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This study aims to assess the essential elements of internal organisational capability that influence the cybersecurity effectiveness of a construction firm. An extended McKinsey 7S model is used to analyse the relationship between a construction firm's cybersecurity effectiveness and nine internal capability elements: shared values, strategy, structure, systems, staff, style, skills, relationships with third parties and regulatory compliance.

Based on a quantitative research strategy, this study collected data through a cross-sectional survey of professionals working in the construction sector in the United Kingdom (UK). The collected data was analysed using descriptive and inferential statistical methods.

The findings underlined systems, regulatory compliance, staff and third-party relationships as the most significant elements of internal organisational capability influencing a construction firm's cybersecurity effectiveness, organised in order of importance.

Future research possibilities are proposed including the extension of the proposed diagnostic model to consider additional external factors, examining it under varying industrial relationship conditions and developing a dynamic framework that helps improve cybersecurity capability levels while overseeing execution outcomes to ensure success.

The extended McKinsey 7S model can be used as a diagnostic tool to assess the organisation's internal capabilities and evaluate the effectiveness of implemented changes. This can provide specific ways for construction firms to enhance their cybersecurity effectiveness.

This study contributes to the field of cybersecurity in the construction industry by empirically assessing the effectiveness of cybersecurity in UK construction firms using an extended McKinsey 7S model. The study highlights the importance of two additional elements, third-party relationships and construction firm regulatory compliance, which were overlooked in the original McKinsey 7S model. By utilising this model, the study develops a concise research model of essential elements of internal organisational capability that influence cybersecurity effectiveness in construction firms.

This paper develops a decision-analysis model to prioritize and select the site to establish a new hospital over different indicators such as cost, market conditions, environmental factors, government factors, locations and demographics. In this way, an integrated model is proposed under the intuitionistic fuzzy information (IFI), the standard deviation (SD), the rank-sum (RS) and the measurement of alternatives and ranking using the compromise solution (MARCOS) approach for ranking hospital sites (HSs).

The IF-SD-RS model is presented to obtain the combined weight with the objective and subjective weights of diverse sub-criteria and indicators for ranking sites to establish the hospital. The IF-MARCOS model is discussed to prioritize the various sites to establish the hospital over several crucial indicators and sub-criteria.

The authors implement the developed model on a case study of HSs assessment for the construction of new hospital. In this regard, inclusive set of 6 key indicators and 18 sub-criteria are considered for the evaluation of HSs. This study distinguished that HS (h₂) with combined utility function 0.737 achieves highest rank compared to the other three sites for the given information. Sensitivity analysis is discussed with different parameter values of sub-criteria to examine how changes in weight parameter ratings of the sub-criteria affect the prioritization of the options. Finally, comparative discussion is made with the diverse extant models to show the reasonability of the developed method.

This study aims to develop an original hybrid weighting tool called the IF-SD-RS model with the integration of IF-SD and IF-RS approaches to find the indicators' weights for prioritizing HSs. The developed integrated weighting model provides objective weight by IF-SD and subjective weight with the IF-RS model. The model presented in the paper deals with a consistent multi-attribute decision analysis (MADA) concerning the relations between indicators and sub-criteria for choosing the appropriate options using the developed IF-SD-RS-MARCOS model.

This paper aims to provide insights into service innovation (SI) during the COVID-19 crisis and its potential impact on tourism development in the medium-to-long term. The pandemic had a devastating effect on the industry, requiring immediate mitigation. It is yet to fully establish the impact of SI in the face of the COVID-19 volatility, uncertainty, complexity and ambiguity (VUCA). This study discusses the potential link between SI and COVID-19 crisis mitigation and offers recommendations for tourism recovery.

This paper synthesizes empirical evidence on post-crisis tourism SI using a theory-based general literature review approach.

COVID-19 crisis spun various forms of SI, which emerged as a conventional solution to crisis prevention, encompassing the management of crisis-time competitiveness, revenue deficits and risk perception. However, resistance to innovative services is linked to situational conditions.

COVID-19 is an unprecedented crisis. Therefore, this study serves as a primer for further inquiry into SI. For instance, areas such as governance in tourism innovation and consumers' inclination toward innovation-driven services are underexplored.

