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The mobile phone industry in India is highly competitive, fast paced and technology-driven. In such a hyper competitive era, effective advertising is considered a key success driver for a mobile phone brand. The purpose of this paper is to identify advertisement effectiveness dimensions for Indian mobile phone industry and to develop hierarchical interrelationships among these dimensions in the Indian print context.

Structured Delphi approach is used to derive the set of dimensions for advertisement effectiveness. Further, techniques such as interpretive structural modeling and MICMAC analysis are used to establish hierarchical linkages among identified dimensions.

On the basis of experts’ opinion, refinement through structured Delphi resulted in the identification of 14 advertisement effectiveness dimensions specific to Indian mobile phone industry. Interpretive structural modeling assisted in the development of linkages among these identified dimensions based on their interrelations. Further, attention, relevance, excitability, liking and consumer preference, etc., turned out to be the dimensions of utmost importance for measuring advertisement effectiveness for the Indian mobile phone industry.

The present research work is limited to the recognition and development of hierarchical interrelationships among advertisement effectiveness dimensions specific to mobile phone business in the Indian print context only. Further studies may be carried out for other product or service category in some different media context.

The present research has several significant implications for academics and advertising practitioners involved in designing and developing promotional campaigns for mobile phone brands in India. The identified 14 dimensions and developed hierarchical model provide valuable insights for improving advertisement effectiveness.

This paper demonstrated successful implementation of Delphi and interpretive structural modeling technique to explore the research area of advertisement effectiveness.

Plagiarism is an epidemic for scholars that needs to be managed. Penalties do not seem to be able to stop people from indulging in it. Manipulation of emotions and values may help in discouraging people from plagiarism. Therefore, the purpose of this paper is to understand the association between felt emotion and plagiarism outcome behaviours. Another objective of the study is to see the role of virtues in discouraging people from plagiarism.

A scenario-based quasi-experimental method is used to collect the data. Graduate students from various Indian universities were invited for the experiment. The partial least square based structural equation modelling is used to test the measurement as well as path model.

The authors found that manipulated shame resulted in feelings of both international and external shame. When individuals feel internal shame, they avoid and discontinue plagiarism. They also try to repair the damage that they cause by plagiarism. However, feeling of external shame only encourages individuals to discontinue plagiarism behaviour. Virtues such as influence, competitiveness and equality weaken the relationship between internal shame and plagiarism-related outcome behaviour. At the same time, these virtues do not affect the relationship between external shame and outcome behaviours.

This study has important implications for the institutions of higher education. The study suggests that universities should provoke the emotion of shame through various communications to students to control the act of plagiarism by their students.

No study seems to have examined if the manipulation of emotions and values can help reduce the problem of plagiarism. This is an attempt towards bridging this important gap in literature. Therefore, findings of this study are of great value to scholars and content developers.

In today’s highly competitive business scenario, advertising symbolizes one of the key marketing activities. The purpose of this paper is to understand the relationship between advertisement effectiveness and consumer buying behavior for major automobile companies of central India.

A judicious mix of qualitative (focus group and Delphi technique) and quantitative (structured questionnaire survey) research methods was used for the present study.

Study found that for automobile sector, effective advertisements have a positive impact on consumer buying behavior. It suggests that customer’s attitude can be shaped favorably through effective advertising. Automobile companies must arrange special training sessions and learning programs for their sales force to ensure supportive customer purchasing behavior.

The scope of the present study is limited to major automobile companies of central India only. Although, the findings can be interpreted and generalized in a global business scenario.

The research work is first of its kind that attempts to study the impact of effective advertising on consumer buying behavior in Indian automobile context. The findings and recommendations can be used for strategic planning of training sessions and workshops for different stakeholders in automobile industry.

The purpose of this study is to identify the barriers to the adoption of blockchain technology in green supply chain management (GSCM) and further analyze the cause and effect relationship to prioritize the barriers for making strategic decisions.

