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The consequences of corporate corruption control (CCC) have either been investigated outside the firm (e.g. foreign direct investment inflows) or inside the firm (e.g. profitability). Yet prior research addresses these implications separately, treating them as distinct phenomena, ignoring questions at their intersection. However, corruption control can be leveraged to benefit both organizations (internally) and environments (externally). In line with open systems theory, this study aims to explore a ripple effect of corruption control not only inside organizations (efficiency through adoption of sustainable resource management practices) but also outside [community-centered corporate social performance (CSP)].

Using a longitudinal sample of multinational enterprises from Forbes list of “The World’s Largest Public Companies,” the authors use a cross-lagged panel design to provide clarity regarding causal effects.

Results confirm causal directionality and support the positive effect of corruption control on resource management and community CSP, contributing toward understanding implications at the organization–environment interface.

The authors examine both internal and external implications of CCC. The use of a cross-lagged design that is relatively novel to the management field allows to check for casual effects between CSP elements that were previously assumed to have reciprocal casual effects.

This study aims to examine the relationship between emotional attachment (EA) and intelligent personal assistant (IPA) continuance intention. While existing theories emphasize purely rational and goal-oriented factors in terms of information technology (IT) continuance intention, this research examines how users' EA toward technology impacts their continuance intention in the absence of cognitive and habitual factors.

This study contextualizes attachment theory from the social psychology/consumer psychology literature to an IT application and formulates and tests a new model that is proposed in the context of IPA continuance. Five research hypotheses developed from contextualization and application of the theory were posited in a structural model and empirically validated using survey results from IPA users.

The results show that users' EA to IPA use significantly influences their IPA continuance intention, along with emotional trust and interaction quality with the IPA.

This study contextualizes attachment theory developed in the social psychology/consumer psychology literature to formulate and test a new model in the context of IPA continuance. This work contributes to the theoretical understanding by investigating IPA continuance intention in the absence of cognitive or habitual factors and fills a critical research gap in IT post-adoption literature. IPA is just one example of technologies to which individuals can form attachments and this research provides an important foundation for future research by positing and testing the value of EA in IT post-adoption behavior. This research also contributes to practical knowledge by inferring that IPA manufacturers, managers and vendors could extend their revenue streams by integrating product features that capture emotion.

The purpose of this study is to examine the facilitating and inhibiting influence of team-level negative affectivity and conscientiousness on a dyad of emergent states, adopting and comparing both the composition and compilation perspectives.

Data were collected over three time points from 410 undergraduate students nested within cross-functional project teams (N = 62). The data, including individual self-reports and judges’ ratings of team performance, were aggregated to the team-level using both composition (mean) and compilation (skewness) approaches.

The findings indicate that mean-levels of negative affectivity were associated with decreased psychological safety. The use of skewed conscientiousness counterintuitively suggests too many highly conscientious members can also be detrimental to psychological safety. Psychological safety influences team potency and ultimately performance.

The results of this study highlight that the aggregation approach used is important. For example, the use of skewed (but not mean-level) conscientiousness brought an undetected and counterintuitive relationship to light. Future research should use compilation approaches in addition to composition approaches.

Diversity, equity and inclusion ideology is the latest appeal of individual compassion, institutional legitimacy and enlightened society. Specific to higher education, diversity, equity and inclusion is an honorable ideology, value and mission. This paper aims to (1) empirically recognize the differences in the level of importance between the university’s and faculty’s diversity, equity and inclusion initiatives, (2) identify the diversity, equity and inclusion outcomes, such as university brand image and student intention to engage postgraduation, (3) uncover the moderating role of university brand preference attainment and (4) validate the mediating role of student identification in diversity, equity and inclusion literature.

A survey of 1,027 usable responses was employed to perform two moderations, four mediations and two confidence interval analyses.

The university’s diversity, equity and inclusion initiatives are significantly more rewarding than the faculty’s diversity, equity and inclusion initiatives for the university brand image and students’ intention to engage postgraduation. Findings uncover the mediating role of student identification and the moderating role of brand preference attainment.

