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Since its inception, CASE (computer‐aided software engineering) tools have been hailed as the “silver bullet” of applications development. Although these tools have failed to live up to such an advance billing (as do most “fix‐all” solutions), these products remain a viable option for practitioners of modern applications development. This study comments on the use of CASE in modern IS installations, using the results of an in‐depth survey completed by 226 IS professionals from over 30 Fortune 500‐type companies. First, the study identifies the most popular features possessed by respondents’ CASE toolsets. Next, we comment on the “gap” perceived to exist between CASE features actually possessed, and those features needed by these professionals in the performance of their job duties. Finally, implications for practice and research are presented.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

Product data management (PDM) is an essential tool that assists engineers and others manage both engineering data and the product development process. This study aims to evaluate the advantages and justify the implementation a PDM system at the broadband wireless group (BWG) of a major telecommunications firm located in Pittsburgh, PA. Examines results of previous PDM implementations and obtains primary data via a department staff survey regarding time spent searching for product or part information, product development cycle time, and information exchange or sharing. Illustrates that PDM implementations have led to significant improvement in all facets of product design, documentation, introduction, manufacturing, and support. These improvements will result in an increase in design reuse and a decrease in product development costs and the number of engineering changes.

The impact of technology on the health and well-being of workers has been a topic of interest since computers and computerized technology were widely introduced in the 1980s. Of recent concern is the impact of rapid technological advances on individuals’ psychological well-being, especially due to advancements in mobile technology that have increased many workers’ accessibility and expected productivity. In this chapter we focus on the associations between occupational stress and technology, especially behavioral and psychological reactions. We discuss some key facilitators and barriers associated with users’ acceptance of and engagement with information and communication technology. We conclude with recommendations for ongoing research on managing occupational health and well-being in conjunction with technological advancements.

The purpose of this paper is to propose a predictive model that could replace lawyers’ annual performance rankings and inform talent management (TM) in law firms.

Eight years of performance rankings of a sample of 140 lawyers from one law firm are used. Artificial neural networks (ANNs) are used to model and simulate performance rankings over time. Multivariate regression analysis is used to compare with the non-linear networks.

With a lag of one year, performance ranking changes are predicted by the networks with an accuracy of 71 percent, over performing regression analysis by 15 percent. With a lag of two years, accuracy is reduced by 4 percent.

This study contributes to the literature of TM in law firms and to predictive research. Generalizability would require replication with broader samples.

Neural networks enable extended intervals for performance rankings. Reducing the time and effort spent benefits partners and lawyers alike, who can instead devote time to in-depth feedback. Strategic planning, early identification of the most talented and avenues for tailored careers become open.

This study pioneers the use of ANNs in law firm TM. The method surpasses traditional static study of performance through its use of non-linear simulation and prediction modeling.

The purpose of this paper is to analyze 86 information technology (IT) internship postings to discern the extent to which the intended outcomes matched professional standards for four-year IT programs.

The researchers text mined specified skills from 86 internship postings and compared them to the competencies outlined in the ACM/IEEE Body of Knowledge.

Results indicated that students can expect to gain experience and exposure to both technical and general competencies. Though research and policy relating to technical fields have emphasized professional competencies such as teamwork, communication, and professionalism, this analysis suggested that the internship postings greatly emphasized technical skills at the expense of general competencies.

The most frequently occurring competencies suggest future research opportunities for considering contextual factors of internship sites. The researchers conclude with implications for using text mining as a tool for comparing internship intent vs outcomes as well as suggestions for policies, standards, and curricula worthy of further exploration.

Employers, educators, and professionals agree that internships offer a promising means to link course content and practical workplace skills, especially in technical fields like IT. However, less clear are the ingredients of effective IT internships.

With a focus on the evolution of human resource management (HRM) quantification over 2000–2020, this study addresses the following questions: (1) What are the data sources used to quantify HRM? (2) What are the methods used to quantify HRM? (3) What are the objectives of HRM quantification? (4) What are the representations of quantification in HRM?

This study is based on an integrative synthesis of 94 published peer-reviewed empirical and non-empirical articles on the use of quantification in HRM. It uses the theoretical framework of the sociology of quantification.

The analysis shows that there have been several changes in HRM quantification over 2000–2020 in terms of data sources, methods and objectives. Meanwhile, representations of quantification have evolved relatively little; it is still considered as a tool, and this ignores the possible conflicts and subjectivity associated with the use of quantification.

This literature review addresses the use of quantification in HRM in general and is thus larger in scope than previous reviews. Notably, it brings forth new insights on possible differences between the main uses of quantification in HRM, as well as on artificial intelligence and algorithms in HRM.

Information systems (IS) managers have become key senior executives for organising the IT resources for delivering support to businesses. Understanding characteristics of IS managers’ employment positions is hence an increasingly important topic in computer personnel research. An investigation in Singapore that included a job advertisement analysis, surveys and case studies was thus conducted to investigate such aspects. This article presents the findings of the job advertisement analysis concerning what kinds of IS managers the market is seeking and what are the basic conditions for such management positions. The literature in this area asserts that job advertisements represent firms’ wishes and the nature of the conditions required of different IS personnel. The results of this analysis therefore reflect a collective market perspective about the changing IS managerial workplace. The results of the analysis benefit both firms and IS employees in formulating personnel development plans and actions, and raise issues for further research.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

This study aims to identify how objective indicators of destination country risk differentiate business study abroad programs from those in other academic disciplines.

The authors trained a neural network model on six years of student-initiated inquiries about study abroad programs at a large US university. The model classified business versus nonbusiness study abroad programs using objective measures of destination country risk as the primary inputs.

The model correctly classifies business and nonbusiness study abroad programs with over 70% accuracy. Business programs were found to be 20% less likely to include destinations where the Centers for Disease Control and Prevention recommend nonroutine vaccinations and favor countries with higher Global Peace Index scores.

These results underscore the need to consider destination country risk in the design and administration of study abroad programs. An understanding of student preferences for lower risk destinations can contribute to improved planning, execution and student experiences in these programs.

Better planning and management of study abroad programs based on understanding of destination country risk can lead to enhanced student safety and experiences.

This study offers a unique perspective on understanding study abroad programs by focusing on objective measures of destination country risk rather than risk perceptions. It also is, to the best of the authors’ knowledge, the first to use a neural network to classify study abroad programs as business versus nonbusiness using objective measures of country-specify risk indicators.