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The purpose of this paper is to explore how multichannel customers evaluate overall satisfaction across distribution channels and what the antecedents are of such satisfaction.

A survey of bank customers in Taiwan was conducted. The total number of valid questionnaires was 479. Reliability and validity were tested. Maximum likelihood procedure of LISREL 8.8 was used to test the hypothesized structural equation model.

The findings indicate that the overall satisfaction in the multichannel environment is a critical determinant of customer retention and participation. The present study also develops the antecedents of multichannel satisfaction. In the multichannel environment, perceived multichannel service quality is positively related to satisfaction, while perceived channel switching difficulty is negatively related to satisfaction.

The present study employs the stimulus‐organism‐response (S‐O‐R) paradigm and the channel loyalty framework to better model customers' response to marketing activities in the multichannel distribution system.

This paper aims to investigate the determinants of successful online viral marketing. More companies in recent years have reduced their advertising expenditures on traditional media. Instead, they focus more on word-of-mouth marketing to reach their potential customers.

A 2 (high/low utilitarian message context) × 2 (high/low hedonic message context) × 2 (message source: strong/weak tie strength) × 2 (channel: e-mail/blog) between-subjects experiment was conducted. A total of 363 completed questionnaires were collected in Taiwan.

The findings are fourfold. First, the greater the tie strength between the sender and the receiver, the more actively they share information. Second, an audience is more willing to share a message with others when the message contains higher degrees of utilitarian or hedonic values. Third, those who are highly involved with the products are more willing to share information than those who are less involved. Fourth, those who access the information via blogs are more willing to share information with others.

The first limitation pertains to the issue of external validity. Also, to maximize internal validity, hypothetical scenarios and experimental designs were used rather than actual e-mail/blog experiences as stimuli. The results of this study provide some key strategic implications for companies that are seeking to enhance a successful viral marketing campaign.

This study suggests there is no “one size fits all” answer. A successful viral marketing campaign is specific to individual characteristics and the approaches used.

The present study combines related research – including communication theory, consumer value and involvement theory – to investigate the determinants of individuals’ intentions to share marketing information.

Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector.

Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs.

The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks.

The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.

The purpose of this paper is to develop chicken cutlets enrobed with bread crumbs vis-à-vis dried carrot pomace and to assess its effect on physico-chemical properties, sensory attributes and texture profile analysis.

Three experimental groups were made: control group chicken cutlets (C), chicken cutlets enrobed with bread crumbs group (T_b) and chicken cutlets enrobed with dried carrot pomace group (T_c). All the procedures used in the study for estimation of various physico-chemical properties, sensory evaluation and texture profile analysis were standard protocols.

There was a significant (p < 0.05) increase in water holding capacity, crude fibre content and ash content of enrobed chicken cutlets, whereas moisture, fat content and shrinkage of product were significantly (p < 0.05) decreased. The results for sensory evaluation and texture profile analysis of enrobed chicken cutlets were better than control group. Overall acceptability score of chicken cutlets enrobed with dried carrot pomace was revealed to be highest (7.5 ± 0.29) and that of control group was found to be lowest (6.4 ± 0.22). Hardness (N/cm²) value found for control group chicken cutlets, chicken cutlets enrobed with bread crumbs group and chicken cutlets enrobed with dried carrot pomace group were 2.2 ± 0.17, 3.1 ± 0.29 and 4.3 ± 0.27, respectively.

Future research may benefit to assess the effect of enrobing with bread crumbs and dried carrot pomace on mineral and vitamin content and lipid profile of meat products.

Enrobing of chicken cutlets with bread crumbs and dried carrot pomace improved the sensory attributes along with texture profile analysis. Hence, enrobing with bread crumbs and dried carrot pomace could be used as processing technology to improve sensory appeal, especially crispiness of meat products.

The sustainable and circular growth of agro-produce is crucial in today's era. The current paper focuses on identifying the drivers which promote sustainability as well as circularity in the agro-produce supply chain and favor its implementation in agro-produce supply chain.

Drivers are identified by carrying out a brief literature review and then short-listed based on the expert's opinion. The crucial drivers identified are then categorized into four groups, i.e. farms, social, government and organization. A DEMATEL-DANP approach is used to analyze the relation among subfactors and factors by deriving network relations map. Further, the weights of each subfactor are measured followed by calculating the ranks of the drivers in significance with successful implementation of sustainable circular economy.

This study has applied DEMATEL-DANP approach to analyze the relationship among four factors and twelve sub factors and their contribution towards adopting the culture of sustainable circular economy in agro-produce supply chain. The results show that “Integration of farmers” and “Updated infrastructure and tools of the farm to promote diverse farming” are primary drivers for implementing SCE in APSC.

The research explored the domains of sustainability and circularity separately. Drivers that could accelerate the implementation of circularity in agro-produce in a sustainable manner were identified. This will help in designing sustainable circular model for working of the agro-produce supply chain.

Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases. However, the performance of these systems is adversely affected by the missing values in medical datasets. Imputation methods are used to predict these missing values. In this paper, a new imputation method called hybrid imputation optimized by the classifier (HIOC) is proposed to predict missing values efficiently.

The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations (MICE), K nearest neighbor (KNN), mean and mode imputation methods in an optimum way. Performance of HIOC has been compared to MICE, KNN, and mean and mode methods. Four classifiers support vector machine (SVM), naive Bayes (NB), random forest (RF) and decision tree (DT) have been used to evaluate the performance of imputation methods.

The results show that HIOC performed efficiently even with a high rate of missing values. It had reduced root mean square error (RMSE) up to 17.32% in the heart disease dataset and 34.73% in the breast cancer dataset. Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases. It increased classification accuracy up to 18.61% in the heart disease dataset and 6.20% in the breast cancer dataset.

The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.

This paper presents the performance analysis of the automatic ticket vending machine (ATVM) through the functioning of its different hardware and software failures.

Frequent failures in the working of ATVM have been observed; therefore, the authors of the paper intend to analyze the performance measures of the same. Authors have developed a mathematical model based on different hardware and software failures/repairs, which may occur during the operation, with the help of the Markov process. The developed model has been solved for two kinds of failure/repair rates namely variable failures (very much similar to real-time failure) and constant failures. Lagrange's method and Laplace transformation are used for the solution of the developed model.

Reliability and mean time to failure of the ATVM are determined. Sensitivity analysis for ATVM is also carried out in the paper. Critical components of the ATVM, which affect the performance of the same, in terms of reliability and MTTF are also identified.

A mathematical model based on different hardware and software failures/repairs of ATVM has been developed to analyze its performance, which has not been done in the past.