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This paper aims to examine various factors affecting the pricing of audit services and the selection of auditors in the Indian audit market. This paper also aims to investigate the impact of financial distress conditions on the audit pricing and auditor choice decisions of a firm, particularly in the context of a developing economy.

The sample comprises 22,644 firm-years for 1,366 Indian firms from 1990 to 2015. The authors adopt ordinary least squares regression technique to model audit fee, and logistic regression technique to model auditor choice as a function of various factors relating to firm attributes and auditor characteristics.

This paper finds that auditors tend to charge an audit fee premium when they are affiliated to a Big 4 auditor, have industry specialization or jointly provide auditing and non-auditing services. Additionally, firms with larger boards, higher proportion of independent board of directors and CEO–Chairman separation are more likely to choose a Big 4-affiliated auditor. The results also suggest that financially distressed firms tend to pay significantly lower audit fees and are more likely to choose non-Big 4 auditors.

This paper is among the few studies which investigate how financial distress impacts the audit pricing and auditor choice decisions of a firm in the context of emerging economies. The findings of this paper raises serious concerns about the credibility of the audited financial statements and corporate governance mechanisms of firms undergoing financial distress. The empirical results of this paper have strong implications for practitioners, regulators and investors.

In the context of India, this article proposes an integrated multicriteria decision-making (MCDM) regression-based methodology to evaluate input-level performance of the schools and investigate the impact of this performance along with contextual factors, i.e. medium of instruction and location of the school, on the school's output level performance, i.e. student pass rate.

First, Shannon entropy-based approach is applied for the weight assignment to different parameters. Then, integrated VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique for order preference by similarity to an ideal solution (TOPSIS)-based methodology is devised to measure the input-level performance of a school. Finally, multiple linear regression (MLR) analysis is incorporated to study the effect of input-level performance and above-mentioned contextual factors on the school's output-level performance.

Proposed methodology is applied to assess the input-level performance of 82,930 primary and secondary schools of West Bengal, India. All the factors have a significant impact on boys' pass rate, whereas only input-level performance and location of the school have a significant influence on the girls' pass rate.

The entropy-based approach highlights the importance of scientific weight assignment. Integrated MCDM demonstrates the significance of aggregation due to the variation in scores related to input-level performance across the methods. Regression analysis facilitates the exploration of determinants influencing the output-level performance of the schools.

This work depicts a holistic picture of the performance measurement system of the schools. It encompasses scientific weight assignment to the evaluation criteria, integrated input-level performance assessment of the schools and investigation into the effect of this performance, as well as other contextual factors on the output level performance.

Although comprehensive work has been conducted in several scholarly journals in electronic service quality (e-SQ) measurement, there has been no cross-functional review of these studies. The majority of the review studies focus on e-SQ assessment in the field of electronic retail. This paper explores and synthesizes e-SQ evaluation work across the various functional domains in the last two decades and maps critical methodological challenges. It further classifies the dimensions used by researchers in six broad categories for better comprehension.

To analyze and appreciate past e-SQ measurement research, a content assessment of the 50 most relevant research papers from various functional domains drawn from prestigious repositories was undertaken.

The results indicate shortcomings noticed in methodological issues in the e-SQ measurement research like research approaches, data analysis procedures, sampling methods, generation and purification of items, validity and reliability assessment, and dimensionality analysis. It further reveals that though e-SQ is multi-dimensional and context-specific, dimensions like content/information quality, website design, ease of use/usability, efficiency, security/and privacy, responsiveness, reliability, customer service, trust and fulfillment have been consistently cited in the reviewed studies across the various functional domains.

An assessment of 50 publications over the past 2 decades identifies key areas of concern in the existing research on e-SQ measurement in various functional domains for scholars and professionals. This study also provides a unique categorization of e-SQ dimensions used in various functional domains and has the potential to guide future research.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.