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Online consumer reviews significantly impact market performance as potential customers rely heavily on these reviews for consumer decision making. Accordingly, experienced online reviewers, or highly motivated reviewers who account for the largest attribution of reviews, are proposed to be an important part of the online reviewing ecosystem. This research examines experienced reviewers in the online communities. Using empirical data, this study found that experienced reviewers tend to behave as experts with the aim to achieve a common good with rating and selection attributes similar to critics. Hence, results showed that experienced reviewers leave lower ratings, have less extremity in their ratings, prefer sophisticated products but do not prefer popular products. The female experienced reviewers are less generous than novice female reviewers and their generosity decreases more dramatically than males in the rating propensity as they become experienced reviewers.

The COVID-19 global pandemic created an optimal environment for counterfeiters to exploit vulnerabilities in the manufacturing industry. The decentralized and global nature of additive manufacturing (AM) systems created new attack vectors for counterfeiting due to ease of compromise of product and process information. To solve this challenge, innovative technologies and scientifically reliable methods for predicting authenticity are in great demand. In this work, a framework for differentiating between authentic and counterfeit AM automotive and aerospace components is proposed. Extant literature was reviewed and current anti-counterfeit technologies analyzed, informing the basis of the framework. The process was validated with a castle nut printed via selective laser-melting of a stainless steel M18 castle nut slightly modified with a Cantor dust fractal. The castle nut was then inspected with 225 and 450 kV X-ray tomography to assess the fidelity of fractal structure. A model trust anchor image sequence was developed and analyzed with a perceptual hash, Hamming distance computations, distribution functions, and null hypothesis for proof of authenticity. For authentic parts, a blockchain was updated with provenance. Future work will explore wider implementation with the goal of reducing prevalence of counterfeit parts in AM manufacturing systems.

Tracing the relationship between pathological personality traits and insider cyber sabotage.

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

Malicious insider attacks affect all sectors, economies, and organizations of the world, with insider cyber-sabotage causing significant damage. Key findings from previous research on malicious insider threats underscore a need for prevention and detection programs that are broadly based. Current risk management administrative and technical mitigation techniques often include indicators of potential malicious insiders in the form of personal factors and observable behaviors. However, these need empirical validation in the context of cyber-sabotage. This article builds on previous research on insider threats and investigates the relationship between motives, behavioral indicators, and insider cyber-sabotage. We examine 74 actual cases of convicted insider threat attacks using a logistic regression model. The results confirm hypotheses that revenge (one of the motives), addiction (one of the observable behavioral indicators), and poor work performance increase the likelihood of insider sabotage. The findings provide empirical validation of cyber-sabotage specific indicators for insider threat programs. Implications and future research are discussed.

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

Contest-based intermediary crowdsourcing represents a powerful new business model for generating ideas or solutions by engaging the crowd through an online competition. Prior research has examined motivating factors such as increased monetary reward or demotivating factors such as project requirement ambiguity. However, problematic issues related to crowd contest fitness have received little attention, particularly with regard to crowd strategic decision-making and contest outcomes that are critical for success of crowdsourcing platforms as well as implementation of crowdsourcing models in organizations. Using Expectation-Confirmation Theory (ECT), we take a different approach that focuses on contest level outcomes by developing a model to explain contest duration and performance. We postulate these contest outcomes are a function of managing crowdsourcing participant contest-fitness expectations and disconfirmation, particularly during the bidding process. Our empirical results show that contest fitness expectations and disconfirmation have an overall positive effect on contest performance. This study contributes to theory by demonstrating the adaptability of ECT literature to the online crowdsourcing domain at the level of the project contest. For practice, important insights regarding strategic decision making and understanding how crowd contest-fitness are observed for enhancing outcomes related to platform viability and successful organizational implementation.

Ensuring privacy in recommender systems for smart cities remains a research challenge, and in this paper we study collaborative filtering recommender systems for privacy-aware smart cities. Specifically, we use the rating matrix to establish connections between a privacy-aware smart city and k-coRating, a novel privacy-preserving rating data publishing model. First, we model privacy concerns in a smart city as the problem of privacy-preserving collaborative filtering recommendation. Then, we introduce k-coRating to address privacy concerns in published rating matrices, by filling the null ratings with predicted scores. This allows us to mask the original ratings to preserve k-anonymity-like data privacy, and enhance data utility (quantified using prediction accuracy in this paper). We show that the optimal k-coRated mapping is an NP-hard problem and design an efficient greedy algorithm to achieve k-coRating. We then demonstrate the utility of our approach empirically. Refereed/Peer-reviewed

