Prior Information Systems Research Articles

공급사슬 역량이 공급사슬 회복탄력성과 기업성과에 미치는 영향에 관한 연구

External risk factors such as pandemics such as COVID-19, the trade dispute between the US and China, and the Russia-Ukraine crisis are causing unexpected damage to companies’ supply chains. Under this environment, companies are in dire need of strengthening the capabilities and resilience of their supply chains. Strengthening the resilience of the supply chain through supply chain capability factors plays an important role in improving corporate performance. The purpose of this study is to analyze this causal relationship through survey analysis. Through prior studies, information systems, collaboration, operational capabilities, and strategic response capabilities were set as variables for supply chain capabilities, and resilience was defined as flexibility and agility. Through structural equation model analysis using AMOS 28.0, it was confirmed that information systems have limitations in improving resilience, and that collaboration and operational capabilities play an important role in improving this. In addition, it was found that supply chain capabilities have an indirect positive effect on customer satisfaction, showing that strengthening supply chain capabilities has a significant impact on corporate performance through resilience. Therefore, companies are required to establish strategies to effectively manage risks and increase customer satisfaction by strengthening supply chain capabilities.

Updating proxy-based site amplification map with in-situ data in Osaka, Japan: A Bayesian scheme based on uncertainty projected mapping

Site amplification maps are mostly proxy-based. Often due to the absence of in-situ data at the regional or local scale, a high level of confidence cannot be assigned to the site amplifications. It has often been observed that the in-situ amplifications differ from proxy-based estimates. So, whenever new in-situ data are made available, it is necessary to update the proxy-based estimates. Bayesian frameworks are recently gaining attention as model updating schemes. This study proposes a Bayesian scheme for updating proxy-based maps with in-situ data. This scheme is based on uncertainty projected mapping (UPM), where the significance of local in-situ data variability determines the posterior estimates. The study area is in Osaka, Japan, where discrepancies in proxy-based estimates and observed ground motions were documented during the 2018 Northern Osaka earthquake. Dense borehole data from the Kansai Geo-informatics Network are available in this area. Peak ground velocity (PGV) site amplification evaluations from this dense borehole network are used as likelihoods to update the prior proxy-based Japan seismic hazard information system (J-SHIS) site amplification map. As a result, the posterior map shows updated site amplification estimates which better represent the in-situ data. The updated site amplification map is then used to investigate the role of site amplification in explaining the building damage during the 2018 Northern Osaka earthquake.

A Design Theory for Certification Presentations

Prior information system research remains inconsistent of the effects of system certifications. In their current use, certifications are often reduced to graphical seals. This approach fails to incorporate detailed assurance information emanating from the certification process. To address this gap, we adopt a design science approach and deploy a four-phase research design to clarify how to design impactful IS certification presentations. First, we identify sources of users' limited understanding of seals and formulate a design proposal for a certification presentation by drawing upon the elaboration likelihood model. In the second phase, we formulate and validate a set of design meta-requirements and guidelines to improve certification presentation, using cognitive load theory and Toulmin's model of argumentation as kernel theories. In the third phase, new certification presentations that comply with the proposed guidelines are developed and evaluated for their effectiveness. We show that presentations that augment seal-based certification presentations with richer assurance information improve certification effectiveness. This increases users' assurance and trust perceptions when the presentations align with the users' cognitive information processing needs in ways that reduce their cognitive load and enhance argument quality of assurance information.

Ambivalence Is Better than Indifference: A Behavioral and Neurophysiological Assessment of Ambivalence in Online Environments

Information representations such as ratings and reviews play an important role in assisting users in making decisions in online environments. Prior information systems (IS) research has mostly focused on the role of extreme valence, i.e., the positivity/negativity of information, portrayed by such representations. Yet this bipolar approach discounts how the coexistence of positivity and negativity (i.e., ambivalence) or their absence (i.e., indifference) is formed and leads to distinct attentional processes and outcomes such as purchase decisions. We theorize how and why the valence of information projected through such representations may elicit mixed feelings and influence decision-making in online environments. We conducted four randomized controlled experiments, including an electroencephalography (EEG) study, to disentangle the influence of ambivalence and indifference on decision-making in an online shopping context. We found that ambivalence and indifference to online information distinctly influenced attention and purchase decisions relative to positivity and negativity. Our findings further suggest the inability of incumbent bipolar representations, such as the widely implemented star rating system, to capture the mixed feelings expressed in online content. We propose a bivariate intervention that overcomes the limitations of bipolar representations by not only discerning ambivalence from indifference but also amplifying purchase decisions for products with ambivalent information by at least 50%, compared to incumbent bipolar representations. Our findings advance ongoing research on the role of information valence in online environments and offer implications for practice.