The study examines 15 potential barriers related to the adoption of blockchain in GSCM which is identified from the literature review and finalized after subsequent discussions with industry professionals. Integrated Fuzzy-Decision-Making Trial and Evaluation Laboratory approach is used to analyze cause and effect relationships and prioritize the barriers. Fuzzy set theory is used to handle the uncertainty and vagueness associated with the personnel biases and data deficiency problems. Three small to medium enterprises’ (SMEs’) are considered for gathering data and further analyzing the crucial barriers that are impeding the adoption of blockchain technology in GSCM.

The findings reveal that “lack of management vision” and “cultural differences among supply chain partners” are the most influencing barriers, whereas; “collaboration challenges” and “hesitation and workforce obsolescence” are the most influential barriers in the adoption of blockchain in GSCM.

The study is developed based on 15 selected barriers which were further tested using data from three SMEs’ in the emerging economy of India. The adoption of blockchain technology in GSCM is at a nascent stage and more research studies are necessary to extend the knowledge base.

Managers need to eliminate the barriers and extend the blockchain technology application in GSCM. Managers need to develop the mission and vision of the company by doing proper alignment of blockchain technology with GSCM goals. Second, managers need to make strong collaborations and remove the hesitation and workforce obsolescence barrier by providing the right education and pieces of training.

Blockchain technology in GSCM is in a nascent stage. This study extends the knowledge base by identifying and further prioritizing the leading blockchain barriers that need to be overcome for effectively adopting blockchain in GSCM.

Today, industrial revolutions demands advanced technologies, means, mediums, tactics and so forth for optimizing their operating behavior and opportunities. It is probed that the effectual results can be seized into system by not only developing advance means and technologies, but also capably adapting these developed technologies, their user interface and their utilization at optimum levels. Today, industrial resources need perfect synchronization and optimization for getting elevated results. Accordingly, present study is furnished with the purpose to expose quality-driven insights to march toward excellence by optimizing existing resources by the industrial organizations. The present study evaluates quality attributes of mechanical machineries for seizing performance opportunities and maintaining competitiveness via synchronizing and reconfiguring firm's resources under quality management system.

In the present study, Kano’s integrated approach is implemented for supporting decision rational concerning industrial assets. The integrative Kano–analytic hierarchy process (AHP) approach is used to reflect the relative importance of quality attributes. Kano and AHP tactics are integrated to define global relative weight and their computational medium is adapted along with ratio analysis, reference point theory and TOPSIS technique for understanding robust decision. The study described an interesting idea for underpinning quality attributes for benchmarking system substitutes. A machine tool selection case is discussed to disclose the significant aspect of decision-making and its virtual qualities.

The decision executives can realize massive benefits by streaming quality data, advanced information, technological advancements, optimum analysis and by identifying quality measures and disruptions for gaining performance deeds. The study determined quality measures for benchmarking machine tool substitute for industrial applications. Momentous machine alternatives are evaluated by means of technical structure, dominance theory and comparative analysis for supporting decision-making of industrial assets based on optimization and synchronization.

The study linked financial, managerial and production resources under sole platform to present a technical structure that may assist in improving the performance of the manufacturing firms. The study provides a decision support mechanism to assist in reviewing the momentous resources to imitate a higher level of productive strength toward the manufacturing firms. The study endeavors its importance toward optimizing resources, which is an evident requirement in industries as the same not only saves money, escalates production, improves profit margins and so forth, but also gratifies the consumption of scarce natural resources.

The study stressed that advance information can be sought from system characteristics in the form of quality measures and attributes, which can be molded for gaining elevated outcomes from existing system characteristics. The same demands decision supports tools and frameworks to utilize data-driven information for benchmarking operations and supply chain activities. The study portrayed an approach for ease of utilizing data-driven information by the decision-makers for demonstrating superior outcomes. The study originally conceptualized multi-attributes appraisement framework associated with subjective cum objective quality measures to evaluate the most significant machine tool choice amongst preferred alternatives.

Physical security has remained an important reason for, and the consequence of, societal living. In recent times, the threat potential and the risks of loss and damage due to criminal activities have increased substantially. In Indian Railways, it is being increasingly felt that efficient security management is inevitable for the development and sustainability of desired state of affairs. The purpose of this paper is to address the broad goal of achieving optimal strategies for minimizing security threats to Indian Railway Security System (IRSS).

