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International collaboration is a crucial requirement of entrepreneurship, particularly in developing emerging economies. This collaboration seems so necessary in the food industry as a major contributor to environmental, social and economic problems. This paper aims to identify, analyse the influential network relationship and prioritise the key success factors (KSFs) of international collaboration formation in the entrepreneurial food industry with a case study on Iran's emerging economy.

To identify a list of KSFs, a qualitative method, literature review, is initially used. A quantitative method, fuzzy-Delphi, then is employed to finalise the main KSFs based on the entrepreneurial food industry experts' opinion. To analyse the causal relationship, and prioritise the KSFs, a fuzzy decision-making trial and evaluation laboratory (DEMATEL)-analytic network process (ANP) methodology, i.e. FDANP, is applied. At the first stage, the cause–effect diagram of KSFs is extracted using fuzzy DEMATEL and then, the KSFs weights and priorities are evaluated using a fuzzy ANP.

The results illustrate that the characteristics of effective development workers are the leading dimension of a successful international collaboration that directly affects other dimensions. On the other hand, increased marketing and trading is the most important KSF that is directly related to international entrepreneurial collaboration team capabilities and professionalism. The leading and casual role of team members also plays a vital role in strategic and communication issues affecting the collaboration success, e.g. market research and new product development. Availability of financial resources and the ability of partners in continuous financing is also a crucial and required factor for a successful collaboration.

Using an extensive review of the literature to extract the KSFs of international entrepreneurial collaboration and finalising them using a fuzzy-Delphi method and examining the cause-effect relations between them, as well as prioritising the KSFs are the main contributions of this paper.

In this research, extracting the innovation drivers of successful crowdfunding from the literature review, screening them for the entrepreneurial small- and medium-sized enterprises (SMEs), analysing the cause-and-effect relationship amongst them and presenting a basic causal conceptual model and eventually determining the importance/weight of each relevant driver were the primary purposes of this research. As a result, the authors have also designed a score function to measure the future innovative crowdfunding score for SMEs.

A multi-layer multi-criteria decision-making (MCDM) approach has been designed and employed to achieve research objectives. After extracting the initial list of drivers, Fuzzy Delphi was applied to screen the relevant innovation drivers of successful crowdfunding for entrepreneurial SMEs. Decision-making trial and evaluation laboratory (DEMATEL) was used to analyse the cause-and-effect relationship amongst the drivers and illustrate a basic conceptual model. Analytical network process (ANP) and Stepwise Weight Assessment Ratio Analysis (SWARA) were applied to determine the importance of the drivers and by aggregating them to measure the innovative crowdfunding score.

Initially, 28 innovation drivers of successful crowdfunding were extracted from the literature. Then by employing the first-round Delphi fuzzy method amongst 15 international entrepreneurs in SMEs, the relevant drivers, including eleven items, were screened and selected. Then by implementing the DEMATEL method, the relationship amongst these screened drivers was identified, and seven drivers were determined as causes and the rest as effects. Subsequently, a conceptual model based on the causal analysis of the drivers from the DEMATEL method was designed. Eventually, by aggregating the weight of drivers emanated from SWARA, DEMATEL and DANP, the score function for measuring the situation of an SME was designed.

According to the crowdfunding scores in this research from entrepreneurs of SMEs, influential factors in developing countries were recognised as two times more prominent in developing countries. This might be rooted in the circumstances of developing countries where many startups and SMEs are emerging in vast areas and different fields due to investment in innovation management. In these countries, the authorities and officials support these companies to empower their capabilities and innovative ideas to (1) deal with the severe competitive market and (2) benefit from them as potential economic engines. Therefore, crowdfunding platforms and public initiatives can be considered one of the most effective government supports, which may involve financial risks.

To the best knowledge of the authors, investigating the innovation drivers of successful crowdfunding via quantitative analysis by multi-layer decision-making approaches has not been considered previously. Moreover, the authors have designed a crowdfunding score function to determine the situation of an entrepreneurial SME in this area. A combination of different MCDM methods, including Fuzzy Delphi, SWARA, DEMATEL, ANP and DANP, to investigate the innovation drivers of successful crowdfunding in SMEs has not been considered previously.

Over the past decade, many small‐ and medium‐sized enterprises have incurred dramatic losses due to major disasters, causing loss of their business information systems and transaction data, so, they have started to outsource their information operations to data centers (DCs), in order to monitor critical business data operations. The purpose of this paper is to propose a dual‐sided business data integrity policy framework.