Clarifying the Role of E-Government Trust in E-Government Success Models: A Meta-analytic Structural Equation Modeling Approach

E-government implementation success is of critical importance for nations. Prior information systems (IS) success models emphasize the effects of information quality, service quality, system quality, and user satisfaction but do not consider e-government trust. This study incorporates e-government trust into the IS success model and empirically tests the model on empirical findings reported in 67 prior studies using meta-analysis methods and structural equation modeling. Our analysis shows that: a) information quality, service quality, system quality, and user satisfaction influence e-government trust, and b) system use mediates the effect of e-government trust on intention to use e-government systems in the future.

The importance of theory at the Information Systems Journal

The importance of theory at the <i>Information Systems Journal</i>

Exploring the consequence of information communication technology-enabled work during non-working hours: a stress perspective

PurposeThis study aims to examine the impact of information communication technology-enabled work during non-working hours (ICT-enabled WNWHs), as a source of stress, on employee behavioral outcomes –in-role job performance, organizational citizenship behaviors (OCBs) that benefit organizations and OCBs that benefit individuals, through emotional responses – work exhaustion, nonwork exhaustion and organization-based self-esteem. As the coronavirus disease 2019 (COVID-19) lockdowns demonstrated that employees frequently engage in ICT-enabled WNWHs, studying stress induced by ICT-enabled WNWHs is essential for understanding employee adaptation to the work-from-home trend that emerged from COVID-19 lockdowns.Design/methodology/approachA quantitative survey comprising 1,178 employees in China was conducted, and the data reliability and validity were confirmed. Partial least squares structural equation modeling analysis was employed to test the hypotheses.FindingsThe study results empirically proved that, although ICT-enabled WNWHs had significant effects on employee behavioral outcomes, the related emotional responses were the mediators of the stress transmission mechanism that directly affected employee behavioral outcomes. Notably, work exhaustion and organization-based self-esteem partially mediate the stress transmission mechanism, while nonwork exhaustion exerts a full mediating effect.Originality/valueThis study proposes the stress transmission mechanism of ICT-enabled WNWHs and delineates emotional responses regarding the work environment attributes of ICT-enabled WNWHs, an approach rarely seen in prior IS studies. To our best knowledge, this study is the first to identify and empirically demonstrate organization-based self-esteem as one among the emotional responses to ICT-enabled WNWHs. Furthermore, it expands understanding of the holistic impacts of ICT-enabled WNWHs, which is lacking in information systems (IS) literature.

To Be or Not to Be …Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents

ABSTRACT Driven by the need to provide continuous, timely, and efficient customer service, firms are constantly experimenting with emerging technological solutions. In recent times firms have shown an increased interest in designing and implementing artificial intelligence (AI)-based interactional technologies, such as conversational AI agents and chatbots, that obviate the need for having human service agents for the provision of customer service. However, the business impact of conversational AI is contingent on customers using and adequately engaging with these tools. This engagement depends, in turn, on conversational AI’s similarity, or likeness to the human beings it is intended to replace. Businesses therefore need to understand what human-like characteristics and competencies should be embedded in customer-facing conversational AI agents to facilitate smooth user interaction. This focus on “human-likeness” for facilitating user engagement in the case of conversational AI agents is in sharp contrast to most prior information systems (IS) user engagement research, which is predicated on the “instrumental value” of information technology (IT). Grounding our work in the individual human competency and media naturalness literatures, we theorize the key role of human-like interactional competencies in conversational AI agents—specifically, cognitive, relational, and emotional competencies—in facilitating user engagement. We also hypothesize the mediating role of user trust in these relationships. Following a sequential mixed methods approach, we use a quantitative two-wave, survey-based study to test our model. We then examine the results in light of findings from qualitative follow-up interviews with a sampled set of conversational AI users. Together, the results offer a nuanced understanding of desirable human-like competencies in conversational AI agents and the salient role of user trust in fostering user engagement with them. We also discuss the implications of our study for research and practice.

Application of Bayesian Algorithm in Risk Quantification for Network Security.

Network security risk quantification involves both technical and management aspects. Risk quantification has great uncertainty and cannot be fully quantified. Therefore, the fully objective realization of network information security risk quantification is not yet mature. This paper analyzes and quantifies the network security risks caused by various threat sources through a network security risk quantification model based on the Bayesian algorithm. By combining expert knowledge, the conditional probability matrix under the inference rule of the Bayesian algorithm is clarified, and the subjective judgment information of experts on the damage degree of the target information system is synthesized into the prior information system of network security threat. The Bayesian algorithm is used to realize the observation node of objective assessment information and combining subjective security threat levels to achieve continuity and accumulation of security assessments. The error is about 3%, which has a very good effect on the quantification of network security risk.

