Towards a thematic dimensional framework of online fraud: An exploration of fraudulent email attack tactics and intentions

2023 | Decision Support Systems | Citations: 1

Authors: Bera, Debalina; Ogbanufe, Obi; Kim, Dan J.

Abstract: Despite anti-phishing filters, social engineering-based cyber-attacks still resu ... Expand

Abstract: Despite anti-phishing filters, social engineering-based cyber-attacks still result in billions of dollars lost annually, significant personal identity theft, loss of corporate secrets, and espionage. We review the phishing literature on psychological attacks and design tactics employed for deception by attackers. The result reveals the need to continuously identify tactics embedded in fraudulent email content to understand why users still fall prey to phishing attacks. Using the literature as a backdrop, we identify and conceptualize a thematic dimensional framework of fraudulent phishing attacks. This study uses benchmark datasets of fraudulent email to empirically validate the proposed dimensional framework. Specifically, a combination of machine learning-based content analysis and topic modeling found a majority of the discovered topics were labeled against the proposed di­ mensions. Statistical analysis was employed to provide empirical evidence of the thematic dimensions of fraudulent email attacks. This study thus, not only extends the cybersecurity literature by identifying and vali­ dating eight dimensions that enrich our understanding of phishing and attack identification but stimulates cu­ mulative research endeavors to develop yet more comprehensive dimensional frameworks of phishing email attacks. Collapse

Topics: phishing email phishing electronic mail email spam personalization

Methods: term frequency–inverse document frequency natural language processing topic model discriminant analysis literature study

The Evaluation of Founder Failure and Success by Hiring Firms: A Field Experiment

2023 | Organization Science | Citations: 0

Authors: Botelho, Tristan L.; Chang, Melody

Abstract: Organizations tout the importance of innovation and entrepreneurship. Yet, when ... Expand

Abstract: Organizations tout the importance of innovation and entrepreneurship. Yet, when hiring it remains unclear how they evaluate entrepreneurial human capital—namely, job candidates with founder experience. How hiring firms evaluate this experience—and especially how this evaluation varies by entrepreneurial success and failure—reveals insights into the structures and processes within organizations. Organizations research points to two perspectives related to the evaluation of founder experience: Former founders may be advantaged, due to founder experience signaling high-quality capabilities and human capital, or disadvantaged, due to concerns related to fit and commitment. To identify the dominant class of mechanisms driving the evaluation of founder experience, it is important to consider how these evaluations differ, depending on whether the founder’s venture failed or succeeded. To isolate demand-side mechanisms and hold supply-side factors constant, we conducted a field experiment. We sent applications varying the candidate’s founder experience to 2,400 software engineering positions in the United States at random. We find that former founders received 43% fewer callbacks than nonfounders and that this difference is driven by older hiring firms. Further, this founder penalty is greatest for former successful founders, who received 33% fewer callbacks than former failed founders. Our results highlight that mechanisms related to concerns about fit and commitment, rather than information asymmetry about quality, are most influential when hiring firms evaluate former founders in our context. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1592. Collapse

Topics: IT career human resource management entrepreneurship systems development innovation management

Methods: qualitative interview field experiment theory development experiment experimental design

Theories: lemon market theory

Managing Organizational Cyber Security – The Distinct Role of Internalized Responsibility

2023 | HICSS | Citations: 0

Authors: Faltermaier, Stefan; Strunk, Kim; Obermeier, Michaela; Fiedler, Marina

Abstract: Desirable user behavior is key to cyber security in organizations. However, a co ... Expand

Abstract: Desirable user behavior is key to cyber security in organizations. However, a comprehensive overview on how to manage user behavior effectively, in order to support organizational cyber security, is missing. Building on extant research to identify central components of organizational cyber security management and on a qualitative analysis based on 20 semi-structured interviews with users and IT-Managers of a European university, we present an integrated model on this issue. We contribute to understanding the interrelations of namely user awareness, user IT-capabilities, organizational IT, user behavior, and especially internalized responsibility and relation to organizational cyber security. Collapse

Topics: IT security user behavior information technology capability IT security defense password

Methods: qualitative interview personal interview qualitative coding grounded theory case study

Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media

2023 | Journal of Management Information Systems | Citations: 0

Authors: Johnson, Matthew; Murthy, Dhiraj; Robertson, Brett W.; Smith, William Roth; Stephens, Keri K.

Abstract: Social media platforms are increasingly used during disasters. In the United Sta ... Expand

Abstract: Social media platforms are increasingly used during disasters. In the United States, users often consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our approach uses transfer learning and classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification but also that real-time classification of social media images using a small training set is possible. Collapse

Topics: social media Twitter hurricane natural hazard application programming interface

Methods: machine learning deep learning literature study convolutional neural network classification method

Fake review detection system for online E-commerce platforms: A supervised general mixed probability approach

2023 | Decision Support Systems | Citations: 0

Authors: Luo, Jiwei; Luo, Jian; Nan, Guofang; Li, Dahui

Abstract: Online consumer reviews play an important role in helping consumers judge the qu ... Expand

Abstract: Online consumer reviews play an important role in helping consumers judge the quality and authenticity of products on e-commerce platforms. However, the constant presence of fake reviews on these platforms has significantly impacted the operation and development of e-commerce platforms. In this study, we develop a novel supervised probabilistic method to detect fake reviews by utilizing the difference in the distribution of nonfraudulent reviews and that of fake reviews. Specifically, we first derive the univariate distributions of several unique features (linguistic, behavioral, and interrelationship features). We then integrate these distributions into two mixed distributions according to their labels to represent the overall difference between non-fraudulent reviews and fake reviews. Next, we randomly generate synthetic review data points with different labels from the above mixed distributions. Finally, we train a Multilayer Perceptron model by using these synthetic review data to obtain a classifier. We conducted several experiments to test the model using several original real-world review datasets. Numerical results indicated that the proposed supervised method outperformed some wellknown sampling models and fake review detection methods, in terms of classification accuracy. Moreover, we extend the proposed method to handle the scenarios with small samples of raw review data. This study con­ tributes to the literature by exploiting the difference in the distribution of non-fraudulent reviews and that of fraudulent reviews, which can improve the accuracy of fake review detection for online platforms. Collapse

