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The purpose of this paper is to understand the reasons behind the intent for information technology (IT) outsourcing in academic institutions in GCC countries. It also aims to develop a multi-criteria decision model (MCDM) to aid the critical decision of IT outsourcing vendor selection.

The research utilizes a questionnaire-based survey to investigate reasons of IT outsourcing intent and the factors considered important for IT outsourcing vendor selection in academic institutions. The results of questionnaire-based study were utilized to develop a grey theory-based MCDM for vendor selection.

The results show that facilitating access to new technology, focus on core-competence, saving staff costs, and improved customer service are the most important factors for IT outsourcing intent while reputation of vendor, access to the state of art technology, quality of service, and knowledge of industry were considered as the most important factors for IT outsourcing vendor selection. Grey theory-based decision model was applied to a real case to facilitate the decision of selection of an IT outsourcing vendor.

Academic institutions that plan to outsource IT in future would be the major beneficiaries of this study. They can utilize the multi-criteria model to select the best vendor. The model facilitates a more rational decision making as it incorporates several criteria considered important for IT outsourcing vendor selection.

This study contributes to the body of research on IT outsourcing. It is first of its kind with its focus on academic institutions in GCC countries where currently education is a priority and IT is a backbone for its delivery. Another novelty of this research is that it propose a MCDM for IT outsourcing vendor selection. The findings of this study would serve as a guide to those institutions that intend to outsource IT functions to meet the ever growing needs of managing IT effectively.

The purpose of this paper is to design, develop, implement and validate a multi-criteria decision model for measuring supplier satisfaction through a case study.

A three-stage methodology was used to develop a framework to measure supplier satisfaction. The framework involved factors and Key Performance Indicators (KPIs) from literature and exploratory study. Further, using the framework, a multi-dimensional decision model to calculate Supplier Satisfaction Index was developed. The proposed decision framework was implemented as a real-world case study in an Indian manufacturing organization.

The study makes two major contributions: first, it develops a framework to measure supplier satisfaction using factors and KPIs suitable to the buyer organization; second, the model developed to calculate supplier satisfaction helps in understanding overall satisfaction of suppliers along with the level of satisfaction of each supplier. The model can also be used to suggest improvements to buyer organizations on specific factors and KPIs under each factor.

Supplier satisfaction plays an important role in multinational companies (MNCs), so the sample of practitioners considered in this study is relevant. However, it is likely that the small sample size of only suppliers and companies selected solely from the Indian manufacturing MNCs may have introduced some bias.

A comprehensive framework for enhancing the relationship with suppliers will be instrumental in deciding, managing and improving the level of supplier satisfaction.

This approach provides purchase managers with the flexibility of selecting factors and KPIs at every level of analysis and also a single index to establish supplier’s satisfaction with a buyer company.

The existing literature on venture capitalists’ (VCs’) exits provides insufficient evidence regarding factors affecting the exit decision. This study aims to identify these factors and examine how VC firms do ranking or prioritize these factors.

The study is based on primary data. The qualitative analysis was done to develop the survey instrument. Fuzzy analytical hierarchical process, which is a popular method of multi-criteria decision modeling, is used to identify or rank the determinants of exit strategy by venture capital firms in India.

Broadly, eight determinants of exit strategy are ranked by VCs. A total of 33 statements describe these eight determinants. The results are analyzed on the basis of four measures of VCs’ profile, i.e. age of VC firm, number of start-ups in portfolios, type of investment and amount of investment.

The survey instrument needs to be validated with a larger sample size and other financial backers than VCs.

The study has direct managerial implication for VC firms as it provides useful information regarding the determinants of exit strategy by VC firms in India. These findings can provide necessary information to other financial backers too, viz., angel investors, banks, non-banking financial institutions and other individual and syndicated set-ups providing funding to start-ups.

The current research is unique as no prior study has explored the determinants of VCs exit strategy and prioritizing these determinants.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

The supply chain (SC) model is commonly utilized in manufacturing and has also been adapted to a variety of service industries with similar goals. However, in a few service industries, most notably academic libraries, the SC model is rarely implemented, despite the fact that SC management best practises have been shown to boost stakeholder satisfaction, revenue and decrease total expenses. This research aims to discuss the development of the entities of an academic library SC model and present the verifying/validation process of the model.

In this research, both qualitative and quantitative approaches have been used. System thinking method used to develop the conceptual SC model of the academic library and fuzzy Delphi method (FDM) used to validate the developed model.

This research explores the functions and components that need to develop and validate the academic library SC model.

The outcome of this research is to construct a valid SC model of an academic library which could be a strategical tool for ensuring a quality service for the library users and the stakeholders because this model shows a holistic view of all entities of the academic library SC.

Humanitarian supply chain (HSC) has been constantly challenged with multiple issues due to the complex dynamics of the disaster. These issues are inevitable and interrelated. The issues produce undesirable cascading effects that make performance measurement complicated. This paper aims to identify and model the critical barriers in the HSC.

The study identifies 17 barriers through the relevant literature and interviews with the stakeholders related to humanitarian organization (HO) in the developing economy. The barriers have been identified from strategic, technological, organizational, economic and operational point of view. Further, the barriers were ranked based on the degree of influence using the grey analytic network process (ANP) approach. The interrelationship among the influential barriers is established through modified total interpretive structural modelling (mTISM). To analyse and demonstrate the iterative consensus among the stakeholders, kappa statistics was adopted.

