Vimal Kumar, Pratima Verma, Sachin Kumar Mangla, Atul Mishra, Dababrata Chowdhary, Chi Hsu Sung and Kuei Kuei Lai
The paper aims to identify key human and operational focused barriers to the implementation of Total Quality Management (TQM). It develops a comprehensive structural relationship…
Abstract
Purpose
The paper aims to identify key human and operational focused barriers to the implementation of Total Quality Management (TQM). It develops a comprehensive structural relationship between various barriers to successfully implement TQM for sustainability in Indian organizations.
Design/methodology/approach
With the help of expert opinions and extant literature review, we identified the case of TQM failure companies and barriers to implement TQM effectively. Interpretive Structural Modeling (ISM) and fuzzy MICMAC techniques are employed to develop a structural model and the identified barriers are categorized based on their dependence and driving power in the various categories.
Findings
From the intensive case analysis, we identify fourteen barriers that constrain the successful implementation of TQM. The findings also provide a hierarchy of barriers in which the absence of top management involvement and ineffective leadership are the human barriers having the highest dependence.
Research limitations/implications
The critical inputs show the implementation of TQM in the firms being more proactive and well prepared in the selected five companies. The study's emphasis on barriers will help organizations in implementing TQM for better sustainability in an organizational context.
Originality/value
In the successful implementation of TQM, barriers need to be identified because failure has often eliminated the organizations from the market. Thus, TQM is the source of strength to achieve higher productivity, profitability, and sustainable business performance. The barriers must be identified to improve organizational performance to contribute to sustainable development.
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Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria…
Abstract
Purpose
Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.
Design/methodology/approach
In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.
Findings
The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.
Practical implications
It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.
Originality/value
Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.
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Philip Megicks, Atul Mishra and Jonathan Lean
Hitherto, assessments of the effectiveness of Indian microfinance institutions in achieving their economic and social goals have largely identified only limited success. Critics…
Abstract
Purpose
Hitherto, assessments of the effectiveness of Indian microfinance institutions in achieving their economic and social goals have largely identified only limited success. Critics of Indian regional rural banks (RRBs) and their prevailing culture have argued that a product‐focused rather than a market‐oriented approach to new service development (NSD) is responsible for their inadequate performance. With this in mind, this work aims to develop a conceptual understanding of the factors influencing market orientation in these institutions, and to assess its impact on outreach performance.
Design/methodology/approach
Following an extensive review of the literature on the Indian microfinance sector, market orientation and new service development, a model framework for understanding the relationships between the factors affecting outreach performance in Indian RRBs is developed. Research propositions for further evidence‐based investigation are posited.
Findings
The attitudes and behaviours of managers, along with institutional characteristics, are identified as influences on market orientation, service innovation, customer satisfaction and outreach performance within RRBs.
Research limitations/implications
This paper is theoretical in its nature and as such proposes a basis for a detailed empirical examination of the proposed model and its associated propositions.
Practical implications
Banking practitioners need to be aware that market orientation may influence NSD and performance in this and related contextual situations.
Originality/value
The conceptual relationships proposed inform those seeking to enhance the performance of RRBs of some of the key internal marketing issues involved in their success, and thus give direction to the development of policies to reduce poverty. In particular, cultural issues relating to perceptions of customers and their effect on effective NSD are examined.
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Atul Shiva, Nilesh Arora and Bikramjit Rishi
Celebrity endorsement is a preferred marketing communication strategy adopted by business firms. The present study suggests theoretical underpinnings for investigating the effect…
Abstract
Purpose
Celebrity endorsement is a preferred marketing communication strategy adopted by business firms. The present study suggests theoretical underpinnings for investigating the effect of celebrity endorsement on individual investors' intentions to invest in the shares of companies. The study integrates marketing communication and behavioural finance theories to understand investor behaviour in the stock market.
Design/methodology/approach
The study used a questionnaire based on a conjoint analysis technique. The retail investors from India filled out the questionnaire. The authors developed an orthogonal design to generate retail investors' investment intentions and applied the full-profile conjoint method.
Findings
The results reveal that investors prefer to invest in technology-related firms when they employ entertainment celebrities to endorse their products. Investors prefer that entertainment celebrities' personalities match the single brand only they are endorsing. Further, investors choose to invest during corrective market trends in emerging economies, such as India.
Originality/value
The study offers practical implications for corporate entities and marketing professionals by analysing retail investors' investment intentions in financial markets.
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Atul Kumar Sahu, Sri Yogi Kottala, Harendra Kumar Narang and Mridul Singh Rajput
Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of…
Abstract
Purpose
Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.
Design/methodology/approach
DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.
Findings
The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.
Research limitations/implications
The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.
Originality/value
To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.
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Atul Rawat, Sumeet Gupta and T. Joji Rao
This study aims to focus on identifying the business risks that cause a delay in the oil and gas projects and suggest the way forward toward the better development of the city gas…
Abstract
Purpose
This study aims to focus on identifying the business risks that cause a delay in the oil and gas projects and suggest the way forward toward the better development of the city gas distribution (CGD) sector in India by suggesting the appropriate mitigation strategies.
