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Most firms select their information technology outsourcing (ITO) vendors based on the two methods of the weighted-criteria evaluation technique – the “qualification score plus the lowest bid price for the highest price score (QS-LBHPS)” and the “qualification score plus the average bid price for the middle price score (QS-ABMPS).” This paper aims to understand whether these two methods provide the same or different results of vendor selection and how the proportional weights of a vendor's qualification and bid price affect the vendor selection results under the two methods.

–In total, 1,000 experimental tests were carried out using the developed spreadsheet template to examine vendor selection results of the two methods (QS-LBHPS and QS-ABMPS) and compare the vendor selection results under three conditions of vendors’ qualification and price weights. A correspondence analysis was also used to determine the proximal relationships among the selection results of the weighted criteria technique under the comparable methods.

The results indicate that, when using the two methods of the weighted criteria technique for a vendor selection, the selection results are significantly correspondent. In addition, the proportions of qualification and price weights affect the selection results under the two methods. The different proportions of qualification and price weights under the two methods yield the same selection results rather than different results.

This study fills the gap in ITO literatures concerning the vendor selection strategy. No empirical studies have been undertaken to compare the results of vendor selection under the two methods of the weighted-criteria evaluation technique. The findings enable a firm's selection team to apply the weighted-criteria evaluation technique effectively and realize that vendor selection results are altered based on the predefined proportions of qualification and price weights.

The purpose of this paper is to help enterprises to define and refresh their specific vendor selection criteria according to changing situations.

This paper firstly analyzes the variety of vendor selection criteria according to the diverse business environment. Furthermore, an approach of vendor selection based on MW‐OBS (an artificial neural network pruning algorithm) is put forward. MW‐OBS contributes a lot in distinguishing the crucial items of selection criteria based on certain enterprise's operational data, instead of assuming the criteria set subjectively. Meanwhile MW‐OBS evaluates the importance weights of these crucial items in criteria by data training.

The vendor selection criteria is believed to change for diverse enterprises and even for an enterprise's mutative business conditions because of the attribute of materials, cooperation relationships, and supplier's performance. The approach establishes the vendor selection criteria for different enterprises based on their own conditions, and once business environment changes, with new data being generated, the set can be refreshed dynamically and timely.

This approach extends the research of neural network pruning algorithm, for example the importance of all reserved criteria can be achieved from trained network without extra optimization,

This approach put emphasis on distinguishing dynamic criteria consistent with enterprise's circumstance. Enterprises are capable of constructing their various criteria collections conveniently according to their own specific situations with the application of approach.

Vendor selection is the main activity in a sourcing decision, which is a strategic decision in that it leads enterprises to eliminate costs and improve their performance. However, an inappropriate selection may compromise the financial and operational status of the enterprise. But vendor selection is a complex, multi-criteria decision-making process because different and conflicting criteria have to be considered and assessed in order to find consistent suppliers. Consequently, evaluating and selecting the best vendor is the key to successful business. Traditionally, vendors are normally selected on the basis of traditional criteria (TC), such as costs and quality, neglecting resilience criteria (RC) (e.g. agility and flexibility). Thus, enterprises ultimately realize that a selecting method which involves TC as the only ones is inefficient and needs to be changed. The paper aims to discuss this issue.

This study was set in motion by a problem in practice. It aims to provide a user-friendly decision-making tool for selecting the best vendor from a group which submitted their tenders for implementing a proposed radio frequency identification (RFID)-based passport tracking system (Dukyil et al., 2017). The main traditional and resilience (“trasilience” henceforth) selection criteria were identified in a unified framework in collaboration with experts in the institution. Next, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) algorithm was used to determine the relative importance of each criterion and the weights thus obtained were integrated into the ELimination Et Choix Traduisant la REalité (ELECTRE) algorithm. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm was also applied, to evaluate the performance of vendors and to select the best one. The qualitative evaluation of the criteria and the vendors was based on four decision makers. Finally, the Spearman’s rank correlation coefficient (SRCC) approach was applied to obtain the statistical difference between the ranking orders obtained from the two algorithms.

The efficiency of the proposed decision-making tool was evident from the real-case study of six tenders submitted for implementing a RFID-based passport tracking system. The SRCC also turned out a “very strong” association value between TOPSIS and ELECTRE.

