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1 – 10 of over 2000The purpose of this paper is to apply a very simple but powerful analysis of the variance‐covariance matrix of individual best‐worst scores to detect which attributes are…
Abstract
Purpose
The purpose of this paper is to apply a very simple but powerful analysis of the variance‐covariance matrix of individual best‐worst scores to detect which attributes are determining utility components and drive distinct consumer segments.
Design/methodology/approach
First an analysis of variance and covariance is used to find attributes which are perceived to have different importance by different consumers and which jointly drive consumer segments. Then we model consumer heterogeneity with Latent Clustering and identify utility dimensions of on‐premise wine purchase behaviour with a principal component analysis.
Findings
Four consumer segments were found on the UK on‐premise market, which differ in the relative strength of five wine choice utility dimensions: ease of trial, new experience, restaurant advice, low risk food matching and cognitive choice. These segments are characterised by sociodemographics as well as wine and dine behaviour variables.
Research limitations/implications
Attributes with high variance signal respondents’ disagreement on their importance and indicate the existence of distinctive consumer segments. Attributes jointly driving those segments can be identified by a high covariance. Principal component analysis condenses a small number of behavioural drivers which allow an effective interpretation and targeting of different consumer segments.
Practical implications
This paper's analysis opens new doors for marketing research to a more insightful interpretation of best‐worst data and attitude scales. This information gives marketing managers powerful advice on which attributes they have to focus in order to target different consumer segments.
Originality/value
This is the first study considering individual differences in BW scores to find post hoc segments based on revealed differences in attribute importance.
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Muhammad Shoaib, Shengzhong Zhang, Hassan Ali, Muhammad Azeem Akbar, Muhammad Hamza and Waheed Ur Rehman
This study aims to identify and prioritize the challenges to adopting blockchain in supply chain management and to make its taxonomic model. Moreover, validate whether these…
Abstract
Purpose
This study aims to identify and prioritize the challenges to adopting blockchain in supply chain management and to make its taxonomic model. Moreover, validate whether these challenging factors exist in the real world and, if they exist, then in what percentage.
Design/methodology/approach
This research adopted the fuzzy best-worst method (F-BWM), which integrates fuzzy set theory with the best-worst method to identify and prioritize the prominent challenges of the blockchain-based supply chain by developing a weighted multi-criteria model.
Findings
A total of 20 challenges (CH's) were identified. Lack of storage capacity/scalability and lack of data privacy challenges were found as key challenges. The findings of this study will provide a robust framework of the challenges that will assist academic researchers and industry practitioners in considering the most significant category concerning their working area.
Practical implications
Blockchain provides the best solution for tracing and tracking where RFID has not succeeded. It can improve quality management in a supply chain network by improving standards and speeding up operations. For inventory management, blockchain provides transparency of documentation for both parties within no time.
Originality/value
To the best of the authors' knowledge, no previous research has adopted the fuzzy best-worst method to prioritize the identified challenges of blockchain implementation in the supply chain. Moreover, no study provides a taxonomic model for the challenges of implementing a blockchain-based supply chain.
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Justin Zuopeng Zhang, Praveen Ranjan Srivastava and Prajwal Eachempati
The paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases…
Abstract
Purpose
The paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support.
Design/methodology/approach
A hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases.
Findings
Drones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels.
Originality/value
The paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones.
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Farhan Muhammad Muneeb, Amir Karbassi Yazdi, P. Wanke, Cao Yiyin and Muhammad Chughtai
This study focuses on the Critical Success Factors (CSF) for developing sustainable entrepreneurship in the Pakistani telecommunication industry. Despite the efforts made by…
Abstract
Purpose
This study focuses on the Critical Success Factors (CSF) for developing sustainable entrepreneurship in the Pakistani telecommunication industry. Despite the efforts made by governments and stakeholders to stimulate sustainable entrepreneurship initiatives, contributions in the telecommunications sector are lacking. Therefore, this study has the major objective of identifying a transformation path for these firms. This is done by providing a theoretical framework for sustainable entrepreneurship in the telecommunications industry, focusing on managerial and operational practices that should be modified according to a set of CSFs identified by experts in Pakistani firms.
Design/methodology/approach
This article proposes a novel Multiple Attribute Decision Making (MADM) approach based on Grey Systems Theory (GST) and Best-Worst Method (BWM) while unveiling endogenous relationships among current managerial/operational practices and the CSFs for sustainable entrepreneurship in the telecommunications industry.
