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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.

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.

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.

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.

Protection motivation theory (PMT) explains that the intention to cope with information security risks is based on informed threat and coping appraisals. However, people cannot always make appropriate assessments due to possible ignorance and cognitive biases. This study proposes a research model that introduces four antecedent factors from ignorance and bias perspectives into the PMT model and empirically tests this model with data from a survey of electronic waste (e-waste) handling.

The data collected from 356 Chinese samples are analyzed via structural equation modeling (SEM).

The results revealed that for threat appraisal, optimistic bias leads to a lower perception of risks. However, factual ignorance (lack of knowledge of risks) does not significantly affect the perceived threat. For coping appraisal, practical ignorance (lack of knowledge of coping with risks) leads to low response efficacy and self-efficacy and high perceptions of coping cost, but the illusion of control overestimates response efficacy and self-efficacy.

First, this study addresses a new type of information security problem in e-waste handling. Second, this study extends the PMT model by exploring the roles of ignorance and bias as antecedents. Finally, the authors reinvestigate the basic constructs of PMT to identify how rational threat and coping assessments affect user intentions to cope with data security risks.

This study aims to assess the effect of peer evaluations on team-level effort, productivity, motivation and overall team performance.

This study explores the impact of a peer evaluation system on 895 multicultural and transnational global virtual teams (GVTs) composed of 5,852 university students from 130 different countries. The study uses a quasi-experiment in which the group project is implemented under two conditions over two sequential iterations. In the first condition, team members do not receive peer evaluation feedback during the project. In the second condition, participants completed detailed peer evaluations of their team members and received feedback weekly for eight consecutive weeks.

Results suggest that when peer evaluations are used in GVTs during the project, teams show: higher levels of group effort; lower levels of average productivity and motivation; and no clear evidence of improved team performance. Results cast doubts on the benefits of peer evaluation within GVTs as the practice fails to reach its main objective of improving team performance and generates some negative internal dynamics.

The major implication of the study for managers and educators using GVTs is that the use of peer evaluations during the course of a project does not appear to improve objective team performance and reduces team motivation and perception of productivity despite increases in teams’ perceptions of effort and performance.

This study contributes to the scanty literature regarding the impact of peer evaluation systems on group-level dynamics and performance outcomes.