The developed trasilient decision-making tool can easily be used to solve similar vendor or supplier selection problem. Moreover, other criteria can be added to fit other cases. Later, the tool was made available to the institution under study for solving future evaluation problems.

The literature shows that none of the previous papers presented an integrated trasilient approach that considers RC and TC simultaneously. This study presents a new trasilience tool for selecting a vendor.

This paper aims to select key criteria for sustainable vendor assessment and spare-parts supplies in the Indian petroleum refining sector using stochastic fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS).

The criteria for sustainable vendor evaluation and selection are identified from the review of the literature and further; it is finalized using the Delphi method. Eight supply chain (SC) experts from the Indian petro refining sector were identified as having more than five years of experience and agreed to participate in this study (known as decision-makers (DM)). Five vendors supplying spare-parts are shortlisted from the market with the discussion and consent of procurement experts from petroleum refineries. Subsequently, criteria and vendors are rated based on relative importance in linguistic terms from the group of eight DMs. As ratings involve uncertainties in the decision-making, the SFTOPSIS method is applied to determine criteria weight and vendor ranking at a distinct significance level (α). The ranking of the vendors is obtained for sustainable supply of spare-parts in the Indian petro refining sector using the SFTOPSIS method.

The ranking of sustainable vendors is obtained through the integrated application of the fuzzy and stochastic approach to capture the uncertainties in the ratings of DMs. The sensitivity analysis is carried out at distinct confidence limits of a normal distribution to obtain a robust ranking of the vendors. In this paper, a case application of SFTOPSIS in the Indian petro refining sector is presented in which key criteria and the vendor ranking are found to be changing with confidence limit for sustainable vendor evaluation.

The fuzziness and randomness in relative ratings collects from a group of DMs are taken in the proposed methodology. The distinct approaches are compared with changing significance-level under stochastic, fuzzy and deterministic TOPSIS to acquire robustness in the ranking. The proposed SFTOPSIS model can be useful to practitioners from the petroleum sector.

The originality of the paper contributes to an application of the SFTOPSIS method that is the extension of FTOPSIS in the petro refining sector of a developing country. The sensitivity analysis with distinct significance-level shows the uncertainties in the collected ratings from the DMs that supports robustness in the ranking. It might be helpful for SC professionals from the petro refining sector, who assess the rank of the vendors at different confidence limits for sustainable supply of spare-parts. Further research in the petroleum industry from emerging economies needs to be undertaken to broaden its scope and applicability.

Considering the limitation of the single stage vendor selection model, this paper proposes a two‐stage vendor selection framework for IT outsourcing in microfinance banks.

The paper attempts to realize a complete analysis at company level using grey systems theory for shaping the relations among variables. With the social choice function – Dodgson function, the first stage is a trial phase that helps the decision‐maker find the potential vendors, then, the decision‐maker employs those chosen vendors for the final selection with modified grey relational analysis (GRA) integrated analytic network process (ANP), which emphasizes the interrelation among those selection criteria, and avoids the subjective estimation of experts and practitioners. The case of a microfinance bank IT outsourcing vendor selection is used to verify the proposed approach.

The results of the empirical study show that the proposed method is practical for ranking competing vendors in terms of their overall performance with respect to multiple interdependence criteria for the bank's IT outsourcing.

The method exposed in the paper can be used for other supplier selection by modifying some criteria and weight.

The paper provides a method for ranking competing vendors in terms of their overall performance with respect to multiple interdependence criteria for IT outsourcing.

The purpose of this study is to determine the criteria used by members of the textile and apparel chain to select overseas vendors. Of specific interest is the value (if any) of ISO 9000 registration, i.e. whether Canadian apparel buyers are influenced by such registration, and whether there are determinants of how buyers respond to registration when selecting vendors.

A list of 16 criteria used by textile and apparel buyers to select overseas vendors was generated based on the literature, in‐depth interviews, and content analysis of a major industry publication. The criteria were tested for importance by use of a survey of representatives of the textile‐apparel supply chain in Canada.

Criteria relating to a vendor's flexibility and responsiveness were most important. Registration to a quality program such as ISO 9000 was not considered important nor did it seem to give an overseas vendor a business advantage over its domestic non‐registered counterpart. Two factors, category of apparel firm and the firm's use of quality programs, were potential determinants of favorable responses to registration.