With an exponential increase in the provisioning of multimedia devices over the Internet of Things (IoT), a significant amount of multimedia data (also referred to as multimedia big data – MMBD) is being generated. Current research and development activities focus on scalar sensor data based IoT or general MMBD and overlook the complexity of facilitating MMBD over IoT. This paper examines the unique nature and complexity of MMBD computing for IoT applications and develops a comprehensive taxonomy for MMBD abstracted into a novel process model reflecting MMBD over IoT. This process model addresses a number of research challenges associated with MMBD, such as scalability, accessibility, reliability, heterogeneity, and Quality of Service (QoS) requirements. A case study is presented to demonstrate the process model.

Recognizing the global need for cybersecurity professionals and shedding light on the employers' preference of skills give educators the opportunity to improve their process to prepare future generations for the workforce. The goal of this paper is to acquire a collection of skills employers value when hiring for a new cybersecurity position. Our research plan is split into three phases. First, we will develop an initial list of cybersecurity skills by using a systematic literature review to assess what past research has found. Next, we will validate the importance of these skills through a ratings-based survey of employers. Finally, we will refine and prioritize the validated skills using two preference capturing studies - rank order survey and conjoint analysis. Discussion and implications for future research and academic departments in cybersecurity are provided.

A key challenge resulting from the rapid growth of the information technology (IT) industry is finding enough qualified workers to fill available positions. In this paper, Holland’s Theory of Occupational Themes, Social Cognitive Career Theory, and Career Construction Theory are used to investigate how job-related beliefs, career planning perceptions, and occupational congruence work together to influence students’ career decisions, major satisfaction, and academic performance. Using 210 student responses, we empirically test a theoretically derived research model. Our findings suggest that job security is a strong predictor of both IT career optimism and career planning ability. In addition, career optimism and career planning ability are important antecedents of students’ IT career commitment, which significantly influences satisfaction with academic major. A modest portion of academic performance was explained by the model. Most notably, occupational congruence was found to be a poor predictor ...

Anglo and Hispanic ethnic groups perceive biometric privacy issues differently.Use of biometric systems may alleviate some, but not all, types of privacy concerns.Anglo and Hispanic ethnic groups reacted differently after exposure to system.Biometric data vulnerability concerns of Anglos were lower than Hispanics after use.Hispanics had higher accountability concerns related to biometrics prior to use. Biometric technology is rapidly gaining popularity as an access control mechanism in the workplace. In some instances, systems relying on biometric technology for access control have not been well received by employees. One potential reason for resistance may be perceived privacy issues associated with organizational collection and use of biometric data. This research draws on previous organizational information handling and procedural fairness literature to frame and examine these underlying privacy issues. Perceived accountability, perceived vulnerability, and distrust were distilled from the previous literature as the primary dimensions of employee privacy concerns related to biometric technology. This study assesses the effects of these privacy concerns, how they vary based on the cultural influences of Anglos and Hispanics.Fire ground accountability is a critical management objective in the firefighting domain. In multi-unit or multi-agency crisis response scenarios, the on-scene incident commander tracks and accounts for each first responder. This research designed and deployed a new fire ground accountability system that tracked firefighters through finger pattern-based biometric logins to their assigned positions on the firefighting apparatus. An instrument measuring level of privacy concern on three underlying dimensions and demographic data was developed, validated and administered in a quasi-experimental field study. A pre-test-post-test survey methodology was employed to detect potential differences in privacy concerns as familiarity with the system increased. The study shows that Anglo and Hispanic subjects frame privacy issues differently associated with use of biometric technology in a fire ground accountability system. Finally, the study showed that some privacy concerns such as distrust and perceived vulnerability can be alleviated through system use with changes in post-use privacy concerns moderated by ethnic affiliation.

Advances in authentication technology have led to a proliferation of biometric-based systems in the workplace. Although biometric technologies offer organizations a cost-effective method of increasing security, employees are often hesitant to permit use. The collection and storage of employee biometric data raises concerns about proper use of these intensely personal identifiers. This work draws from organizational privacy practices, electronic monitoring, procedural fairness, self-construal, and technology adoption theories. We investigate the effects of independent and interdependent self-construal on three newly developed dimensions of employee privacy concern related to organizational use of biometric technology. These dimensions include perceived accountability, perceived vulnerability, and perceived distrust toward the organization. We test the predictive power of our model using data from an organization deploying a new biometric system designed to track employee work assignments under the auspices of improving personnel safety. Results indicate that self-construal plays a significant role in the formulation of privacy concerns and both perceived accountability concerns and perceived vulnerability concerns are significant predictors of attitude toward using biometric technology in the workplace.

