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The main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.

The study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.

The findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.

This research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.

The present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

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 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 main problem addressed by this research is the current debate between the negative and positive effects of industrial clusters. This debate is a result of gaps between theoretical implications and empirical evidence in both the classical agglomeration theory and the agglomeration lifecycle theory. The purpose of this study is to propose a framework for developing an index measuring both organizational cluster involvement and organizational supply chain including the three pillars (economic, social, and environmental). Furthermore, the index acts as a quantitative predictor of the stages of the life cycle of industrial clusters. Adopting a case study methodology, the applicability of the index development framework is demonstrated. First, cross-sectional exploratory interviews are performed to locate items measuring the three pillars of organizational sustainability within Egyptian communication industry. Second, an explanatory, cross-sectional approach is applied gathering data from eight professionals related to involvement and supply chain sustainability of their organizations. Analytical hierarchical process is used for weighting and aggregating individual item metrics into two indicators (Saaty, 1980). Measuring, managing, and controlling capabilities of organization's supply chains outweighs the need to manage risks. The proposed framework aids firms within a cluster in making timely decisions about what needs addressing to improve supply chain sustainability performance. Hence, all environmental, social, and economic capabilities can be effectively monitored and controlled.

The purpose of the study is to provide insights into the process of decision making. Indian Railways as an organization has to use the available resources based on thorough analysis and proper application of available evaluation methodologies.

This study uses an integrated group discussion – analytical hierarchy process (AHP) to prioritize capital-intensive Indian Railways projects.

Through an email-based survey, six important criteria for ranking upcoming domestic freight corridors were selected, and weights for these criteria were calculated through AHP. The sensitivity analysis of the decision model suggests that the results of this study are significant, reliable and robust.

This study lays a foundation for the decision-makers of Indian Railways to consider a scientific approach while finalizing the big investment projects. This paper provides essential insights on prioritizing capital-intensive transport projects and is readily applicable to any case study.

Shadegan County as a wetland area was selected because of its susceptibility to flooding hazards and inundation. The purpose of this paper is to analyze flooding hazard based on the analytical hierarchy process methodology.

The eight influencing factors (slope, distance from wetland, distance from river, drainage density, elevation, curve number, population density and vegetation density) were considered for flood mapping within the Shadegan County using analytical hierarchical process, geographical information system and remote sensing. The validation of the map was conducted based on the comparison of the historical flood inundation of April 21, 2019.

The results showed that around 32.65% of the area was under high to very high hazard zones, whereas 44.60% accounted for moderate and 22.75% for very low to the low probability of flooding. The distance from Shadegan Wetland has been gained high value and most of the hazardous areas located around this wetland. Finally, the observed flood density in the different susceptibility zones for the very high, high, moderate, low and very low susceptible zones were 0.35, 0.22, 0.15, 0.19, and 0.14, respectively.

To the best of the authors’ knowledge, the flood susceptibility map developed here is one of the first studies in a built wetland area which is affected by anthropogenic factors. The flood zonation map along with management and restoration of wetland can be best approaches to reduce the impacts of floods.

Agricultural sector not only meets the nutritional requirements of all living creatures but also generates the primary source of the raw material provided by various branches of industry to fulfill their functions. It is of great importance to increase studies on oilseeds which have an important role in Turkey's agricultural products. They are grown in almost all of the country, which are vital for the nutrition and many sectors. The main purpose of the study is to offer an integrated approach to determine potential warehouse locations for oilseeds.

This is the first study that integrates Delphi, analytical hierarchical process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), P-Median and Panel data analysis in a real case. This integrated approach consists of the following steps, respectively: (1) The criteria were determined by the Delphi method. (2) The weights of the criteria were calculated by AHP and the provinces with the highest oilseed warehouse potential in seven regions of Turkey were specified by TOPSIS. (3) Oilseed warehouse numbers and locations were obtained by P-Median. (4) In order to answer whether the distribution network is profitable in the future with the determined center locations, a forecast model based on panel data analysis was created. (5) Regional representatives were determined for 2030, and the distribution network was analyzed again. (6) The costs that arose in 2018 and 2030 were computed and compared by cost analysis. (7) The effect of the change in criteria weights on the alternative results was tested by scenario analysis.

The findings indicated that oilseed crop production potential and oilseed crop production area turned out to be the most important criteria. Furthermore, the results showed that this model is robust and suitable for warehouse location selection studies.

The study can serve as a guide for local and central policy makers with both the criteria it uses and the model it develops.

The main contribution of this study is that the integrated approach has been used for the first time in location selection in a real case.

The statistics show that older adults have been major fire victims in high-rise buildings. However, the fire safety building codes in most countries are not inclusive and do not reflect provisions for older adults. The research aims to develop a fire risk assessment index system for older adults living in high-rise residential buildings in India. The study further develops a fire rating system to improve fire safety provisions in high-rise buildings in India.

The paper employs Delphi, analytic hierarchy process (AHP) and fuzzy comprehensive risk evaluation techniques. It considers 18 in-depth interviews and group discussions with builders, developers, architects, policymakers, and fire safety experts and consultants to develop a fire risk assessment system for older occupants living in high-rise residential buildings in India.

The fire protection systems and fire management systems are not as per the needs of older adults. The egress system components need improvisation considering the challenges of older adults.

The study is limited to older adults living in high-rise residential buildings in the capital city of Gujarat, Ahmedabad.

This study will assist policymakers in developing fire safety standards that are targeted to the needs of older persons living in high-rise residential buildings during a fire evacuation.

While several approaches for measuring fire risk in high-rise residential buildings exist, no such system has been developed specifically for older adults in India.

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 at modelling the enablers of dairy supply chain (DSC) in Indian context.

Interpretive structural modelling (ISM) approach has been used to model the enabler of dairy supply chain. The opinion has been taken from the industry experts and experienced academicians. Further, Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) used to classify the enablers based on driving and dependence power.

Findings show that stakeholder trust and top management support/leadership are the very crucial enablers in dairy supply chain; they are at a lower level of hierarchical structure and work as primary enablers to development of DSC. While customer satisfaction and financial performance are at top of the digraph, it shows these enablers are the outcome of a smooth supply chain. The MICMAC analysis suggests that the identified enablers are largely classified into dependent and independent enablers; there are no autonomous enablers in the dairy supply chain.

The study can aid businesses in the dairy processing industry in managing demand fluctuations, enhancing product quality, implementing effective information systems and adapting procedures, thereby enhancing supply chain performance.

There is very limited study on enablers of the dairy supply chain in general, while in the Indian context, there is no specific study on modelling the enablers of dairy supply chain.