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Configuring strategies to ensure a health service provider’s resilience when extreme disruptions occur is not simple. Optimal configuration in such circumstances is rare. Therefore, this research has relied on the dynamic capability view (DCV) to develop a decision-support framework for configuring resilience strategies that will mitigate the worst challenges and improve the performance of health service providers during “extreme” disruptive events.

The research adopted a multi-study, multi-method approach comprising interviews, quality function deployment (QFD), and fuzzy set qualitative comparative analysis (fsQCA).

The findings reveal that, during a crisis, standalone resilience strategies are not enough. To guarantee performance, healthcare services require a combination of resilience strategies and a negation of challenges.

This research extends our current knowledge of healthcare operational management by offering optimal configurations of resilience strategies to manage performance during extreme disruptions. Thus, it offers strategic insights into how health-service managers can be more resilient during a crisis.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry.

The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company.

Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources.

This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

Small and medium-sized enterprises (SMEs) are a major source of employment and revenue growth in developing nations like India, but they also face challenges from resource shortages, shifting consumer demand and heightened competition. This research aims to discover the aspects that enhance SMEs' competitiveness and performance.

By analyzing literature and consulting experts, 10 factors that boost a firm's competitiveness were identified. The total interpretive structural modeling (TISM) method was then used to determine their interaction and structural hierarchy. Neutrosophic-MICMAC analysis was employed to assess the driving-dependence power of each factor.

The study discovered that the factor, namely “entrepreneurial orientation,” was found to be a significant one. “Manufacturing strategy” was found to be extremely dependent on the remaining competitive advantage factors.

This SME-focused framework can be adopted by large businesses to enhance organizational performance by focusing on critical factors. The study depends on experts' judgment, which might be biased. Findings will assist SMEs in identifying significant factors influencing competitive advantage and relationships, increasing awareness of factors contributing to competitive edge.

The results of the research may encourage SME sector managers and practitioners to prioritize the factors that contribute to a firm's competitive advantage.

The majority of research on SME competitive advantage focuses on individual aspects. To add to the body of knowledge on the subject, this study applies the TISM technique to Indian SMEs to identify the contextual interactions among factors that increase long-term competitiveness.

This study responds to the calls from the literature on identifying interactions among the sustainable supply chain management (SSCM) drivers, which influence focal firms’ SSCM decisions. It also determines how the effect of SSCM drivers differs across the upstream and downstream supply chains (SCs) entities of fast-moving consumer goods (FMCGs) companies.

We employ a multi-method design encompassing three studies: study 1 (multiple-case study), study 2 (quantitative survey), and study 3 (multiple-criteria decision-making or MCDM modeling).

The results show that the external drivers such as customer pressure, competition, and supplier pressure and internal drivers such as top management commitment interact to influence the adoption of SSCM practices, and this interaction is diverse across upstream and downstream SC entities of the FMCG sector. The study provides empirical evidence of relationships among the SSCM drivers, which influence SSCM decisions.

Understanding these interactions will help managers derive strategies to manage the overall SSCM ecosystem and recognize the multiplier effects of upstream to downstream and vice versa.

The number of university students seeking mental health support is rapidly increasing. To provide additional psychological support to students accessing well-being services, this study aimed to pilot a mental health Web application (app) called Orpheus®.

Guided by student consultations, a multi-methods approach was adopted, including an examination of in-app data, chart reviews of routinely collected student information and interviews with mental health practitioners. Usage data were analysed descriptively. Changes in mental health outcomes were examined using means, standard deviations and reliable change indices for anxiety and depression scores. Inductive and deductive thematic analysis was conducted on qualitative data from staff interviews and student feedback.

A total of 26 students registered an account with 39 completed app visits. On 37 of the 39 (94.9%) occasions, students reported reductions in the intensity of unwanted negative emotions. Statistically significant reductions in the average pooled anxiety and depression scores were observed. Of the 15 students who completed pre- and post-routine outcome measures, between 20% and 60% showed reliable and meaningful symptom improvements. Students reported that the app was helpful and easy to set up and use, with no adverse events. Practitioners highlighted barriers and facilitators related to the technology features, situational contexts and individual differences.

Integrating Orpheus in real-world settings resulted in promising implementation processes, potential for future uptake and positive outcomes. However, ongoing research, staff training and app testing are needed to further improve the implementation processes for digital mental health interventions.

While stress, anxiety and depression rank as the second leading cause of work-related ill health in the UK construction sector, there exists a scarcity of empirical studies explicitly focused on investigating the sources of occupational stress among construction workers and professionals at both the construction project and supply chain levels. This study seeks to identify and investigate the primary stressors (sources of stress) in UK construction projects and to propose effective strategies for preventing or reducing stress in this context.

The study adopted a qualitative multi-methods research approach, comprising the use of a comprehensive literature review, case study interviews and a focus group. It utilised an integrated deductive-inductive approach theory building using NVivo software. In total, 19 in-depth interviews were conducted as part of the case-study with a well-rounded sample of construction professionals and trade supervisors, followed by a focus group with 12 policy influencers and sector stakeholders to evaluate the quality and transferability of the findings of the study.