Competitive fitness environment platforms and technology rely on reward-based gamification, which can be traced back to customer loyalty programs started by the airline and hotel industry in the 1980’s. These reward systems use basic game elements of Badges, Levels/Leaderboards, Achievements, and Points (BLAP) to invoke intrinsic motivation. Reward-based gamifications are easy to implement and the application of such systems has been proven to be successful in adaptive learning of certain types of skills or encouraging the completion of routine tasks. However, implementing reward-based game elements without designing a meaningful experience tailored to the individuals’ characteristics or learning needs could lead to user boredom or disengagement over the long run. This research extends Hedonic Motivation System Model (HMSAM), which is specific system acceptance model based on cognitive absorption in a competitive fitness context, by examining the effect of users’ competitive and engagement characteristics. We propose that considering individual competitive differences as well as providing a meaningful immersive experience can enhance IS platform design and have practical results regarding enhancement of competitive fitness technology design in support of improved individual user performance and safety.

“Insiders” remain a significant threat to organizations—evidenced by recent cases involving Robert Hansen, Bradley Manning, and Edward Snowden—even in light of significant movement toward neutralizing the threat through detection and prevention. Insiders pose detection challenges for security professionals because they often have legitimate access and intimate organizational knowledge. Nonetheless, past insider threat detection research has predominantly focused on signature-based detection of digital indicators of insider activity and behavioral profiling. This article develops a novel relationship between addiction theory and the insider threat from an information systems perspective. This discussion introduces seven propositions concerning this relationship, addiction antecedents, and the factors moderating the relationship between addiction and the insider threat. This model has significant implications for the insider threat detection challenge, as it provides new signals that may be useful for detec...

Security incidents occur at an alarming rate with malicious software (malware) involved in a large number of these incidents. Current malware evaluation and handling practices in organizations are unstandardized and intelligence regarding threat levels often comes from vendors and peers, who lack a unified threat metric system. This paper explores a method to quantify malware threats systematically and proposes a quantitative malware threat metric system. The proposed method makes communicating the level of threat more effective and efficient, particularly for information sharing organizations.

Efforts to understand what goes on in the mind of an insider have taken a back seat to developing technical controls, yet insider threat incidents persist. We examine insider threat incidents with malicious intent and propose an explanation through a relationship between Dark Triad personality traits and the insider threat. Although Dark Triad personality traits have emerged in insider threat cases and deviant workplace behavior studies, they have not been labeled as such and little empirical research has examined this phenomenon. This paper builds on previous research on insider threat and introduces ten propositions concerning the relationship between Dark Triad personality traits and insider threat behavior. We include behavioral antecedents based on the Theory of Planned Behavior and Capability Means Opportunity (CMO) model and the factors affecting those antecedents. This research addresses the behavioral aspect of the insider threat and provides new information in support of academics and practitioners.

This research uses machine learning and outlier analysis to detect potentially hostile insiders through the automated analysis of stored data on cell phones, laptops, and desktop computers belonging to members of an organization. Whereas other systems look for specific signatures associated with hostile insider activity, our system is based on the creation of a “storage profile” for each user and then an automated analysis of all the storage profiles in the organization, with the purpose of finding storage outliers. Our hypothesis is that malicious insiders will have specific data and concentrations of data that differ from their colleagues and coworkers. By exploiting these differences, we can identify potentially hostile insiders. Our system is based on a combination of existing open source computer forensic tools and datamining algorithms. We modify these tools to perform a “lightweight” analysis based on statistical sampling over time. In this, our approach is both efficient and privacy sensitive. As a result, we can detect not just individuals that differ from their co-workers, but also insiders that differ from their historic norms. Accordingly, we should be able to detect insiders that have been “turned” by events or outside organizations. We should also be able to detect insider accounts that have been taken over by outsiders. Our project, now in its first year, is a three-year project funded by the Department of Homeland Security, Science and Technology Directorate, Cyber Security Division. In this paper we describe the underlying approach and demonstrate how the storage profile is created and collected using specially modified open source tools. We also present the results of running these tools on a 500GB corpus of simulated insider threat data created by the Naval Postgraduate School in 2008 under grant from the National Science Foundation.