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This paper aims to explore key management actions for implementing security on the cloud, which is a critical issue as many organizations are moving business processes and data on it. The cloud is a flexible, low cost and highly available technology, but it comes with increased complexity in maintaining the cloud consumer’s security. In this research, a model was built to assist strategic decision-makers in choosing from a diverse range of actions that can be taken to manage cloud security.

Published research from 2010 to 2022 was reviewed to identify alternatives to management actions pertaining to cloud security. Analytical hierarchical process (AHP) was applied to rate the most important action(s). For this, the alternatives, along with selection criteria, were summarized through thematic analysis. To gauge the relative importance of the alternatives, a questionnaire was distributed among cloud security practitioners to poll their opinion. AHP was then applied to the aggregated survey responses.

It was found that the respondents gave the highest importance to aligning information security with business needs. Building a cloud-specific risk management framework was rated second, while the actions: enforce and monitor contractual obligations, and update organizational structure, were rated third and fourth, respectively.

The research takes a general view without catering to specialized industry-based scenarios.

This paper highlights the role of management actions when implementing cloud security. It presents an AHP-based multi-criteria decision-making model that can be used by strategic decision-makers in selecting the optimum mode of action. Finally, the criteria used in the AHP model highlight how each alternative contributes to cloud security.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

Significant developments in the service sector have been brought about by Industry 4.0. Automated digital technologies make it possible to upgrade existing services and develop modern industrial services. This study prioritizes critical factors for adopting Industry 4.0 in the Indian service industries.

The author identified four criteria and fifteen significant factors from the relevant literature that have been corroborated by industry experts. Models are then developed by the analytical hierarchy process (AHP) and analytical network process (ANP) approach to ascertain the significant factors for adopting Industry 4.0 in service industries. Further, sensitivity analysis has been conducted to determine the sensitivities of the rank of criteria and sub-factors to corroborate the results.

The outcome reveals the top significant criteria as organizational criteria (0.5019) and innovation criteria (0.3081). This study prioritizes six significant factors information technology (IT) specialization, digital decentralization of all departments, organizational size, smart services through customer data, top management support and Industry 4.0 infrastructure in the transition toward Industry 4.0 in the service industries.

The potential factors identified in this study will assist managers in determining strategies to effectively manage the Industry 4.0 transition by concentrating on top priorities when leveraging Industry 4.0. The significance of organizational and innovation criteria given more weight will lay the groundwork for future Industry 4.0 implementation guidelines in service industries.

Our research is novel since, to our knowledge, no previous study has investigated the potential critical factors from organizational, environmental, innovation and cost dimensions. Thus, the potential critical factors identified are the contributions of this study.

This study aims to investigate the comparative importance of factors influencing the customer shift behavior from conventional to Islamic banking for consumer finance in Pakistan.

First, a comprehensive analysis of the existing literature was conducted to identify a broad range of factors related to customer shift behavior. Through an expert sampling, 14 essential factors were chosen for further investigation. Second, a questionnaire was developed using the analytical hierarchy process (AHP). This questionnaire was then distributed among customers who had previously been using conventional banking services but had made a shift toward Islamic banking. The purpose of this questionnaire was to gather data and insights regarding their motivations and decision-making process behind the shift, and a sample 215 customers are taken in the study.

The results of AHP depicts that the religiosity is a most important factor influencing customers to shift from conventional to Islamic banking, and the second most important factor is pricing. The other subsequent important factors are reputation of the bank, marketing and promotion, service quality, behavior of banks staff, Shariah compliance, management, convenience, fastness and charges/fees. Whereas documentation, ambiance and recommendation are found least important factors to patronize Islamic banking.

The study recommends Islamic banks to create awareness, concentrating on religious factor to have a greater impact on growth of Islamic banking and shrinking of conventional banking. Further, it suggests Islamic banks to apply Shariah-recommended approach of doing business, to help community in best possible way and to launch differentiated marketing techniques to attract customers. It also proposes regulatory authorities to provide facilitation to Islamic banking business by providing level playing field similar to conventional banking, tax equality and conversion of public financing from conventional banking to Islamic banking.

