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The purpose of this paper is to determine the role of service quality (SQ), trust and commitment to customer loyalty (CL) for telecom service users. Further, the moderating role of gender, marital status and connection type within the model was tested.

A measurement model was created based on valid 615 responses from Indian TSUs for SQ, trust, commitment and loyalty with the help of partial least squares structural equation modeling (PLS-SEM). Multi-group analysis (MGA) was conducted to understand the moderating effect of marital status, gender and connection type within the model.

The results suggest that, out of five dimensions of SQ, only responsiveness, assurance and empathy have a significant positive relationship with both commitment and trust. Tangibility has a significant positive relationship with trust only. Both commitment and trust have a significant impact on loyalty. It was noticed that both commitment and trust act as mediators between three SQ dimensions (assurance, empathy and responsiveness) and CL. MGA revealed that empathy and responsiveness positively induce trust in telecom users who are single. Whereas, assurance increases commitment toward telecom service providers in married users. Assurance and empathy significantly contribute toward commitment and trust, respectively, in male users as compared to females. Empathy was found important for postpaid users for trust-building, whereas trust was found to be more important for prepaid users to stay loyal to the service provider.

This article contributes toward understanding the role of SQ, trust and commitment to CL moderated by marital status, gender and connection type in an integrated model concerning telecom service.

The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.

The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.

The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.

A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability.

In this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance.

In the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently.

There is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone.

The performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.

In this paper, the objective is to perform mathematical modeling to optimize the steady-state availability of a multi-state repairable crushing system of a sugar plant using the evolutionary algorithm of Particle Swarm Optimization (PSO). The system availability is optimized by evaluating the optimal values of failure and repair rate parameters concerned with the subsystem of the system.

Mathematical modeling of the multi-state repairable system is performed to develop the first-order differential equations based on the exponential distribution of the failure and repair rates. These differential equations are recursively solved to obtain the availability under normalizing conditions. The availability of the system is optimized by using the PSO algorithm. The results obtained by PSO are validated by using the Genetic Algorithm (GA).

The availability analysis of the system concludes that the cane preparation (F1) is critical of the crushing system and the optimized availability of the system using PSO is achieved as high as 87.12%.

A crushing system of the sugar plant is evaluated as it is the main system of the sugar plant. The maintenance data associated with failure and repair rate parameters were analyzed with the help of maintenance records/logbook and by conducting personal meetings with maintenance executives of the plant. The results obtained in the paper helped them to plan maintenance strategies accordingly to get optimal system availability.

The purpose of this paper is to study the status and trend of intellectual capital disclosures by selected companies in India. Three categories of intellectual capital disclosures across six industry groups were measured. The relation of the three categories of disclosures, i.e. human capital, relational capital and structural capital disclosures with the measures of organisational performance such as sales, R&D, R&D intensity, net profit and export intensity has also been studied.

Based on National Industrial Classification 2008, six sectors, namely pharmaceutical, basic metals, industrial manufacturing, energy, financial services and information technology were included in the study and 20 companies were selected from each sector based on the availability of data from 2004-2005 to 2013-2014, thus, making a sample of 1,200 firm-years. For collecting the data, a list of keywords related to various dimensions of intellectual capital was prepared and the count of keywords was searched in the annual reports of the companies.

Significant and positive trend coefficients were found in the majority of the sectors. Analysis revealed that trend coefficients differed across various sectors indicating the presence of sector specificity. Results of trend analysis reveal that structural capital-related disclosures have stagnated in case of pharmaceutical sector after hitting the peak. Significant variations were found across sectors in terms of all three types of intellectual capital disclosures. Results of study empirically support the fact that intellectual capital disclosures tend to increase with size of the organisation.

As data have been collected from annual reports of the companies, the accuracy of the findings is limited to the accuracy of the reported data.

The study is an original piece of work. This study provides an insight into the disclosure trend of intellectual capital in an emerging economy.

This paper reviews studies that have examined how accounting information impacts commercial lending judgments. Issues discussed involve the usefulness of accounting data in lending decisions, effects of different accounting methods on lenders’ judgments, bankruptcy and default judgments, and decision processes pertaining to the use of accounting information in lending decisions. Additionally, the paper reviews the research on how audits and other forms of assurance influence commercial loan officers’ judgments. Topics include the way perceived auditor independence influences loan officers’ judgments, the impact of financial statement audits and audit opinions on lending decisions, how internal control reports and other CPA firm reports influence loan decisions, ways in which audit report disclosures and wording impact lending decisions, how perceived auditor quality affects lending decisions, and the effects of limited assurance engagements on loan officers’ judgments.

The purpose of this paper is to examine the effect of the psychological bond on behavioral loyalty (word‐of‐mouth, continuance and non‐audit services) to audit firms providing services to companies listed on the Polish Stock Exchange.

A model is proposed and tested using structural equation modeling with LISREL. Data were collected from top executives of companies listed on the Warsaw Stock Exchange.

The psychological bond has a positive effect on word‐of‐mouth, continuance and non‐audit services.

The current research is limited to the investigation of the psychological bond as an antecedent of behavioral loyalty in Poland. Future research should identify and assess other antecedents and replicate these across different countries. A longitudinal survey across different points in time might reveal more useful information about auditor‐client relationships.

Auditors need to consider ways in which they can develop the psychological bond with their clients. This bond is the basis for the client believing the audit firm is superior to others, which has been found to lead to behavioral loyalty in this study. In particular, management of the auditor brand and reputation, personal experience of the audit firm and alumni relations are discussed as ways of enhancing the psychological bond among client executives.

This study is the first to examine the impact of the psychological bond on behavioral loyalty in auditor‐client relationships.

This study proposes a model that influences customer citizenship behavior during COVID-19, and empirically tests the effects of fast-food restaurant customers' perceptions of justice (price and procedural justice) on trust; trust on satisfaction and loyalty; and trust, satisfaction and loyalty on customer citizenship behavior. Furthermore, it was questioned whether there was a disparity between customer expectations based on the restaurant's image and consumption experience.

The data were gathered from customers of fast-food restaurants in the shopping centers in Turkey. The data set, which included 437 valid questionnaires, was subjected to CFA for validity and reliability, SEM analysis for hypothesis and paired sample t-Tests for the research questions.

The findings of the study indicate that perceived justice affects customer trust, which, consequently, affects customer loyalty and satisfaction during the COVID-19 period. Findings also demonstrate that, while customer loyalty and trust increase customer citizenship behavior, customer satisfaction alone is insufficient to increase customer citizenship behavior. The study also shows that during the COVID-19 period, fast-food restaurants should have raised awareness of employees’ fair behaviors toward the customers and provided additional services to differentiate themselves in the market. Also, it indicates that customer expectations related to price, cleanliness and professional appearance of staff are not met after taking service.

No research has been found in the literature focusing on the expectations, justice, trust, satisfaction, loyalty and citizenship behaviors of fast-food restaurant customers in the COVID-19 pandemic process. Therefore, the results can fill the gap in relevant literature by testing the relationships between justice, trust, satisfaction, loyalty and citizenship during the pandemic and provide inferences for fast-food business owners.