The authors use two forecasting techniques, namely, Delphi technique and Harva method, whose joint approach allows the authors to use both quantifiable (Delphi technique) and linguistic (Harva method) data. The choice of the two approaches provides a multi-method approach to the research problem.

Predicted trend toward the expected scenario in 2020 has more or less matched with the actual developments for improvement in security scenario of Indian Railways. The positive indications are that there is an improving trend, which is expected to lead to a much better state of affairs with certain inputs.

The joint approach of Delphi technique and Harva method is a multi-method example of original research work in the railway security, which can also be implemented in other security settings, such as aviation or marine security. A replication of the exercise closer to the target date will throw light on the exact state of affairs in the area of railway security in India.

An outcome in consonance with the present exercise has been the implementation of the policy developed on the basis of the forecasts. Policy efforts initiated in the recent past have been consistent with the features discussed in the study. From the above indicators, it can be inferred that some of the policy initiatives taken are in line with the trend status as predicted by the Delphi exercise.

Security has been considered as an important reason, as well as a consequence, of living in a society. It has been perceived as the condition of being protected against danger or loss, and also refers to the freedom from exposure to danger (protection), implying a feeling of assurance against danger. Research work in this area, thus, has strong social welfare implications. This is particularly so as the area of security gained importance, not only in India, but also across the world.

The present study is the first of its kind in the area of railway security using systems approach. The approach used is quite generic and can also be implemented in other security settings, such as aviation or marine security.

Block chain technology (BCT) has apparent capability of handling information in digital format, which has dragged attention of the practitioners for its utility in industrial and manufacturing practices. Conversely, the managerial adoption of BCT is relatively limited, which motivated the authors to identify crucial dimensions that can persuade the acceptance of BCT from an executive perspective. Thus, the present study is aimed to conduct to understand crucial barriers under BCT for managerial implementation in supply chain management (SCM) of small and medium enterprises (SMEs).

The present study investigated evident barriers to understand implementation of BCT. A questionnaire based survey is performed to collect primary data from service and manufacturing based companies in India. Survey responses are received online and the data is analyzed in a scorecard. The scorecard embedded the scribed entries of Likert scale to determine the relative score.

In present study, sixteen barriers from three categories named as technological, organizational and environmental are evaluated, where, five sub-barriers from technological domain, seven sub-barriers from organizational domain and four sub-barriers from environmental domain are evaluated. The findings of the study determined that the three factors, i.e. “complexity in setup/use”, “Security and privacy concern” and “Technological awareness” mostly affect the adaptation of BCT in SCM. Conversely, “Market dynamics”, “Scalability” and “Cost” do not influence the intention to adopt the technology.

Only few studies have endeavored to ascertain the BCT adoption in SCM of SMEs in developing country like India. Thus, the study is filling a momentous gap of mapping BCT dimensions in the scholastic literature. The findings are expected to enable SMEs to understand important factors to be considered for adopting BCT in their curriculum. Furthermore, the study may benefit the BCT developers and suppliers to endure customized solutions based on the findings.

Today, proficient practices are required to stimulate along various boundaries of the supply chain (SC) to exploit manufacturing resources economically, effectually and gracefully for retaining operational excellence. Accordingly, varieties of paramount practices, i.e. Lean, Agile, Resilient and Green practices, are integrated in present study with the objective to develop a Decision Support Framework (DSF) to select robust supplier under the extent of Lean-Agile-Resilient-Green (LARG) practices for a manufacturing firm. The framework is developed and validated in the Indian automotive sector, where the primary data is collected based on perceptions of the respondents working in an automotive company.

LARG metrics can ponder ecological balance, customer satisfaction, associations, effectiveness and sustainability and thus, the study consolidated LARG practices in one umbrella to develop a DSF. The analytical approach under DSF is developed by the integration AHP, DEMATEL, ANP, Extended MOORA and SAW techniques in present study to evaluate a robust supplier under the aegis of LARG practices in SC. DSF is developed by scrutinizing and categorizing LARG characteristics, where the selected LARG characteristics are handled by fuzzy sets theory to deal with the impreciseness and uncertainty in decision making.