Findings
CSFs for achieving sustainable entrepreneurship in the Pakistani telecommunications industry were found to rely on a tripod, based on effectiveness, transparency, and accountability that are embedded within the ambit of managerial and operational practices, such as focusing and reducing digital illiteracy, targeting poor communities, helping the young in structuring start-ups.
Originality/value
This article contributes to the MADM research stream by proposing a novel use of the BWM technique based on GST to promote sustainable entrepreneurship CSFs in Pakistani telecommunications firms.
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This article introduces the best-worst scaling object case, a quantitative method of producing individual level models of heterogeneous perceptions, for use in behavioural…
Abstract
Purpose
This article introduces the best-worst scaling object case, a quantitative method of producing individual level models of heterogeneous perceptions, for use in behavioural decision making research in projects. Heterogeneous individual perceptions refer to observed or unobserved differences between individual perceptions that impact the outcome being studied. Individual level models of perceptions are important to account for the impact of heterogeneous perceptions on measurement tasks, so they do not become an unobserved source of variance that potentially biases research inferences.
Design/methodology/approach
An overview of individual heterogeneity is provided highlighting the requirement for individual level models in quantitative perception measurements. A literature review is then conducted of the quantitative methods and tasks used to measure perceptions in behavioural decision making research in projects and their potential to produce individual level models.
Findings
The existing quantitative methods cannot produce the necessary individual level models primarily due to the inability to address individual level scale effects, responses styles and biases. Therefore, individual heterogeneity in perceptions can become an unobserved source of variance that potentially biases research inferences.
Practical implications
A method new to project management research, the best-worst scaling object case, is proposed to produce individual level models of heterogeneous perceptions. Guidance on how to implement this method at the individual level is provided along with a discussion of possible future behavioural decision making research in projects.
Originality/value
This article identifies a largely unacknowledged measurement limitation of quantitative behavioural decision making research in projects and provides a practical solution: implementing the best-worst scaling object case at the individual level.
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Tiziana de Magistris, Etiénne Groot, Azucena Gracia and Luis Miguel Albisu
The aim of this study is to analyse Millennial generation's preferences for wine attributes in two countries, one from the “New World” (USA) and the other from the “Old World”…
Abstract
Purpose
The aim of this study is to analyse Millennial generation's preferences for wine attributes in two countries, one from the “New World” (USA) and the other from the “Old World” (Spain), in order to see whether they are different. Heterogeneity in attribute importance is investigated, with wine consumers classified into different segments according to attribute importance.
Design/methodology/approach
The Best‐Worst choice method was used with information obtained from a survey conducted in two cities of Spain and the USA (Zaragoza and Fayetteville), respectively. Then, attribute importance heterogeneity was modelled and consumers were classified with a latent class model.
Findings
The results indicate that American and Spanish Millennial consumers present some similarities but also some differences in wine preferences. While Millennial consumers in the USA attributed more importance to “I tasted the wine previously”, Spanish Millennials ascribed more importance to the “designation of origin”. Moreover, heterogeneity in attribute importance in both countries was detected and five consumer segments were identified showing clear differences in terms of the importance attached to different wine attributes: “Traditionalists”, “Wine seekers”, “Label fans”, “Insecure” and “Price conscious”. These wine consumer segments could be characterized by traditional socio‐demographic profiles and only differed in wine consumer preferences.
Originality/value
The Best‐Worst method, used to compare wine consumers from the “New World” and the “Old World”, asks them to choose among hypothetical wines defined by a mix of traditional and novel attributes, according to previous studies.
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Amir Karbassi Yazdi, Peter Fernandes Wanke, Thomas Hanne and Eleonora Bottani
This paper aims to assess and prioritize manufacturing companies in the healthcare industry based on critical success factors (CSFs) of their reverse logistics (RL). The research…
Abstract
Purpose
This paper aims to assess and prioritize manufacturing companies in the healthcare industry based on critical success factors (CSFs) of their reverse logistics (RL). The research involves seven medical device companies located in the Tehran Province, Iran.
Design/methodology/approach
To identify and prioritize companies based on CSFs of RL, the study proposes a three-phase decision-making framework that integrates the Delphi method, the best-worst method (BWM) and the Additive Ratio Assessment (ARAS) method with Z-numbers. The weights required for this method are obtained by a variant of the BWM based on Z-numbers, denoted as Z-numbers Best-Worst Method, or ZBWM. Since decision-makers face an uncertain environment, Z-numbers, which are a kind of fuzzy numbers, are applied.