Based on a review of the available literature, case studies, and in‐depth interviews with top CEOs and experts in the field, a fuzzy Delphi method is proposed in two frameworks. In addition, a risk evaluation rule is derived by applying Bayesian decision analysis to mitigate the risk and lower the cost in their outsourcing policy; and Delphi method is used to extract 11 DC service quality evaluation indicators and also use these indicators to conduct a benchmark in Taiwan. Furthermore, the proposed framework is applied to figure out critical service advantages as well as suggestions for the DC involved in the benchmark.

The results of framework point out that enterprises should monitor the four operation elements (facility and infrastructure, server system management, information security management, and disaster recovery (DR) mechanism) to ensure and improve their data integrity; and DC firms need to build robust facilities and services in the five operation elements (customizability, serviceability, information technology infrastructure, security management, and knowledge intensity).

This paper uses a hybrid Delphi‐Bayesian method to propose a new framework, which is adequately integrated with the consensus of experts and business decision makers; higher professionalism and content validity are achieved. Enterprises can use these indicators to evaluate the service quality of DCs among DC firms.

This paper aims to investigate Supply Chain (SC) Performance Measurement Systems (PMSs) (SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. Furthermore, the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such Black Swan events is investigated in this study.

The research methodology is based upon a novel qualitative and quantitative mixed-method. A Systematic Literature Review (SLR) was initially employed to identify two complete lists of SCPMSs and IDTs. Then, a novel Interval-Valued Intuitionistic Hesitant-Fuzzy (IVIHF)-Delphi method was firstly developed in this paper to screen the extracted SCPMSs. Afterward, the Propriety, Economic, Acceptable, Resource, Legal (PEARL) indicator of the Hanlon method was innovatively applied to prioritize the identified IDTs for each finalized SCPMS.

Two high-score SCPMSs including the SC operations reference (SCOR) model and sustainable SCPMS were recommended to improve measuring the performance of the pharmaceutical SC of emerging economies such as Iran in which the societal, biological and economic issues were undeniable, particularly during unexpected events. Employing nine IDTs such as simulation, big data analytics, cloud technologies, etc., would facilitate implementing sustainable SCPMS from distinct perspectives.

This is one of the first papers to provide in-depth insights into determining the priority of contribution of IDTs in applying different SCPMSs during global pandemics. Proposing a novel multi-layer mixed-methodology involving SLR, IVIHF-Delphi, and the PEARL indicator of the Hanlon method is another originality offered by this paper.

This chapter aims to identify, analyse, classify and rank the sustainability indices and internationalisation challenges of the footwear industry in the emerging economy of Iran. This would provide deeper decision-making insights into Iranian footwear businesses. First, a list of sustainability indices and internationalisation challenges was obtained by reviewing the literature. Then, a combination of multi-criteria decision-making (MCDM) approaches was implemented. The initial sustainability indices and internationalisation challenges were screened using the fuzzy Delphi method, keeping a total of 14 criteria. The best–worst method (BWM) was employed to weigh and rank the criteria. The interpretive structural modelling (ISM) technique and cross-impact matrix applied in MICMAC were employed to visualise the conceptual model based on the levels and classification of the important criteria for the internationalisation of the Iranian footwear industry. The 14 criteria were demonstrated to be important in internationalisation. The most critical sustainability indices were reducing hazardous substances in leather tanning and labour education and training. In contrast, exchange rate instability in Iran’s economy and strict chemical regulations for clothing and footwear were found to be the most important internationalisation challenges. Hence, these criteria should be considered in the internationalisation strategies of the Iranian footwear industry. A combined multilayer sustainable decision-making approach was used to analyse the Iranian footwear industry’s essential sustainability indices and internationalisation challenges. Furthermore, implications and insights are offered to footwear businesses for future decision-making.

This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context.

20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used.

The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties.

This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care.

This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.

Privatization is a complex issue in many developing countries; therefore, it is vital to examine the obstacles that prevent its proper implementation. The goal of this study is to identify and analyze the barriers to private sector investment in the Water and Sewage Industry (WSI) and to suggest effective ways to attract the private investors to this sector.

The obstacles to private sector investment in the WSI were identified by conducting a desktop literature review and interviewing an expert panel, using the fuzzy Delphi technique. The most important barriers were identified and categorized. A structured survey was then developed and distributed to private sector investment experts. The Fuzzy Analytic Hierarchy Process (FAHP) was applied to further examine the responses and to rank the identified barriers.

The results showed that the greatest barrier to privatization is the weakness of insurance companies in controlling investment risks, and the second greatest barrier is the weakness of the country's capitalist culture. A review of recent success stories revealed that these barriers can be overcome with transparent price policies and increased interaction between the public and private sectors, which motivate private investors to invest in the WSI.

The elicitation of this study can be useful to both private and public sectors for the development of infrastructure projects, particularly for the WSI.