The study has identified 63 measures (15 for Lean, 15 for Agile, 14 for resilient and 19 for Green) to support the robust supplier selection process for manufacturing firms. The findings of study explicate “Internal communication agility”, “Interchangeability to personnel resources”, “Manufacturing flexibility”, “degree of online solution”, “Quickness to resource up-gradation”, “Manageability to demand and supply change”, “Overstocking inventory practices” as significant metrics in ranking order. Additionally, “Transparency to share information”, “Internal communication agility”, “Manufacturing Flexibility”, “Green product (outgoing)” are found as influential metrics under LARG practices respectively.

A technical DSF to utilize by the managers is developed, which is connected with knowledge-based theory and a case of an automobile manufacturing firm is presented to illustrate its implementation. The companies can utilize presented DSF to impose service excellence, societal performance, agility and green surroundings in SC for achieving sustainable outcomes to be welcomed by the legislations, society and rivals. The framework represents an important decision support tool to enable managers to overcome imprecise SC information sources.

The study presented a proficient platform to review the most significant LARG alternative in the SC. The study suggested a cluster of LARG metrics to support operational improvement in manufacturing firms for shifting gear toward sustainable SC practices. The present study embraces its existence in enrolling a high extent of collaboration amongst clients, project teams and LARG practices to virtually eradicate the likelihood of absolute project failure.

Advanced manufacturing machines (AMMs) are searched as a momentous asset across the manufacturing societies for quenching and addressing the production units under economical circumstances, i.e. production of high-quality of goods under feasible cost. AMMs are significant in holding the managers against their rivals and competitors with high profit margins. The authors developed the decision support mechanism/portfolio (DSM-P) consist of knowledge-based cluster approach with a dynamic model. The purpose of research work is to measure overall economic worth of AMMs under objective and grey-imperfect (mixed) data by exploring the proposed DSM-P.

The authors developed the DSM-P that consist of knowledge-based cluster, three multi-criteria decision-making (MCDM) techniques-1-2-3 with complementary grey relational analysis-4(GRA), approach with a dynamic model (complied by technical plus cost and agility measures of AMMs). The proposed DSM-P enables the manager to map the overall economic worth of candidate AMMs under objective and grey-mixed data.

The presented DSM-P assist the managers for handling the selection problem of AMMs, i.e. CNCs, robots, automatic-guided vehicle, etc under mixed (objective cum grey) data. To enable the readers for intensely understand the work, the utility of proposed approach is displayed by illustrating a polar robot evaluation and selection problem. It is ascertained that the robot candidate-11 alternative is fulfilling the entire technical cum cost and agility measures.

The DSM-P provides more precise and reliable outcomes due to a usage of the dominance theory. Under the dominance theory, the ranks are obtained by MCDM techniques-1-2-3 are compared with ranks gathered by the GRA-4 under objective cum grey data, formed the novelties in presented research work. From a future perspective, the grey-based models in DSM-P can be built/extended/constructed more extensive and can be simulated by the same approach.

The purpose of this paper is to develop a multi-level hierarchical framework (evaluation index system) toward evaluating an “appraisement index” from the prospectus of measuring and monitoring resilient performance of the candidate industry.

In this reporting, vagueness, imprecision, as well as inconsistency associated with subjective evaluation information (aligned with ill-defined assessment indices of SC resilience performance), has been tackled by the application of fuzzy theory.

Subjective evaluation information (expressed in linguistic term) acquired from the committee of decision makers (called expert group), against different resilience indices/metrics, has been fruitfully explored through the proposed fuzzy-based resilience performance appraisement module. Finally, a case study from an Indian automobile company has been conducted from the perspective of checking effectiveness of the proposed methodology for evaluation of appraisement index indicating SC resilience extent.

This methodology might be successfully applied to help other decision-making problems from the perspective of performance appraisal and benchmarking of candidate alternatives/choices under predefined criteria and subjective evaluation circumstances.