Evaluating the impact of big data analytics usage on the decision-making quality of organizations

Big data initiatives are critical for transforming traditional organizational decision making into data-driven decision making. However, prior information systems research has not paid enough attention to the impact of big data analytics usage on decision-making quality. Drawing on the dynamic capability theory, this study investigated the impact of big data analytics usage on decision-making quality and tested the mediating effect of data analytics capabilities. We collected data from 240 agricultural firms in China. The empirical results showed that big data analytics usage had a positive impact on decision-making quality and that data analytics capabilities played a mediating role in the relationship between big data analytics usage and decision-making quality. Hence, firms should not only popularize big data analytics usage in their business activities but also take measures to improve their data analytics capabilities, which will improve their decision-making quality toward competitive advantages.

Pathways to individual performance: Examining the interplay between knowledge bases and repository kms use

The aims of this study are to gain further insight into the contingent performance effects of repository knowledge management systems (KMS) use. While prior research has laid the foundations, a nuanced understanding of the interplay between the individual's personal and social knowledge bases and KMS usage behaviors is missing. Drawing from prior information systems literature, we identify two components of KMS use, usage frequency and usage intensity. We examine the influence of knowledge bases on individual performance, and the moderating influence of the usage behaviors. We test the hypothesized research model on usage and performance data from 18,219 real estate agents. We find support for different pathways to individual performance based on configurations of knowledge bases and usage behavior. Overall, the study provides an integrated view of the interplay between the three constituents of technology-based learning – cognition, behavior, and performance.

Machine learning in tutorials – Universal applicability, underinformed application, and other misconceptions

Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for the critical analysis of socio-technical systems based on machine learning. To provide a foundation for future critical analysis of machine learning-based systems, we engage with how the term is framed and constructed in self-education resources. For this, we analyze machine learning tutorials, an important information source for self-learners and a key tool for the formation of the practices of the machine learning community. Our analysis identifies canonical examples of machine learning as well as important misconceptions and problematic framings. Our results show that machine learning is presented as being universally applicable and that the application of machine learning without special expertise is actively encouraged. Explanations of machine learning algorithms are missing or strongly limited. Meanwhile, the importance of data is vastly understated. This has implications for the manifestation of (new) social inequalities through machine learning-based systems.

The personalization–privacy paradox in the attention economy

The personalization–privacy paradox persists because consumers appreciate the value of personalization, yet marketers’ exploitation of consumers’ personal information to provide such personalization raises privacy concerns. Consumers then may refuse to provide personal information, which limits personalization efforts. Attempts to rely on information technology (e.g., anonymizing techniques, peer-to-peer communication) to address privacy concerns largely have proven ineffective, often because they are overly sophisticated for consumers. Thus, even if the personalization–privacy paradox seemingly arose with mobile technologies, it must stem from a theoretical foundation. Prior information systems literature tends to adopt micro-oriented theories to understand the paradox, but this perspective article instead seeks to investigate the personalization–privacy paradox at a macro level, with an attention economy lens. By investigating the relationship among personalization, privacy, and attention, this study seeks to offer insights pertaining to the ecology of attention, choice architectures, and stylistic devices, as well as some relevant implications for research and practice.

Human Capital is Not Enough: How Offshore BPO Professionals Use Social Support to Deal with Strenuous Work Conditions

The past decade has witnessed explosive growth in the segment of information technology (IT) professionals who work in the offshore business process outsourcing (BPO) industry. BPO positions are highly regimented and standardized with coercive and normative controls to deter employees from deviating from prescribed work procedures and exercising independent judgment. Coping with these challenges requires a different set of skills than human capital and technical competencies that are discussed in most prior information systems (IS) research. This paper develops theory on the role of social support in employee compensation, and tests the theory using a unique data set of 8,000+ Indian BPO professionals during the 2006–2011 time period. Co-worker social support has a larger impact at higher levels of time stress and supervisor stress, while supervisor social support plays a larger role for senior-level positions than for junior-level positions. We discuss implications of our theory and findings for research and practice.

Critically studying openness: A way forward

Critically studying openness: A way forward

PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research

Exploring theoretically plausible alternative models for explaining the phenomenon under study is a crucial step in advancing scientific knowledge. This paper advocates model selection in information systems (IS) studies that use partial least squares path modeling (PLS) and suggests the use of model selection criteria derived from information theory for this purpose. These criteria allow researchers to compare alternative models and select a parsimonious yet well-fitting model. However, as our review of prior IS research practice shows, their use—while common in the econometrics field and in factor-based SEM—has not found its way into studies using PLS. Using a Monte Carlo study, we compare the performance of several model selection criteria in selecting the best model from a set of competing models under different model set-ups and various conditions of sample size, effect size, and loading patterns. Our results suggest that appropriate model selection cannot be achieved by relying on the PLS criteria (i.e., R2, Adjusted R2, GoF, and Q2), as is the current practice in academic research. Instead, model selection criteria—in particular, the Bayesian information criterion (BIC) and the Geweke-Meese criterion (GM)—should be used due to their high model selection accuracy and ease of use. To support researchers in the adoption of these criteria, we introduce a five-step procedure that delineates the roles of model selection and statistical inference and discuss misconceptions that may arise in their use.