This study furthers the understanding of importer behavior and overseas vendor selection criteria as well as the importance of ISO 9000 registration to buyers in the soft goods industries.

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.

Today, the high cost of e-learning systems’ implementation and the difficulty of managing the infrastructures motivate educational institutions toward application of cloud-based e-learning systems. This new system should be aligned with the academics’ aims and pedagogical principles to be beneficial for learners and instructors. Therefore, the vendor selection of learning systems is one of the most important processes to migrate toward cloud-based e-learning. The purpose of this paper is to develop a new framework to facilitate the vendor selection of cloud-based e-learning systems in the cloud market.

To identify the initial criteria as to the vendor selection of cloud-based e-learning services, a literature review is done. To enrich the initial criteria, a focus group of experts is investigated, and the framework developed; then, a survey analysis is conducted to validate the proposed framework. The extracted criteria and sub-criteria are weighted and prioritized using best-worst method (BWM).

The results indicate that the main dimensions of vendor selection framework as regards cloud-based e-learning systems are managerial, technological and pedagogical factors. The rank orders and weights of the mentioned aspects and their sub-criteria are calculated using the BWM.

The proposed framework helps managers to get a big picture of requirements as to cloud-based e-learning and more effectively to select appropriate vendors in this initiative. In the vendor selection process, managers must pay attention to technological issues as well as managerial and pedagogical considerations.

Cloud-based e-learning systems are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, there are little studies on how educational institutions and organizations could be able to select appropriate cloud-based e-learning systems. This paper explores the ignored but critically important subject of cloud-based e-learning. The main contribution of this paper is to propose a novel and integrated framework containing the important aspects of vendor selection in cloud-based e-learning services. The proposed framework comprises managerial, technological and pedagogical aspects simultaneously as well as sub-criteria denoting each aspect.

The aim of this paper is to identify the criteria that are used for vendor or supplier rating, prioritize these criteria based on the industry inputs and develop a vendor rating model. The data were collected from an Indian start-up working in product development using three-dimensional printing (3DP).

Factors of importance for vendor rating were identified through industry visits, and interacting with the industry experts from the start-up under consideration, substantiated by extensive review of relevant literature. A questionnaire-based survey was carried out to further narrow down the factors important to the industry, prioritizing them with a pairwise comparison analysis as envisaged in the analytical hierarchy process (AHP) technique along with the calculation of consistency ratios. Technique for order preference by similarity to ideal solution (TOPSIS) methodology was further used for data aggregation.

This research brought forward the criteria that are useful for rating vendors or suppliers with reference to 3DP sector.

This paper integrates AHP and TOPSIS to solve a multi-criteria vendor rating problem. The attempt was made to make vendor rating process universal so that it can encompass all the vendors of the firm.

To provide decision makers (DMs) an option for addressing problems involving finite alternative sets and multiple criteria, where criterion weighting is difficult or impossible.

The multicriteria decision problem is described, and a typically descriptive (rather than prescriptive) tool, data envelopment analysis (DEA), is summarized, along with a hypothetical but typical example of a multicriteria decision (vendor selection). The DEA approach is modified to incorporate weight constraints and is used to rank the available vendors. Results are compared with those from the use of a popular multicriteria decision tool (SMART) and a naïve averaging approach.

The modified DEA approach yields results very similar to those produced using SMART; these results are quite satisfactory in spite of the fact that DEA requires less involvement on the part of the DM. In addition, non‐dominant optima (a possible anomaly with DEA) are avoided, and often a single alternative, rather than a non‐dominated set, will result, thus providing a unique optimum.

Results are based on the analysis of a single data set. Future investigation should examine the performance of the DEA approach when other data sets involving more like as well as more unlike alternatives are involved.

With DEA the burden on the DM is reduced, as the need for eliciting criterion weights is obviated. DEA should thus provide an acceptable alternative to prescriptive modeling tools when multiple DMs are involved and/or criterion weight determination is unfeasible.

This paper demonstrates how DEA, a tool used more typically in post hoc evaluations, can be used also, with some modifications, as a prescriptive decision support tool.