SI acts as a situational facilitator, but its characteristics can impede or facilitate adoption. Moreover, the irrelevance of innovations in some environments is evidenced. Thus, practitioners must adopt a responsive learning approach in SI adoption. To mitigate the COVID-19 impacts, reconfiguration in SI, recovery marketing strategy, knowledge gap and governance will be critical interventions.

This paper is one of the first comprehensive discussions on the potential role of SI in mitigating the impact of COVID-19 on the THI.

Following the job demands-resources theory, this study investigates the role of female managers in enhancing employee well-being in terms of psychological health via workplace resources.

To accomplish this objective, we conducted a comprehensive literature review to identify key IPS. Subsequently, a fuzzy-based algorithm was employed to prioritize these skills. Following this, we developed an algorithm based on Extreme Gradient Boosting (XGBoost) to predict the quality of workers’ IC. The efficacy of the XGBoost model was assessed by applying it to three real-life construction projects.

Upon application of the model to the case studies, we made the following conclusions: (1) “Leadership Style,” “Listening,” “Team Building” and “Clarifying Expectations” emerged as significant skills and (2) the model accurately predicted workers’ IC quality in over 78% of the cases. This algorithm has the potential to preempt interpersonal conflicts, enhancing job-site productivity, team development and human resources management. Furthermore, it can guide construction managers in designing IPS training programs.

This study contributes to the existing knowledge by addressing the crucial connection between IPS and IC quality in construction projects. Additionally, our novel approach, integrating fuzzy logic and XGBoost, provides a valuable tool for IC prediction. By identifying significant IPS and offering predictive insights, this research facilitates improved communication and collaboration in the construction industry, ultimately enhancing project outcomes.

Achieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.

An integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.

The results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.

A new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.

The purpose of the paper is to propose a novel Green-BIM team (GBT) through a framework that would be essential in mitigating barriers to Green-BIM integration.

An interpretive stance is adopted for this study. Through a qualitative survey, 25 experts with proficiency in green building construction and building information modelling (BIM) implementation were interviewed. Code-based content analysis was carried out using NVivo12.

The findings of this study signified the need for a GBT and proposed architect, client, Green consultant, designer (mechanical, electrical and plumbing [MEP], structural), BIM coordinator, engineer (MEP, structural), project manager, quantity surveyor and facilities manager to be involved in the team representing design phase, construction phase and operational phase.

The experts were limited to the Sri Lankan context; however, the findings can also benefit countries with socio-economic and cultural backgrounds similar to Sri Lanka.

Findings will be beneficial for policymakers and industry professionals to promote a BIM-enabled green building environment. The proposed GBT model extends existing theoretical frameworks, emphasising the need for a multi-disciplinary team throughout the entire lifecycle of a green building.

The proposed GBT model aligns with broader societal goals related to sustainable development. This approach provides a sustainable pathway to achieve economic goals for all stakeholders in the construction sector.

There is a dearth of literature on a GBT to improve the construction of green buildings in Sri Lanka. Thus, the developed model is unique as it presents a novel GBT for the Sri Lankan construction sector. Further, it elaborates roles and responsibilities of team members with comprehensive details on how to mitigate the barriers to Green-BIM integration.

This study aims to determine the effects of lodging infrastructure development (LID) on Cordillera Administrative Region’s (CAR) environmental quality and natural resource management and its implication to globally responsible leadership. Specifically, this study sought to determine the contribution of LID to environmental deterioration and natural resource degradation in the CAR. As a result, a mathematical model is developed, which supports sustainability practices to maintain the environmental quality and natural resource management in CAR, Philippines.

This study used a descriptive research design using a mixed-methods approach. Self-structured interview and survey were used to gather the data. The population of this study involved three groups. There were 6.28% (34) experts in the field for the qualitative data, 70.24% (380) respondents for the quantitative data and 23.47% (127) from the lodging establishments. 120 respondents from the Department of Tourism – CAR (DOT-CAR) accredited hotels. Nonparametric and nonlinear regression analysis was used to process the data.