Focusing on diversity, equity and inclusion at the institutional level is more impactful than at the faculty level. In industries where frontline employees have significant autonomy, such as higher education, the positive brand performance outcomes are related to the faculty’s diversity, equity and inclusion awareness, not the faculty’s diversity, equity and inclusion advocacy. Diversity, equity and inclusion initiatives should align with the student’s shared values.

The study relies on institutional theory to underscore the asymmetric importance of the university’s and faculty’s diversity, equity and inclusion initiatives in achieving perceived brand image and engagement.

Warren Buffett asserted that the greatest issue confronting American business and the economy is rising health-care costs, which have risen to 17% of gross domestic product. Public policymakers, health-care providers and other stakeholders grapple with cost-containment and increased health-care delivery efficiencies. There exists a paucity of theory-driven research addressing how information technology vis-à-vis electronic health records (EHR) may supply a managerial mechanism for increasing bottom-line hospital performance, thereby attaining competitive advantage.

A systematic interdisciplinary literature review motivated by resource advantage theory (RAT) offers a conceptual foundation for analyzing the financial, informational and physical workflows that are core elements of supply chain management in a hospital.

RAT links how EHR impacts profitability, competitive advantage and macromarketing factors in hospital supply chains. The literature review provides a research synthesis of the implementation and adoption of EHR to reveal its impact on a hospital’s competitive advantage. Although legislative initiatives like the 2009 Health Information Technology for Economic and Clinical Health Act and the Affordable Care Act encourage EHR adoption, there remains a reluctance for hospitals to do so.

The extant literature precedes the relevant legislation, has incomplete data or focuses solely on patient outcomes.

Studies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.

LSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.

Based on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.

The technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.

The novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.

To the best of the authors’ knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. The findings of this study can also help organizations to find a better and effective approach for resume screening and evaluation.

The purpose of this study is to investigate forecast models using data provided by the Texas Commission on Environmental Quality (TCEQ) to monitor and develop forecast models for air quality management.

The models used in this research are the LDF (Fisher Linear Discriminant Function), QDF (Quadratic Discriminant Function), REGF (Regression Function), BPNN (Backprop Neural Network), and the RBFN (Radial Basis Function Network). The data used for model evaluations span a 12‐year period from 1990 to 2002. A control chart of the data is also examined for possible shifts in the distribution of ozone present in the Houston atmosphere during this time period.

Results of this research reveal variables that are significantly related to the ozone problem in the Houston area.

Models developed in this paper may assist air quality managers in modeling and forecasting ozone formations using meteorological variables.

This is the first study that has extensively compared the efficiency of LDF, QDF, REGF, BPNN and RBFN forecast models used for tracking air quality. Prior studies have evaluated Neural Networks, ARIMA and regression models.

Understanding large amounts of information and efficiently using that information in improved decision making has become increasingly challenging as businesses collect terabytes of data. Businesses have turned to emerging technology including neural networks, symbolic learning, and genetic algorithms. In the current study, four classification methods were compared using results from an Indonesian contraceptive‐method preference survey. The four methods are linear discriminant analysis, quadratic discriminant analysis, backpropagation neural networks, and modular neural networks. The modular neural network is a more complex and less frequently used neural network model. This comparative study gives insight into its performance on classifying observations from a challenging data set, the 1987 National Indonesia Contraceptive Prevalence Survey.

Project managers in information systems play a central role in the development, maintenance, and enhancement of software. Software metrics assist these managers in identifying opportunities for process improvement and help quantify software characteristics. Weaknesses in the traditional approaches to measuring reliability have led to the development of software metrics. The interpretation of software metrics can be critical to making effective responses in the management information systems’ decision‐making processes. This paper gives insight into the use and understanding of some software metrics.

With the growth of worldwide e‐commerce, companies are increasingly targeting foreign online consumers. However, there is a dearth of evidence as to whether global consumers prefer to browse and buy from standardized global web sites or web sites adapted to their local cultures. This study provides evidence from five different countries as to whether global consumers prefer local web content or standardized web content. The study also measures how the degree of cultural adaptation on the web affects consumer perception of site effectiveness.