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A descriptive case study approach was adopted to examine employees' perceptions of the prevalence and usefulness of wellness programs. Relying on Centers for Disease Control and Prevention Health ScoreCard (CDC HSC), this study aims to assess the prevalence of worksite wellness programs, policies and benefits in general and incorporated employee perspectives to contextualize the CDC HSC wellness assessments.

The authors first compared the CDC HSC assessments to evaluate the effectiveness of wellness programs in 20 select organizations. Follow-up employee interviews (n = 25) were conducted to contextualize the CDC HSC assessments.

A variety of wellness programs are likely to increase employee engagement and participation when organizations adopt a bundling approach to combine wellness policies, incentives and an array of wellness programming opportunities that encourage and incentivize employees’ health promotion behaviors.

Future researchers might examine health metrics, in terms of dollars, doctor visits or biometrics before and after the implementation of a wellness program or paid wellness coordinator. Because this study interviewed employees and not members of executive leadership or finance and accounting, financial metrics were not available or the focus of this study. The inclusion of leaders and directors of wellness initiatives would offer additional ways for examining the impact of wellness initiatives on employee behaviors on organizational outcomes.

Nutrition, weight management and chronic disease management were identified as major challenges impacting the health of employees. Even when organizations reported robust scores in their CDC HSC assessments, employers identified these three areas as critical for sustaining the health and well-being of their employees. Finally, the issue of employee safety was a top priority for all organizations regardless of how they scored on their CDC HSC assessments.

The authors suggest that when high-impact wellness practices are linked to organizational supports in the workplace, these efforts are likely to have more positive effects on both employee outcomes and organizational outcomes. A regular routine of checking on wellness issues can help keep potential problems from going unnoticed. An example of this is a reminder to stretch at a morning meeting or during work hours as a reminder to stay focused on health and well-being.

The authors aggregated the organizational assessments for different wellness interventions and compared the scores (falling above or below) with standardized CDC wellness scores. The incorporation of the CDC scorecard ensured a standardized and evidence-based evaluation of workplace wellness programs. This additional step informed the interview guide and follow-up with employees who offered recommendations for how organizations could enhance their wellness programs and policies.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

The objective of this research is to examine the accelerated adoption mechanisms of emerging technologies in information systems. Its goal is to comprehend the drivers behind the prompt assimilation of technology trends such as TikTok, ChatGPT, mobile payment schemes, cryptocurrency and VR.

The study follows the systematic literature review methodology (using the PRISMA protocol to guide the selection of scholarly materials from Google Scholar, Scopus and Springer). Specifically, the research draws on identified literature on the adoption trajectories of technologies (ChatGPT, TikTok, cryptocurrency, mobile payment systems, and virtual reality) to systematically assess pertinent insights, and draws on theoretical lenses of Disruptive Innovation Theory to reach interpretations.

The study indicates that the prompt assimilation of technology is shaped by several variables such as user-centered design, network effects, content powered through algorithms, viral trends, ease-of-use and accessibility features, engagement levels and retention rates.

The selection of specific platforms may limit the generalizability of findings.

The emergence of new technologies is causing a shift in societal behaviors and norms, which has significant social implications. While platforms such as TikTok offer opportunities for community-building, there are concerns regarding digital divide and privacy issues that need to be addressed. So understanding the impact of these changes becomes vital for achieving fairness in access and making technology's potential transformation practicalized effectively.

This research enhances the current body of literature by presenting a thorough examination of the non-linear patterns involved in adopting advanced technologies. By combining knowledge from numerous fields, this study delivers an integrated comprehension regarding what factors prompt swift adoption.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.

This study aims to understand the progressive marketization of health data collection and use, through a study of its historical development in France, from the 1930s to the present day.

The authors collected a set of legal, institutional, political and media data. These came from the INA (National Audiovisual Institute), the French national newspaper websites and the websites legifrance.gouv.fr and vie-publique.fr. The authors then conducted a thematic content analysis.

The study results highlight the changes in the health-care system related to the increased use of data in France over three major periods. The first period – 1930s to 1980s – is marked by the creation of the French social security system to collect large sets of data to better manager people’s health care. The second period – 1980s to 2000s – is characterized by the adoption and assimilation of tools to manage patient data through several national and European regulations. The last period – 2000s to the present – saw the introduction of measures in favor of the digitalization of health care, and consequently of data, in parallel with the advancement of digital technologies in general. The institutional dynamics in healthcare have evolved with the nature of the actors and their practices, in connection with new perceptions about health data.

This research sheds light on the historical transformation of health data collection and use in France, revealing the involvement of diverse stakeholders, the discourses driving data development and the need for regulation. It exposes the dual nature of health data collection and use, initially sanctioned by the state and public entities but later exploited for private interests.