The originality of this study lies in its comprehensive analysis of factors influencing consumer shift behavior from conventional to Islamic banking in the context of consumer finance in Pakistan. By using the AHP, the study provides a structured approach to understanding the relative importance of these factors. This is the uniqueness of the paper that it applies the AHP for the analysis. Furthermore, the study offers practical implications for Islamic banks and regulatory authorities to effectively address and capitalize on this consumer shift trend.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

This paper aims to define a hierarchical and multi-criteria framework based on pillars of the Modernization of Higher Education to evaluate European Accounting Doctoral Programmes (EADE-Model).

The authors applied a quali-quantitative methodology based on the analytic hierarchy process and the survey approach. The authors conducted an extensive literature and regulation review to identify the dimensions affecting the quality of Doctoral Programmes, choosing accounting as the relevant and pivotal field. The authors also used the survey to select the most critical quality dimensions and derive their weight to build EADE Model. The validity of the proposed model has been tested through the application to the Italian scenario.

The findings provide a critical extension of accounting ranking studies constructing a multi-criteria, hierarchical and updated evaluation model recognizing the role of doctoral training in the knowledge-based society. The results shed new light on weak areas apt to be improved and propose potential amendments to enhance the quality standard of ADE.

Theoretical and practical implications of this paper are directed to academics, policymakers and PhD programmes administrators.

The research is original in drafting a hierarchical multi-criteria framework for evaluating ADE in the Higher Education System. This model may be extended to other fields.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

Co-working spaces bundle all real estate services into a package and leverage shared economy trend to create new opportunities for growth. This sector is anticipated to expand significantly due to changes in mobility and office design driven by the development of remote or hybrid work settings. The current study attempts to identify key motivating factors for users in emerging economies in choosing co-working spaces.

Using analytic hierarchy process (AHP) methodology and the self-determination theory framework, a total of 4 criteria-level factors, along with 13 sub-criteria level factors were identified as key motivators for adapting to co-working spaces.

The study highlights a few factors and their relative importance, which could help firms/organizations to start or offer co-working spaces within emerging economies.

The study contributes to literature by advancing the understanding of key motivators for users of co-working spaces within the ambits of emerging economies. In the process, the authors enlist a few factors vis-à-vis their relative importance, which could help firms/organizations to start or offer co-working spaces within emerging markets.

This study aims to determine the order of importance of blockchain technology gains for foreign trade management. The second aim of the research is to select the most suitable blockchain type for the foreign trade process.

The data required for the research analysis were collected through comparison matrices scored by foreign trade experts. The data were analyzed with the analytical hierarchy process method, and the gains of blockchain technology for the foreign trade process were prioritized. Finally, the most suitable type of blockchain technology was selected with the PROMETHEE method.

As a result of the study, subject matter experts perceived that the most critical blockchain gain for the foreign trade process would be cost savings. In addition, it has been observed that the general trade process differs compared to the logistics and payment processes. Finally, open blockchain was the most suitable blockchain type for foreign trade processes.

In this study, the positive gains of blockchain technology are considered. In future studies, it is recommended to consider the existing negative factors to be able to use blockchain technology in foreign trade processes. In addition, it is suggested to conduct a study by dividing foreign trade into two, import and export.

All stakeholders who want to integrate blockchain technology into their foreign trade processes, including foreign trade companies, software developers, policymakers and international institutions, can benefit from the results of this study. A blockchain technology software created for foreign trade management can be shaped according to the results of this study.

Although the gains that blockchain technology provides to foreign trade processes are frequently emphasized in the literature, which gain would be greater has not been examined. The lack of an answer to the issue of how blockchain technology should be designed for foreign trade processes has been the missing part of the relevant literature. To the best of the authors’ knowledge, this study is the first experimental study in the literature that prioritizes blockchain gains and selects the appropriate blockchain type for foreign trade processes.

This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.