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The Indian government is grappling in generating sufficient revenue resources through taxation to meet their expenditure on public goods and services. Therefore, the government authorities must possess adequate information on factors affecting the taxation revenue of the country to craft and execute policies effectively. Hence, this study endeavors to explore the determinants of tax revenue by incorporating conventional, economic policy and institutional factors.

The study employed the Auto Regressive Distributed Lag (ARDL) modeling by using the data set from 1991 to 2022 according to the availability of the data.

The findings illustrate that trade openness, life expectancy, value added by the manufacturing sector and per capita GDP (Gross domestic Product) positively affect the tax efforts of the government in the conventional determinants. Similarly, in economic policy factors, the financial deepening also exhibits a favorable effect. Conversely, the inflation rate positively boosts the tax efforts in the short run, but it ultimately erodes the tax effort of the government in the long run. In the institutional factors, the official development assistance also illustrates a positive effect.

The findings assert that the Indian government should devise better macro-economic and foreign trade policies with expediting the economic restructuring and bolstering their ability to manage and utilize the foreign aid assistance to boost the tax revenue of the country.

To the authors’ knowledge, this is the first study to incorporate these factors in the Indian context.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2024-0314

This study aims to investigate the determinants of GST_efficiency of the Indian states to assist the policymakers, government and GST council to devise their policies and strategies to boost the GST_efficiency of the Indian states.

The analysis has used the panel data set of 27 Indian states and 3 UTs with a time span of 2017–18 to 2022–23. The study has used the Generalized Method of Moment regression for exploring the determinants of GST_efficiency of the state governments in India.

The findings depict that sectoral composition, inflation rate, financial development, state’s self-reliance, per capita income and gross fiscal deficit have a significant effect on GST_efficiency of the state governments. The findings support the Tanzi effect 1977 and claim that the rise in the inflation level erodes GST_efficiency of the state governments. The rise in the self-reliance of the state government will make the Indian states self-dependent and will reduce their reliance on central transfers.

The government should make efforts to make the Indian states self-reliant by increasing the share of OTR (Own Tax Revenue) instead of increasing their revenue efficiency in short-run through devolution and central transfers. Moreover, the Indian government should devise their macro-economic policies to curb the inflation level and gross fiscal deficit of the state governments in the country.

To the best of the authors’ knowledge, this may be the first study to explore the determinants of GST_efficiency of the state governments in India.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

Research and development (R&D) is a vital strategy for firms to sustain their competitive locus and profitability in the global marketplace. Therefore, the existing research is engrossed in the correlation between firm performance (FP) and R&D intensity (RDI) meta-analysis. It also examined the ‘Type of Firm’ as a moderator in this relationship.

This study is motivated by its potential to address existing knowledge gaps, guide decision-making, influence policy and contribute to advancing theoretical and practical insights in the domain of business, economics and innovation.

This study is based on the secondary data. The researcher uses ‘Meta- Essentials 1.5’ for meta-analysis covering the studies of developed and emerging economies from 1985 to 2022.

The outcome conveys a small effect of magnitude between RDI and FP. It also indicates the positively significant linkage between them, directing that investing in R&D projects leads to improvement in the performance of companies. It also points out that private firms engaging in R&D activities have a negative while public firms have a positive correlation with their performance.

Understanding this linkage is imperative as it aids managers in making strategic decisions, the government in funding research-related schemes and investors in choosing R&D projects for investment.

This paper aims to examine the effect of procedural justice on affective commitment, through the mediating of organizational trust and job satisfaction.

Data were collected from 305 executives working in eight large cement organizations through a standardized questionnaire. Confirmatory factor analysis, structural equation modelling and mediation analysis were performed to examine the relationship.

Procedural justice significantly influenced job satisfaction and organizational trust directly. Organizational trust and job satisfaction are partially mediated by organizational justice and affective commitment. Interestingly, procedural justice does not influence affective commitment directly.

Procedural justice and affective commitment are crucial aspects of an organization. Limited research has been conducted linking procedural justice, organizational trust, job satisfaction and affective commitment. This study was conducted in the South Asian country of India, where power-distance prevails

The purpose of this study is to propose an integrated framework utilizing the theory of planned behaviour (TPB) and technology acceptance model (TAM) to augment the understanding on consumers’ showrooming behaviour.

