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Retail stores are required to provide a stimulating in-store experience for customers and do this by developing various strategies. One strategy implemented by retailers is the use of sensory cues to encourage consumers to engage with the environment and the products on display and available for purchase. Conducted in a lighting department, the purpose of this paper is to consider how retailers can employ a multisensory cue, which is formed by combining three single cues to positively impact consumer behaviours – specifically time spent, touching and purchase.

The study comprised an experimental design, which implemented single congruent visual, auditory and olfactory cues that formed a multisensory cue. Consumer behaviour outcomes of these cues were measured using objective measures.

The results show that a multisensory cue impacts time spent and purchasing, but no evidence of it affecting touching was noted. In the case of the single cues, auditory and scent cues impacted time spent, but their effect was not to the extent of the multisensory cue, which was superior.

The study focussed on one product category within a general furnishing store, thus limiting the extent to which the findings can be generalised.

The effect of a multisensory cue exceeded that of single cues, emphasising the need for retailers to consider and develop a multisensory retail environment.

While research into the effect of single cues on consumer behaviours has shown positive effects, research into a multisensory cue, especially in a real-retail setting, is relatively scarce.

The purpose of this paper is to examine the influence of magnetic field on Williamson nanofluid embedded in a porous medium in the presence of non-uniform heat source/sink, chemical reaction and thermal radiation effects.

The governing physical problem is presented using the traditional Navier–Stokes theory. Consequential system of equations is transformed into a set of non-linear ordinary differential equations by means of scaling group of transformation, which are solved using the Runge–Kutta–Fehlberg method.

The working fluid is examined for several sundry parameters graphically and in a tabular form. It is noticed that with an increase in Eckert number, there is an increase in velocity and temperature along with a decrease in shear stress and heat transfer rate.

A good agreement of the present results has been observed by comparing with the existing literature results.

The purpose of this paper is to contribute to the limited body of empirical knowledge on the impact of Lean Six Sigma (LSS) program implementations on organizational performance in financial services by investigating how antecedents of Lean Six Sigma program success (motivations, selected LSS methods and challenges) affect organizational performance enhancement via LSS program performance.

A sample of 198 LSS professionals from 7 countries are surveyed. Structural equation modeling (SEM) is performed to test the questioned relations.

This study’s findings comprise: (1) LSS program performance partially mediates the relationship between motivations for LSS implementation and organizational performance, (2) selected LSS method applications has a fully (mediated) indirect impact on organizational performance, (3) LSS implementation challenges also have an indirect (mediated) impact on organizational performance and (4) LSS program performance has a positive impact on organizational performance.

The findings of this research predominantly provide nuances and details about LSS implementation antecedents and effects, useful for managers in advising their business leaders about the prerequisites and potential operational and financial benefits of LSS implementation. Furthermore, the paper provides evidence and details about the relationship between important antecedents for LSS implementation identified in existing literature and their impact on organizational performance in services. Thereby, this research is the first in providing empirical, cross-sectional, evidence for the antecedents and effects of LSS program implementations in financial services.

The purpose of this paper is to present different naming, packaging, and pricing strategies adopted by private label (PL) retailers in India. This study also aims to identify preferred private label brand (PLB) categories, factors influencing their selection, and the importance of cues in evaluation of PLBs. The overall purpose is to identify important areas for future research of PLBs in the wake of organized retail growth in an emerging economy (India is the context here).

This study is based on in-store observations of major Indian retail chains, longitudinal analyses of customers’ shopping bills, qualitative analyses of consumer interviews, and focus group discussions.

The results indicate that retailers primarily adopt “Sub-branding” (using the store name along with a separate brand name) and “House of Brands” (using a separate brand name only) strategies to sell PLBs in the Indian market. Groceries, food and beverages, and apparel are the preferred categories in PLB. Price, quality, and convenience are the major factors influencing PLB. Taste, ingredients, packaging, price, brand name, and store name are the main factors that are used to evaluate PLBs.

Due to the qualitative analyses and interpretation, there are limitations to this study which need to be empirically validated.

This research has implications for organized retailers in understanding the various strategies used for PLBs in India.

This study is a novel study for documenting the PLB strategies adopted by organized retailers in India. It also uses a longitudinal exploratory approach to further understanding the consumption of PLBs in India.

The authors investigate the manufacturer's choice of discount schemes in a supply chain with competing retailers.

Using a game-theoretic model, the authors build two discount frameworks and compare and analyze the effects of different discount schemes on the performance of supply chain members.

The authors find that the retail price (market demand) in the quantity discount scheme is always higher (lower) than that in the market share discount scheme. The authors also find that the retailers' preference for discount schemes is antithetical to the manufacturer's preference in most cases. However, under certain conditions, there will be a win-win situation where Pareto-optimization occurs between the manufacturer and retailers when they choose the same discount scheme.