Findings
First, after customizing CSFs by the Delphi method and obtaining 15 CSFs of RL, these are ranked by the hybrid BWM-ARAS method with Z-numbers. Results reveal which company appears to perform best with respect to their RL implementations. Based on this result, healthcare device companies should choose the highest priority company based on the selected RL CSFs and results from using the BWM-ARAS method with Z-numbers.
Originality/value
The contribution of this paper is using a hybrid ARAS-BWM method based on Z-numbers. Each of these methods has some merits compared to other similar methods. The combination of these methods contributes a new approach for prioritizing companies based on RL CSFs with high accuracy and reliability.
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Faisal Talib, Mohammad Asjad, Rajesh Attri, Arshad Noor Siddiquee and Zahid A. Khan
Recent years have witnessed a significant rise in Indian healthcare establishments (HCEs) which indicate that there is a constant need to improve the healthcare quality services…
Abstract
Purpose
Recent years have witnessed a significant rise in Indian healthcare establishments (HCEs) which indicate that there is a constant need to improve the healthcare quality services through the adoption and implementation of TQM enablers. The purpose of this paper is to identify such enablers and then propose a ranking model for TQM implementation in Indian HCEs for improved performance.
Design/methodology/approach
The study identifies 20 TQM enablers through comprehensive literature survey and expert’s opinion, and classifies them into five main categories. The prominence of these enablers is established using a recently developed novel multi-criteria decision making (MCDM) method, i.e. best-worst method (BWM). The importance of the various main category and sub-category enablers is decided on the basis of their weights which are determined by the BWM. In comparison to other MCDM methods, such as analytical hierarchy process, BWM requires relatively lesser comparison data and also provides consistent comparisons which results in both optimal and reliable weights of the enablers considered in this paper. Further, a sensitivity analysis is also carried out to ensure that the ranking (based on the optimal weights) of the various enablers is reliable and robust.
Findings
The results of this study reveal that out of five main category enablers, the “leadership-based enablers (E1)” and the “continuous improvement based enablers (E5)” are the most and the least important enablers, respectively. Similarly, among the 20 sub-category enablers, “quality leadership and role of physicians (E14)” and “performing regular survey of customer satisfaction and quality audit (E52)” are the most and the least dominating sub-category enablers, respectively.
Research limitations/implications
This study does not explore the interrelationship between the various TQM enablers and also does not evaluate performance of the various HCEs based on the weights of the enablers.
Practical implications
The priority of the TQM enablers determined in this paper enables decision makers to understand their influence on successful implementation of the TQM principles and policies in HCEs leading to an overall improvement in the system’s performance.
Originality/value
This study identifies the various TQM enablers in HCEs and categorizes them into five main categories and ranks them using the BWM. The findings of this research are quite useful for management of the HCEs to properly understand the relative importance of these enablers so that managers can formulate an effective and efficient strategy for their easy and smooth implementation which is necessary for continuous improvement.
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Khaled Hamad Almaiman, Lawrence Ang and Hume Winzar
The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.
Abstract
Purpose
The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.
Design/methodology/approach
This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.
Findings
With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.
Research limitations/implications
Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.
Practical implications
Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.
Originality/value
Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.
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Polymeros Chrysochou, Athanasios Krystallis, Ana Mocanu and Rachel Leigh Lewis
The purpose of this paper is to explore differences in wine preferences between Generation Y and older cohorts in the USA.
Abstract
Purpose
The purpose of this paper is to explore differences in wine preferences between Generation Y and older cohorts in the USA.
Design/methodology/approach
A total of 260 US consumers participated in a web‐based survey that took place in April 2010. The best‐worst scaling method was applied measuring the level of importance given by participants to a list of most common attributes used in choice of wine. Independent sample t‐tests were applied to compare the best‐worst scores between Generation Y and older cohorts.
Findings
Differences were found in the level of importance that Generation Y gives to wine attributes in comparison to older cohorts. Generation Y was found to attach more importance to attributes such as “Someone recommended it”, “Attractive front label” and “Promotional display in‐store”, whereas older cohorts gave more importance to attributes such as “I read about it” and “Grape variety”. This suggests that Generation Y preferences for wine are driven by marketing added‐value activities such as promotions and labelling, whereas limited importance is given to information about wine, reflecting lack of subjective knowledge, experience and involvement about wine.
Research limitations/implications
This research adds to generation‐based research in wine marketing and contributes towards a better understanding of the differences between generation cohorts in relation to their preferences towards wines.
Originality/value
This study is among the first to compare wine preferences of Generation Y with older cohorts using the best‐worst scaling method.
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