The barriers have been ranked to determine their extent of influence and modelled to reveal the interrelationship among them. The issues concerned with skills of personnel are the most influential barrier. Other three critical barriers identified are: chaotic operative environment, conflicting objectives of HO and lack of funding for information technology (IT) infrastructure. Appropriate funds need to be allocated towards IT infrastructure and personnel training.

Both quantitative and qualitative performance measurement frameworks had been proposed earlier for HSC. However, limited literature addresses the implementation issues with the available frameworks. This study advances the knowledge on performance measurement barriers of HSC and develops a functional description to identify the critical role of performance measurement in HOs. The study proposes a new version of the interpretive structure modelling, using mTISM technique, to determine the contextual interactions between various HSC performance measurement barriers.

The purpose of this paper is to suggest a framework that would guide the practicing manager to decide the degree of information systems (IS) outsourcing.

The proposed framework has been derived from the findings of a previous empirical study and qualitative inputs. Analytical hierarchy process (AHP) is then used to apply the framework. The framework is finally validated in three India‐based banks.

The proposed framework helps in creating a strategic alignment between the business strategy and information system strategy. The application of the framework shows the preference of managers towards high IS outsourcing. Strategic alignment and medium term impact emerge the important factors in IS outsourcing. The validity of the framework is proved in three banks.

The validation exercise has been done on a small sample due to resources constraints and a study involving a larger sample is desired. Further, it is advised to review the framework on regular intervals and make suitable changes in decision factors.

The framework is helpful to managers in identifying the critical factors which can act as useful inputs in taking informed decisions on the degree of IS outsourcing.

The paper fills some of the gaps in IS outsourcing by suggesting a practice‐ oriented framework that guides the decision maker to undertake a systematic and structured approach in arriving at an outsourcing decision. The framework has evolved from the practices of banks in India for which there does not exist any similar research.

A wide number of technologies are currently in store to harness the challenges posed by pandemic situations. As such diseases transmit by way of person-to-person contact or by any other means, the World Health Organization had recommended location tracking and tracing of people either infected or contacted with the patients as one of the standard operating procedures and has also outlined protocols for incident management. Government agencies use different inputs such as smartphone signals and details from the respondent to prepare the travel log of patients. Each and every event of their trace such as stay points, revisit locations and meeting points is important. More trained staffs and tools are required under the traditional system of contact tracing. At the time of the spiralling patient count, the time-bound tracing of primary and secondary contacts may not be possible, and there are chances of human errors as well. In this context, the purpose of this paper is to propose an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations.

Pandemic situations push the world into existential crises. In this context, this paper proposes an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations. By exploring the daily mobility and activities of the general public, the system identifies multiple levels of contacts with respect to an infected person and extracts semantic information by considering vital factors that can induce virus spread. It grades different geographic locations according to a measure called weightage of participation so that vulnerable locations can be easily identified. This paper gives directions on the advantages of using spatio-temporal aggregate queries for extracting general characteristics of social mobility. The system also facilitates room for the generation of various information by combing through the medical reports of the patients.

It is identified that context of movement is important; hence, the existing SemTraClus algorithm is modified by accounting for four important factors such as stay point, contact presence, stay time of primary contacts and waypoint severity. The priority level can be reconfigured according to the interest of authority. This approach reduces the overwhelming task of contact tracing. Different functionalities provided by the system are also explained. As the real data set is not available, experiments are conducted with similar data and results are shown for different types of journeys in different geographical locations. The proposed method efficiently handles computational movement and activity analysis by incorporating various relevant semantics of trajectories. The incorporation of cluster-based aggregate queries in the model do away with the computational headache of processing the entire mobility data.

As the trajectory of patients is not available, the authors have used the standard data sets for experimentation, which serve the purpose.

This paper proposes a framework infrastructure that allows the emergency response team to grab multiple information based on the tracked mobility details of a patient and facilitates room for various activities for the mitigation of pandemics such as the prediction of hotspots, identification of stay locations and suggestion of possible locations of primary and secondary contacts, creation of clusters of hotspots and identification of nearby medical assistance. The system provides an efficient way of activity analysis by computing the mobility of people and identifying features of geographical locations where people travelled. While formulating the framework, the authors have reviewed many different implementation plans and protocols and arrived at the conclusion that the core strategy followed is more or less the same. For the sake of a reference model, the Indian scenario is adopted for defining the concepts.

This study proposes a methodology based on data source triangulation to measure the “strategic fit” for the automotive supply chain.

At first, the authors measured the responsiveness of the Indian automobile supply chain, encompassing the top ten major automobile manufacturers, using both sentiment and conjoint analysis. Second, the authors used data envelopment analysis to identify the frontiers of their supply chain. The authors also measured the supply chain's efficiency, using the balance sheet. Further, the authors analyzed the “strategic fit” zone and discussed the results.

The results indicate that both the proposed methods yield similar outcomes in terms of strategic fitment.

The study outcomes facilitate measuring the strategic fit, thereby leveraging the resources available to align. The methodology proposed is both easy to use and practice. The methodology eases time and costs by eliminating hiring agencies to appraise the strategic fit. This valuable method to measure strategic fit can be considered feedback for strategic actions. This methodology could also be incorporated possibly as an operative measurement and control tool.

Data triangulation meaningfully enhances the accuracy and reliability of the analyses of strategic fit. Data triangulation leads to actionable insights relevant to top managers and strategic positioning of top managers within a supply chain.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.