A brownfield site is well described by various definitions and the idea to redevelop it is supported by identifying numerous benefits for the society. Further, the existing literature covers a broad range of different aspects of the brownfield redevelopment thus elaborating different features. At present, there is no overview of the brownfield features from the real estate development perspective focusing on the physical, legal and financial aspects of a site and property. This paper aims to address these issues.

At first, this paper contributes with the literature survey after which the features were structured according to the real estate development perspective. Additionally, the authors distinguish different expert groups and show the importance of keeping apart their aggregated opinions. Fuzzy Delphi technique is considered as an excellent method to gather such diverse panel data since it supports expert diversity in its procedure and calculation. Additionally, this method captures the uncertainty due to the human factor in valuation and thus improves the validity of the features quantification.

The survey was among the experts grouped by the specific goals and tasks. This paper presents the findings how the different expert groups value the brownfield features relevant for development potential and what they are.

The paper aims to contribute to the complex decision‐making process in brownfield redevelopment by identifying, structuring and rating the most relevant features of development potential. The authors introduced the method that highlights the importance of rigorous procedure for the panel data collection and advances the weighting of the features. This is of particular importance for the field of the real estate development appraisal since the present features or variables influence the future marketability and cost of a development. Missing these features seriously endangers the appraisal. A similar threat can influence any econometric model recently extensively used in policymaking.

The purpose of the study was to identify and weight the dimensions and indicators of individual job performance (IJP).

To identify dimensions and their measuring indicators, a literature review was conducted in PubMed, Embase™, ProQest, Scopus®, Web of Science™ and Google Scholar. Based on the results of the literature review and consensus among the research team, dimensions and measuring indicators were identified and an IJP framework was constructed. For weighting dimensions and indicators, fuzzy analytical hierarchy process (FAHP) and fuzzy Delphi technique (FDT) were conducted, respectively.

Based on the results, the conceptual framework showed that IJP consisted of four dimensions as follows: task performance (TP), contextual performance (CP), counterproductive work behaviors (CWBs) and adaptive performance (AP). The results of FAHP showed that TP (0.358) had the highest weighting in measuring IJP. The weight of CWBs, CP and AP was 0.302, 0.244 and 0.096, respectively. The results showed that 102 indicators were identified to measure the dimensions. Using the FDT, 45 indicators were accepted and 57 were rejected. Of the accepted indicators, 12 indicators belonged to TP. In addition, 11, 12 and 10 belonged to CP, CWBs and AP, respectively.

The results of the article can be useful in research and practice and bridge the gap between theory and practice. In the research area, the researchers can use this model to develop questionnaires for evaluating IJP in all occupations. The researchers can choose the most effective dimensions or indicators in accordance with the corresponding occupation. Moreover, the results can be used in practice (economic and commercial impact), teaching, influencing public policy and helping to establish a more complete knowledge system.

In order to select dimensions and their measuring indicators, the work in the study took the unique approach to employ powerful decision-making methods combined with fuzzy methods to remove any ambiguity in opinions.

In this research, the initial list of internal capabilities in small and medium-sized enterprises (SMEs) leading to success in international markets has been extracted. Then, the most relevant capabilities to international SMEs under servitisation and hybrid offerings have been screened. Next, the selected capabilities have been classified, and ultimately the relationship amongst the capabilities has been analysed. The conceptual model for SMEs participating in international markets with hybrid offerings has been illustrated.

A literature review has been employed to extract the initial list of internal capabilities to address the research objectives. Then, a novel hesitant fuzzy Delphi (HFD) method has been developed to select the most relevant capabilities for SMEs for hybrid offerings in international markets by using the experts opinions. Subsequently, a novel hesitant fuzzy interpretive structural modelling (HFISM) has been developed to classify the capabilities, design a level-based conceptual model and present the relationship amongst the prominent capabilities.

After the literature review, sixteen internal capabilities leading to success in the international market via hybrid offerings have been extracted. Then, eight selected capabilities were chosen for further investigation by applying 15 expert opinions and via the HFD approach. According to HFISM results, a level-based conceptual model was emanated, and “ability to take advantage of international opportunities”, “financial strength”, “technology level” and “efficient innovation management” were considered as the most fundamental capabilities resulting in successful hybrid offerings in international markets.

Alongside the multi-layer decision-making approach developed in this manuscript to analyse the internal capabilities roles in hybrid offering success towards international markets, to the best knowledge of the authors, the hesitant fuzzy approaches developed in this article have not been previously presented by any other scholar. A novel HFD approach has been designed for consensus amongst the experts under uncertain circumstances. Furthermore, a novel HFISM has been suggested and employed in this research to comprehend the relationship amongst the internal capabilities.