The results reveal seven main stressors and 35 influencing factors within these 7 areas of stress in a UK construction project, with “workflow interruptions” emerging as the predominant stressor. In addition, the results of the focus-group, which was conducted with a sample of 12 prominent industry experts and policy influencers, indicate that the findings of the case study are transferrable and could be applicable to other construction projects and contexts. It is, therefore, recommended that these potential stressors be addressed by the project team as early as possible in construction projects. Additionally, the study sheds empirical light on the limitations of the critical path method and identifies “inclusive and collaborative planning” as a proactive strategy for stress prevention and/or reduction in construction projects.

The findings of this study are mainly based on the perspectives of construction professionals at managerial and supervisory levels. It is, therefore, suggested that future studies are designed to focus on capturing the experiences and opinions of construction workers/operatives on the site.

The findings from this study have the potential to assist decision-makers in the prevention of stress within construction projects, ultimately enhancing workforce performance. It is suggested that the findings could be adapted for use as Construction Supply Chain Management Standards to improve occupational stress management and productivity in construction projects. The study also provides decision-makers and practitioners with a conceptual framework that includes a list of effective strategies for stress prevention or reduction at both project and organisational levels. It also contributes to practice by offering novel ideas for incorporating occupational stress and mental health considerations into production planning and control processes in construction.

To the best of the authors’ knowledge, this is the first, or one of the very few studies, to explore the concept of occupational stress in construction at the project and supply chain levels. It is also the first study to reveal “workflow” as a predominant stressor in construction projects. It is, therefore, suggested that both academic and industry efforts should focus on finding innovative ways to enhance workflow and collaboration in construction projects, to improve the productivity, health and well-being of their workforce and supply chain. Further, it is suggested that policymakers should consider the potential for incorporating “workflow” into the HSE's Management Standards for stress prevention and management.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

Safety program initiatives in the construction industry remain below the desired level, especially in developing countries, and the reasons for low adoption are not well established. This study aims to investigate and quantify the impact levels of the key barriers to safety program initiatives in the construction industry in Iraq.

The study used a multi-method research design to assess the barriers to safety program initiatives in the construction industry in Iraq. Initially, a comprehensive literature review was conducted to identify potential barriers. Following this, semi-structured interviews with construction professionals were conducted to gather in-depth insights. Thematic analysis was then applied to analyze the results of these interviews. Subsequently, a questionnaire survey was developed based on the findings from the literature review and interviews to quantitatively measure the impact of the identified barriers among a broader group of construction professionals. Finally, a fuzzy synthetic evaluation (FSE) was used to assess the overall impact of these barriers comprehensively.

The results showed that the top three key barriers to the implementation of construction safety programs in Iraq include tight project schedules, inadequate commitment to safety and the lack of safety rules and policies. A FSE indicated that each group of barriers, including unsupportive industry norms, poor safety awareness, an unconducive work climate and poor governance, significantly hinders safety program implementation in the Iraqi construction industry.

The study was conducted in the construction industry of Iraq, specifically focusing on Baghdad, Anbar, Basra and Erbil governorates, as these cities are prominent within the country. In addition, this research exclusively captured the perspectives of construction consultants, contractors and clients.

The findings suggest that improving supportive and safety-aware industry norms, increasing safety awareness, generating a safe work climate and culture and improving safety governance can significantly influence the widespread adoption of safety programs in the construction industry. To the best of the authors’ knowledge, this constitutes the first study to quantify the impact levels of barriers to construction safety program initiatives in developing countries. It provides additional insight to promote a safer construction industry. Moreover, the findings can notably be used by construction practitioners and policymakers to develop and successfully implement safety programs in construction.

This study aims to develop an assessment model for identifying the primary barriers to safety program initiatives within the construction industry of developing countries, exploring deeper into the barriers that impede the adoption of safety programs in construction, and providing valuable insights that could serve as a foundation for crafting strategies to enhance safety program initiatives within the industry.

Despite the current attention toward the concept of data culture, a commonly accepted scope and definition is currently lacking. Addressing this conceptual fuzziness would be beneficial to pursue the development of knowledge on data culture in the public sector. The research aims at advancing theory by building a novel conceptualization of the constituent elements of data culture in local governments and their relationships.

For this purpose, the authors used a multi-method research design. More precisely, the authors conducted 12 semi-structured interviews with mayors and heads of administration from local governments, and a document analysis. The authors inductively mapped the findings to an existing heuristic featuring seven levels of data culture and extracted relationships between these levels.

The authors find several elements belonging to the data culture of local governments for each level of the existing generic heuristic and identify 24 influence relationships between these levels. The authors integrate these findings into the data culture model, which conceptualizes data culture in local governments.

The data culture model provides a strong theoretical basis for researchers to position their research and further advances knowledge on this still elusive concept. Practitioners can use the data culture model as a reflective tool to understand which elements impacted their current data behavior.

To the best of the authors’ knowledge, this is the first work to provide a conceptualization of data culture in local governments at this level of depth, and to conceptualize relationships between constituent elements of data culture.