social network, rather than through knowledge of formal policies and associated sanctions. We used a scenario-based experimental approach and collected data from new employees with five years or less of work experience. Our results show that employee's behavior changes over time. Reinforcement through sanctions becomes more important in the maintenance phase, while imitation of others becomes less relevant. Collapse

Topics: data security cybersecurity behavior data encryption data completeness human resource management

Methods: survey statistical power analysis hierarchical linear modeling experimental group experiment

Theories: social learning theory theory of planned behavior