Topics: fake review electronic commerce big data decision support system user behavior

Methods: computational algorithm experiment machine learning semi-supervised learning artificial neural network

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

2023 | Information Systems Frontiers | Citations: 0

Authors: Meske, Christian; Bunde, Enrico

Abstract: Hate speech in social media is an increasing problem that can negatively affect ... Expand

Abstract: Hate speech in social media is an increasing problem that can negatively affect individuals and society as a whole. Moderators on social media platforms need to be technologically supported to detect problematic content and react accordingly. In this article, we develop and discuss the design principles that are best suited for creating efficient user interfaces for decision support systems that use artificial intelligence (AI) to assist human moderators. We qualitatively and quantitatively evaluated various design options over three design cycles with a total of 641 participants. Besides measuring perceived ease of use, perceived usefulness, and intention to use, we also conducted an experiment to prove the significant influence of AI explainability on end users’ perceived cognitive efforts, perceived informativeness, mental model, and trustworthiness in AI. Finally, we tested the acquired design knowledge with software developers, who rated the reusability of the proposed design principles as high. Collapse

Topics: artificial intelligence explainable artificial intelligence perceived ease of use usage intention social media

Methods: design science experiment design principle survey qualitative content analysis

Applying Dispositional, Situational, and Organizational Risk Propensity to Technology Threat Avoidance Theory - A tripartite View

2023 | European Conference On Information Systems | Citations: 0

Authors: Pfaff, Theresa; Nastjuk, Ilja; Matschak, Tizian

Abstract: Human error is one of the biggest cybersecurity vulnerabilities in organization ... Expand

Abstract: Human error is one of the biggest cybersecurity vulnerabilities in organizations, making organizational security solutions become ineffective. It is therefore crucial for organizations to adapt their risk management in concordance to arising threats. Using the Technology Threat Avoidance Theory (TTAT) as a theoretical lens, this article investigates the relationship between security threats, employees' risk perception, and avoidance behavior. While recent research has suggested that an individual's dispositional risk propensity can influence the perception of technology threats, we argue that additional risk facets, namely situational and organizational risk propensity, can influence employees' threat perception. Through a scenario-based online experiment, our study reveals that risk perception in a work environment is not a static risk assessment but rather depends on situational aspects and the organization's risk propensity. Our study contributes to existing research by investigating the different aspects of risk-taking in an organizational context improving our understanding of employees' behavior at work. Collapse

Topics: electronic mail self efficacy malware website IT security

Methods: survey experiment PLS tool partial least squares path modeling computational algorithm

Theories: technology threat avoidance theory

Conducting Online Focus Groups - Practical Advice for Information Systems Researchers

2023 | Communications of the Association for Information Systems | Citations: 1

Authors: Schulze, Laura; Trenz, Manuel; Cai, Zhao; Tan, Chee-Wee

Abstract: Video-based online focus groups (OFGs) present an emerging opportunity for Info ... Expand

Abstract: Video-based online focus groups (OFGs) present an emerging opportunity for Information Systems (IS) researchers to circumvent spatial and temporal constraints in collecting rich data. They enable researchers to overcome interpersonal and operational challenges arising from face-to-face (F2F) focus groups (FGs) by allowing participants, who are located anywhere in the world, to share their personal experiences from behind their screens. However, the realization of the full potential of OFGs for IS research is currently hampered by challenges and uncertainty over best practices when conducting such FGs. Consequently, we offer a detailed account of our own experiences with seven OFGs in the context of digital platforms. In supplementing our own experiences with those of others reported in extant literature on (online) FGs in and beyond the IS discipline, we (a) arrive at hurdles inherent to the OFG method, (b) derive lessons learned from our own experience with OFGs, and (c) prescribe actionable advice to researchers who are interested in conducting OFGs in the future. Collapse

Topics: IS discipline video conference social media electronic mail online chat

Methods: focus group literature study survey group interview qualitative interview

Introduction to the Minitrack on Social Commerce: The Good, the Bad, and the Ugly

2023 | HICSS | Citations: 0

Authors: Sundaram, David; Sadovykh, Valeria; Peko, Gabrielle

Abstract: ... Expand

Abstract: Collapse

Topics: electronic commerce technostress mobile application privacy fake review

Methods: sentiment analysis machine learning

Detecting Fake Reviews: Just a Matter of Data

2023 | HICSS | Citations: 0

Authors: Theuerkauf, René; Peters, Ralf

Abstract: Along with the ever-increasing portfolio of products online, the incentive for m ... Expand

Abstract: Along with the ever-increasing portfolio of products online, the incentive for market participants to write fake reviews to gain a competitive edge has increased as well. This article demonstrates the effectiveness of using different combinations of spam detection features to detect fake reviews other than the review-based features typically used. Using a spectrum of feature sets offers greater accuracy in identifying fake reviews than using review-based features only, and using a machine learning algorithm for classification and different amounts of feature sets further elucidates the difference in performance. Results compared by benchmarking show that applying a technique prioritizing feature importance benefits from prioritizing features from multiple feature sets and that creating feature sets based on reviews, reviewers and product data can achieve the greatest accuracy. Collapse

Topics: fake review email spam digital distribution mobile application market mobile application

Methods: machine learning computational algorithm random forest classification term frequency–inverse document frequency Cross-Industry Standard Process for Data Mining