On the one hand, the authors assume that the two retailers are symmetrical in market size and operation efficiency. It would be interesting to study the effect of different discount schemes on retailers when the retailers have different market sizes or operating efficiency. On the other hand, the authors study the manufacturer's choice of discount schemes in a supply chain with one common manufacturer and two competing retailers. However, in practice, there exist other supply chain structures. Future research can examine the problem of choices of discount schemes in other different supply chain structures.

This paper help retailers and manufacturers to choose the best discount schemes.

This paper suggests that a high discount scale is not always beneficial (detrimental) to retailers (the manufacture).

The authors build two discount schemes (the quantity and the market share) in a supply chain consisting of one manufacturer and two retailers, and the authors focus on the effects of different discount schemes on the competition between two retailers. By comparing the two discount schemes, the authors study which discount scheme is the better choice for the manufacturer when facing competing retailers.

Gene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.

The proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.

The proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.

This paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.

The present study aims to explore the barriers to Lean Six Sigma (LSS) implementation in the healthcare sector and develop the ranking of finalized barriers using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach under a fuzzy environment.

The LSS barriers are identified through the literature review and validated by the expert's opinion and statistical analysis. A total of 124 experts were identified through the purposive sampling method for conducting this study. A questionnaire survey method is used to collect the data related to identified LSS barriers in the healthcare sector. The screened barriers are ranked through the Fuzzy DEMATEL approach.

In this study, a total of 21 barriers were identified with the help of a systematic literature review and screened 13 significant barriers by the expert opinions of healthcare personnel. The result reveals that “Lack of top management commitment and support, lack of awareness about LSS”, “resistance to culture change and inadequate resources emerges as the most critical barriers”. The prioritization of barriers facilitates the managers to make effective policies and guidelines for LSS implementation in healthcare organizations.

To avoid LSS implementation failure, the practitioners and researchers need to focus on LSS barriers as per suggested ranking more conventionally and make plans and adoption policies accordingly.

This study is unique in terms of investigation and empirical analysis of LSS implementation barriers in the healthcare sector in the Indian context. The outcomes of the present study will help the managers of healthcare organizations to make the strategies and policies for LSS implementation as per the recommended LSS barriers.

The purpose of this paper is to investigate the impact of fairness concerns of the retailer on the pricing policies of the supply chain partners, their individual profits, and the overall performance of a dual-channel supply chain composed of one manufacturer and one retailer. First, the authors model the dual-channel supply chain under retailer’s fairness concern. Second, the authors derive the optimal pricing policies of the channel members. Third, the authors analyze the effects of retailer’s fairness and bargaining power on the pricing strategies and profit functions of the dual-channel supply chain system.

The authors adopt the manufacturer-led Stackelberg game theoretic framework, where the dominant manufacturer’s pricing decisions are based on the retailer’s pricing decision. The paper considers Nash bargaining solution as the fairness reference point to formulate the utility function of the fair-retailer. The paper uses this approach because it endogenously accounts for the competitive power and cooperative contribution of the channel members when they interact.

The authors find that the retailer’s fairness concerns are not always beneficial for its better performance. If the retailer is moderately sensitive towards its fairness, it will positively influence its performance. However, if the fairness concern becomes too high then it will negatively impact the retailer’s performance because it results in customers’ migration towards direct online channel for buying the products. In addition, if the retailer’s fairness concerns are mild, the manufacturer’s prices will decrease in retailer’s bargaining power, which is opposite otherwise.

The authors use Nash bargaining solution model as the fairness reference in the context of dual-channel supply chain, which is comparatively a recent approach and has been used independently from dual-channel supply chain system.

Mobile-based payment is increasing exponentially but in the developing country like India, consumers’ perception is highly positive in daily cash transaction. The purpose of this research is to identify and examine the important challenges for mobile wallet (m-wallet) implementation in India. In the wake of COVID-19, one of the transmission mechanisms of this virus has been the coins and paper money passed between a buyer and a seller. As such m-wallet considered as a convenience of payment has become a necessity in light of the pandemic.

The authors explored 19 unique sets of challenges selected from the literature and collected data from 14 experts from private sector, multinational corporations and mixed private and public partnership who have significant knowledge and experience of mobile payment implementation and use in their respective organisations. Also, the authors have used Interpretive Structural Modelling (ISM) methodology in developing a hierarchal model for the identified challenges. The authors implemented Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis to classify the identified challenges.

The ISM-based framework is divided into nine different hierarchical levels. “Lack of strong regulatory compliance (Ch6)” has been recognised as the most important challenge, which inhibited the mobile wallet implementation, whereas “Perception of customers about the value of using mobile wallets (Ch11)” is the most dependent critical challenge. There are seven hierarchical layers in between the top and the bottom level with the varied number of challenges based on their driving and dependence power.

This is the first research to the best of our knowledge that has not only comprehensively reviewed the m-wallet literature but also employed a unique ISM-MICMAC-based approach to develop a framework of challenges for the m-wallet implementation.

The study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.

In this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L₉ orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.

The study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.

In the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.