Design/methodology/approach
The study is a systematic review of literature on risks causing a delay in oil and gas projects. Comprehensive literature was carried out following a seven-step model to develop an exhaustive list of risk classifications and factors, risk identification methods and strategies to mitigate the risks. Weighted average ranking method is used to identify the top ten risks affecting oil and gas projects.
Findings
This research identifies the top ten risks frequently impacting the oil and gas projects, which are project cost, improper project management, change in economic parameters, currency exchange rate, government regulations and laws, contractor and subcontractors issues, lack of skilled labor, delay in approvals, health and safety issues and force majeure. These risks are primarily responsible for cost overrun and project delay. Additionally, this study recommends the implementation of joint risk management to avoid CGD project delay.
Originality/value
The CGD industry is in the growing stage with many projects under construction. However, there is a lack of research to manage risks in the CGD project. This study contributes to the limited literature available on risk management in oil and gas projects. Additionally, it highlights the need for further research to explore the different risks factors affecting the CGD business and its operations and subsequently develop appropriate mitigation strategies.
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Anoop Kumar Sahu, Nitin Kumar Sahu and Atul Kumar Sahu
The purpose of this study is to design a DSS for construction sectors, which can determine the status of the related supplier alternatives, accompanying G-T, SC measures and their…
Abstract
Purpose
The purpose of this study is to design a DSS for construction sectors, which can determine the status of the related supplier alternatives, accompanying G-T, SC measures and their interrelated metrics. In today’s era, a supplier is observed as significant among entire agents of green supply chain (SC) management. Presently, it is determined that appraising worth of the supplier under green-traditional (G-T), SCs concerns still require the support of novel algorithmic/decision support systems (DSSs), which could embrace potential decision-making.
Design/methodology/approach
The authors have proposed a DSS (consisting of the implementation of multi-level multi-criterion decision-making [ML-MCDM], reference point approach [RPA] and multi-objective optimization on the basis of simple ratio analysis [MOOSRA] methods on constructed MCDM supplier evaluation appraisement module) for measuring the performance score of clay-brick suppliers coming under G-T SCs corresponding to fuzzy and non-fuzzy information. A comparative analysis is conducted among the performance scores against alternatives, obtained by the three methods, i.e. ML-MCDM, RPA and MOOSRA, for robustly making a potential decision.
Findings
The presented research offers a DSS toward managers of construction sectors for benchmarking the performance scores against supplier alternatives under G-T SC measures and their interrelated metrics, modeled by fuzzy cum non-fuzzy information.
Originality/value
Presented research work exhibited a DSS that can be used by construction sectors for benchmarking the supplier alternatives in accordance with their performance scores under G-T SCs. The MCDM G-T supplier evaluation appraisement module is constructed pertaining to small-scale clay-brick production units, located in the northern part of India to check the effectiveness of the proposed DSS.
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Nan Li, M. Prabhu and Atul Kumar Sahu
The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective…
Abstract
Purpose
The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.
Design/methodology/approach
The study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.
Findings
The findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.
Originality/value
A conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.
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Subramanian Surya Narayanan and Parammasivam K.M.
The purpose of this paper is to comprehensively evaluate the progress in the development of trapped vortex combustors (TVCs) in the past three decades. The review aims to identify…
Abstract
Purpose
The purpose of this paper is to comprehensively evaluate the progress in the development of trapped vortex combustors (TVCs) in the past three decades. The review aims to identify the needs, predict the scope and discuss the challenges of numerical simulations in TVCs applied to gas turbines.
Design/methodology/approach
TVC is an emerging combustion technology for achieving low emissions in gas turbine combustors. The overall operation of such TVCs can be on very lean mixture ratio and hence it helps in achieving high combustion efficiency and low overall emission levels. This review introduces the TVC concept and the evolution of this technology in the past three decades. Various geometries that were explored in TVC research are listed and their operating principles are explained. The review then categorically arranges the progress in computational studies applied to TVCs.
Findings
Analyzing extensive literature on TVCs the review discusses results of numerical simulations of various TVC geometries. Numerical simulations that were used to optimize TVC geometry and to enhance mixing are discussed. Reactive flow studies to comprehend flame stability and emission characteristics are then listed for different TVC geometries.
Originality/value
To the best of the authors’ knowledge, this review is the first of its kind to discuss extensively the computational progress in TVC development specific to gas turbine engines. Earlier review on TVC covers a wide variety of applications including land-based gas turbines, supersonic Ramjets, incinerators and hence compromise on the depth of analysis given to gas turbine engine applications. This review also comprehensively group the numerical studies based on geometry, flow and operating conditions.
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R.V. ShabbirHusain, Atul Arun Pathak, Shabana Chandrasekaran and Balamurugan Annamalai
This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.
Abstract
Purpose
This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.
Design/methodology/approach
A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.
Findings
This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.
Research limitations/implications
The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.
Practical implications
The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.
Originality/value
The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.