The effects of LID on the environmental quality and natural resource management in CAR as measured through carbon emission from liquefied petroleum gas (LPG), electricity and water consumption in the occupied guest rooms revealed a direct correlation between the LID. Findings conclude that the increase in tourist arrival is a trigger factor in the increase in LID in the CAR. The increase in LID implies a rise in carbon emission in the lodging infrastructure. Any increase in tourist arrivals increases lodging room occupancy; the increased lodging room occupancy contributes to carbon emissions. Thus, tourism trends contribute to the deterioration of the environmental quality and degradation of the natural resources in the CAR. A log-log model shows the percentage change in the average growth of tourist arrival and the percentage increase in carbon emissions. Establishments should observe standard room capacity to maintain the carbon emission of occupied lodging rooms at a minimum. Responsible leadership is a factor in the implementation of policy on standard room capacity.

The result of the study has some implications for the lodging businesses, the local government unit (LGU), the Department of Tourism (DOT) and the Department of Environment and Natural Resources (DENR) in the CAR. The study highlights the contribution of the lodging establishments to CO₂ emission, which can degrade the quality of the environment, and the implication of responsible leadership in managing natural resources in the CAR. The direct inverse relationship between energy use and CO₂ emission in hotels indicates that increased energy consumption leads to environmental degradation (Ahmad et al., 2018). Therefore, responsible leadership among policymakers in the lodging and government sectors – LGU, DOT and DENR – should abound in the CAR. Benchmarking on the model embarked from this study can help in designing and/or enhancing the policy on room capacity standardization, considering the total area with its maximum capacity to keep the carbon emission at a lower rate. Furthermore, as a responsible leader in the community, one should create programs that regulate the number of tourists visiting the place to decrease the number of overnight stays. Besides, having the political will to implement reduced room occupancy throughout the lodging establishments in CAR can help reduce the carbon emissions from the lodging businesses. After all, one of the aims of the International Environment Protection Organization is to reduce CO₂ emissions in the tourism industry. Hence, responsible leadership in environmental quality preservation and sustainable natural resource management must help prevent and avoid greenhouse gas (GHG) emissions.

Most studies about carbon emission in the environment tackle about carbon dioxide emitted by transportation and factories. This study adds to the insights on the existing information about the carbon emission in the environment from the lodging establishments through the use of LPG, electricity and water consumption in the occupied guest rooms. The findings of the study open an avenue for globally responsible leadership in sustaining environmental quality and preservation of natural resources by revisiting and amending the policies on the number of room occupancy, guidelines and standardization, considering the total lodging area with its maximum capacity to keep the carbon emission at a minimum, thus contributing to the lowering of GHG emissions from the lodging industry.

Previous research has found that family characteristics, including family income, entrepreneurship/business experience and family size, can influence offspring’s entrepreneurial potential and perception of the barriers to entrepreneurship. This paper aims to extend this proposition to women in Oman to determine whether family income, entrepreneurship/business experience and family size influence women’s perception of barriers to entrepreneurship

This study is based on primary data that was collected through a structured questionnaire from 123 female respondents at an Omani private university. The data was analysed using PCA, correlation and regression analysis to determine the influence of the family characteristic on the perception of barriers to entrepreneurship.

The findings concluded that the three family characteristics being tested were not able to predict a change in the perception of barriers to entrepreneurship. This contradicts previous research conducted in Western contexts and highlights the potential weakness in family support for female entrepreneurship in Oman.

These results challenge some of the extant findings in the literature, thus enriching the current perspectives on female entrepreneurship and the impact of Omani family characteristics, in terms of income, economic background and family size, on the perception of barriers that hinder entrepreneurship among female students

This study seeks to comprehend if political exposure containing disapproval and different values will affect implicit knowledge sharing (KS) amongst colleagues in the organization. This research examines participants' responses to a colleague's social-media political exposure and their readiness to perform implicit KS to their colleague.

Data collection uses an online questionnaire and a vignette approach. Subsequently, data analysis for 316 finished surveys employs structural equation modelling-partial least squares (SEM-PLS).

The findings show that the perceived-value similarity of political posts of a colleague significantly and indirectly affects workers' readiness to do implicit KS. Besides, likes and trusts also significantly affect workers' readiness to perform implicit KS. While perceived-value similarity strongly shapes likes, likes significantly and positively affect trusts.

Sharing social-media postings associated with political exposure can hinder the implicit KS in organizations and is understudied in the field of knowledge management. Especially, unlike this study which focuses on private companies, previous studies have paid more attention to public enterprises. Besides, this paper's empirical verification is obtained from private organizations in Indonesia, which is also neglected by scholars.

Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.

In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.

Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.

The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.

Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.