Rationale for “Liking” on Social Networking Sites

The “ like” feature is popularly utilized by online social media users for different reasons including socializing, giving feedback and giving or seeking attention as well as for pure affection. The “ like” function is a gamified element of social networking sites used billions of times per day. Despite its widespread use in the social media space, little is known about the different factors that influence Facebook users’ “ like” continuation intention or the game mechanics of “ like.” To address this relevant issue, a cross-sectional survey was administered with 728 adolescent Facebook users (12–18 years old). This study utilized the theory of planned behavior to investigate the role of attitude (hedonic motivation, reciprocal benefit, and social presence), subjective norms (primary influence and secondary influence), and perceived behavioral control (self-efficacy and habit) in influencing the continuation intention of “ like” as well as the influence of self-efficacy and habit on the game mechanics of “ like.” This investigation addresses the urgent need to understand better the postadoption issues as well as the intentions to use specific features of social media. The results suggest that social presence, primary and secondary influence, self-efficacy, and habit significantly predicted Facebook “ like” continuation intention. Furthermore, self-efficacy and habit significantly predicted the game mechanics of “ like.” Different theoretical and practical implications of the study are presented and discussed in light of prior information systems literature.

Validation of a vignettes-based, hands-on cybersecurity threats situational assessment tool

Advanced Persistent Threats (APTs) have been growing with social engineering and corporate e-mail compromise reported as the two most penetration vectors to organizational networks. Historically, users (i.e., office assistants, managers, executives) have access to sensitive data and represent up to 95% of cybersecurity threats to organizations. This study addressed the problem of threats to organizational information systems (IS) due to vulnerabilities and breaches caused by employees. While in the past, only selected employees at the organization had access to the computer networks, with the proliferation of mobile devices almost all employees and vendors/contractors have access to the organizational networks. Computer and mobile device users are one of the weakest links in the cybersecurity chain, due to their limited cybersecurity skills (CySs). Over the years, the measures of CySs of computer users were based on self reported surveys or measured knowledge only. Prior IS and medical research found participants view scenarios as nonintrusive and unintimidating, while providing a realistic way to assess various situations from sexual harassment to chemical hazards. Therefore, this paper discusses the validation stage of a cybersecurity threats situational assessment tool that utilizes vignettes with observable hands-on tasks to measure and quantify CySs. Discussions and future research are also presented.

External Knowledge and Information Technology: Implications for Process Innovation Performance

Prior information systems research highlights the vital role of information technology (IT) for innovation in firms. At the same time, innovation literature has shown that accessing and integrating knowledge from sources that reside outside the firm, such as customers, competitors, universities, or consultants, is critical to firms' innovative success. In this paper, we draw on the knowledge-based view of the firm to investigate how search in external knowledge sources and information technology for knowledge absorption jointly influence process innovation performance. Our model is tested on a nine-year panel (2003-2011) of Swiss firms from a wide range of manufacturing industries. Using instrumental variables, and disaggregating by type of IT, we find that data access systems and network connectivity hold very different potential for the effective absorption of external knowledge, and the subsequent realized economic gains from process innovation. Against the backdrop of today's digital transformation, our findings demonstrate how firms should coordinate strategies for sourcing external knowledge with specific IT investments in order to improve their innovation performance.

Understanding the Role of IS and Application Domain Knowledge on Conceptual Schema Problem Solving: A Verbal Protocol Study

One of the most neglected areas of information systems research is the role of the domain to which researchers apply IS methods, tools, and techniques; that is, the application domain. For example, little prior information systems (IS) or related research has examined how IS and application domain knowledge (ISDK and ADK, respectively) influence how individuals solve conceptual schema problem-solving tasks. In this research, we investigate the effects of both ISDK and ADK on two types of conceptual schema problem-solving tasks: schema based and inferential. We used verbal protocol analysis to explore the roles that ISDK and ADK play in the problem-solving processes participants use when addressing these tasks. We found that, for the two types of conceptual schema problem-solving tasks, ADK and ISDK have similar effects on problem-solving processes. That is, we found that, for schema-based problem-solving tasks, participants used focused (depth-first) processes when the application domain was familiar as did participants with greater IS domain knowledge. We also found that, for inferential problem-solving tasks, participants used exploratory (breadth-first) processes when the application domain was familiar as did participants with greater IS domain knowledge. We then show how cognitive psychology literature on problem solving can help explain the effects of ISDK and ADK and, thus, provide the theoretical foundation for analyzing the roles of each type of knowledge in the process of IS problem solving.