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Technology-enabled healthcare focuses on providing better information flow and coordination in healthcare operations. Technology-enabled health services enable hospitals to manage their resources effectively, maintain continuous patient engagement and provide seamless services without compromising their perceived quality.

This study investigates the role of technology-enabled health services in improving perceived healthcare quality among patients. Data are collected from the users (n = 418) of health platforms offered in multi-specialty hospitals. Multiple learners are employed to accurately represent the users' perceived quality regarding the perceived usefulness of the features provided via these digital health platforms.

The best-fitted model using a decision tree classifier (accuracy = 0.86) derives the accurate significance of features offered in the digital health platform in fostering perceived healthcare quality. Diet and lifestyle recommendations (30%) and chatting with health professionals (11%) are the top features offered in digital health platforms that primarily influence the perceived quality of healthcare among users.

The predictability of perceived quality with the individual features existing in the digital health platform, the significance of the features on the perceived healthcare quality and the prediction rules showing the combined effect of features on healthcare quality can help healthcare managers accelerate digital transformation in hospitals by improving their digital health platform, designing and offering new health packages while strengthening their e-infrastructure.

The study represents perceived healthcare quality with the features offered in digital health platforms using machine learners based on users' post-pandemic experience. By advancing digital platforms with more patient-centric features using emerging technologies, this model can further foresee its impact on the perceived quality of healthcare, offering valuable directions to healthcare service providers. The study is limited to focusing on digital health platforms that can deal with people's general healthcare needs.

The hiring process for information technology (IT) personnel, given its high stakes and intricacies, demands an objective, methodical and nuanced approach. The multifaceted nature of IT roles necessitates a comprehensive evaluation methodology to identify and prioritize pertinent skills and competencies. Therefore, this study aims to devise and implement a robust multi-criteria decision-making model integrating the fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) for assessing and ranking the skills required for IT personnel.

This study integrates expert opinions and insights from the literature to identify five key criteria and 21 sub-criteria essential for IT personnel selection. The fuzzy Analytic Hierarchy Process (AHP) was applied to determine the relative importance of each criterion and sub-criterion. These weighted criteria were then utilized in the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to systematically rank five candidate alternatives based on their suitability for IT roles.

Results identified “Objective of factors” to be the most significant criteria, whereas “Assessment Centre Score” as the most viable sub-criterion. Using the integrated model results of fuzzy AHP-TOPSIS candidate, A3 was the most fitted IT personnel, whereas Candidate A4 has emerged as the poor/ unsuitable fit.

This research helps firms and policymakers use their limited resources efficiently. Furthermore, this study acts as a guideline for future researchers to empirically investigate the impact of listed skills on employee performance.

This study is a pioneering effort in integrating fuzzy AHP and TOPSIS to address the challenges in IT personnel selection, catering to the intricate layers of human evaluative judgments. Its extended applicability and innovative approach makes it a valuable contribution to the existing body of knowledge, serving as a benchmark for future endeavors in related domains.

This article reports on the development and evaluation of an online conflict coaching system. The authors develop and test a broadly applicable yet tailored Conflict Coach that extends the current practice of coaching. It provides diagnostics and advice across several emotions and dimensions of conflict drawn from research and conflict management practice.

The coach engages coachees through five emotions, divided into three levels of conflict intensity and ten dimensions of conflict to deliver analysis and advice “packets” derived from both research findings and practice. A Prolific survey used as a proxy for the coaching system was administered to 341 respondents. A variety of closed and open-ended questions were asked about the respondents’ chosen conflict, the packets of advice and about their overall experience.

The coach was judged as somewhat helpful, quite understandable, quite implementable and providing sufficient advice. A sampling of the findings signals the importance of dealing with anger and dislike and finding ways to communicate more effectively. Longer-term assessments indicated that the advice continued to be helpful and worth recommending to others. There are opportunities to further develop the prototype including through field testing and use of artificial intelligence.

To the best of the authors’ knowledge, the Conflict Coach is a first attempt to provide a widely available yet tailored online system for responding to conflict. It promises positive social and practice implications that extend upon and complement existing conflict management through increased accessibility to information and advice based on published research findings and practice. Research opportunities accompany its possible further development.

This work provides an overview of academic articles on the application of artificial intelligence (AI) in healthcare. It delves into the innovation process, encompassing a two-stage trajectory of exploration and development followed by dissemination and adoption. To illuminate the transition from the first to the second stage, we use prospect theory (PT) to offer insights into the effects of risk and uncertainty on individual decision-making, which potentially lead to partially irrational choices. The primary objective is to discern whether clinical decision support systems (CDSSs) can serve as effective means of “cognitive debiasing”, thus countering the perceived risks.

This study presents a comprehensive systematic literature review (SLR) of the adoption of clinical decision support systems (CDSSs) in healthcare. We selected English articles dated 2013–2023 from Scopus, Web of Science and PubMed, found using keywords such as “Artificial Intelligence,” “Healthcare” and “CDSS.” A bibliometric analysis was conducted to evaluate literature productivity and its impact on this topic.

Of 322 articles, 113 met the eligibility criteria. These pointed to a widespread reluctance among physicians to adopt AI systems, primarily due to trust-related issues. Although our systematic literature review underscores the positive effects of AI in healthcare, it barely addresses the associated risks.

This study has certain limitations, including potential concerns regarding generalizability, biases in the literature review and reliance on theoretical frameworks that lack empirical evidence.

The uniqueness of this study lies in its examination of healthcare professionals’ perceptions of the risks associated with implementing AI systems. Moreover, it addresses liability issues involving a range of stakeholders, including algorithm developers, Internet of Things (IoT) manufacturers, communication systems and cybersecurity providers.

We examine how formal institutions influence the employment growth ambitions of entrepreneurial firms with an explicit focus on how their influence varies for early-stage versus established entrepreneurial firms.

We develop four moderation hypotheses based on a review of research on entrepreneurial growth ambitions and formal institutions. Then, we use comprehensive institutional and entrepreneurship performance data from three large international data sources to examine the hypothesized relations.

Our findings suggest that effective formal institutions promote employment growth for established entrepreneurs in particular. Moreover, we found that ineffective formal institutions hinder employee growth for early-stage firms in particular.

We offer several original implications for policymakers and ecosystem builders seeking to support and encourage the early-stage entrepreneurship that accounts for the majority of new job growth in socioeconomic systems.