Selective sampling was used for data collection. The integrated TAM-TPB framework led to 12 propositions, which were tested using partial least squares-structural equation modelling.

Both perceived relative search benefits offline and relative purchase benefits online significantly determined the consumers’ showrooming behaviour along with perceived ease purchasing online and the overall usefulness of the showrooming sequence. Results of the study revealed that the showrooming sequence helped consumers avoid the regret of making suboptimal product choices and paying a higher price for the same product. Online trust was found to partially mediate the relationship between consumers’ intention to showrooming and the actual showrooming behaviour.

Notwithstanding the fact that further research is required to arrive at definitive conclusions, this study is an initial move towards understanding the consumers’ showrooming behaviour, and the research provides meaningful insights.

As showrooming substantially erodes profits, devising strategies to defend showrooming customers becomes crucial. The findings of the study provide the basis for formulating strategies to counter showrooming customers.

The paper is amongst the first studies which helps enhance the understanding of consumers’ showrooming behaviour, which is an emerging area in the present multi-channel retailing environment.

This study identifies the facilitators and inhibitors for the adoption of e-learning for the undergraduate students of architecture. Nine constructs are identified as facilitators and five constructs are identified as inhibitors to the adoption of online learning systems in the context of the study. These constructs were used to propose a research model.

596 architecture undergraduates responded to a structured questionnaire. The questionnaire was finalized after a pilot study and included standard scale items drawn from previous studies. An exploratory factor analysis was followed by structural equation modeling (SEM) to test the proposed model.

All the identified facilitators emerged significant except social influence and price value. Furthermore, technology risk emerged insignificant while all other inhibitors had significant impact on Behavioral Intention to adopt e-learning.

The study has strong implications in academia as HEIs in developing countries need to make their students computer proficient, boost the implications of e-learning services by mitigating risks and motivating students to acquire knowledge through flexible e-learning modules.

The COVID-19 pandemic forced educational institutions to switch to online modes of learning. For students of architectural programs in a developing country like India, this has been unprecedented and has brought in a new set of challenges and opportunities. With the extension of the pandemic induced lockdown in educational institutions, students – and other stakeholders – have no choice but to adapt to this new normal of dependence on remote learning.

Purpose: The prominence of quality education for building sustainable development is undeniable and is distinctly pointed out in 1 of the 14 sustainable development goals (SDGs). In the same context, this study intends to investigate the role of university commitment, perceived organisational prestige, student satisfaction, and perceived employability in enhancing sustainability in higher education.

Need of the Study: To evaluate how student satisfaction mediates the relationship between university commitment, perceived organisational prestige, and perceived employability with sustainable university institutes.

Methodology: An adapted questionnaire was used in this study to capture the perception of 458 management graduates selected through the purposive sampling method. Partial least squares structural equation modelling (PLS-SEM) technique was used to analyse the data with the help of Smart PLS software.

Findings: The results of this study show that student satisfaction is the strongest predictor of sustainable university institutes. University commitment was found to lead to student satisfaction significantly. Furthermore, student satisfaction wasn’t found to play the role of mediator in the proposed model.

Practical Implications: This study aims to fulfil theoretical, research, and management implications for students, higher education institutes (HEIs), and policymakers. HEIs are recommended to instil university commitment, perceived organisational prestige and student satisfaction via various practices and amendments in their curriculum. Students are recommended to enhance their perceived employability to achieve career sustainability.

The present study aims at developing an integrated model designed to predict and explain the various factors that influence customers' behavioral intentions to use or not to use one particular SST, i.e. ATM services.

A list of households in a particular Indian city was obtained from the municipal corporation. 268 respondents were contacted using systematic sampling technique. Structural equation modeling was used to demonstrate the stability of the proposed model and to test the hypotheses.

The study finds that bank customers are less innovative and less optimistic to try out new technologies. Usefulness of the technology helps in developing positive attitude towards the technology. Customers' intentions to use technology are significantly affected by their attitude towards the technology.

The present study confines to only two banks and that too limited to the branches of these banks operating in one city only.

Despite extensive use of ATMs, the absence of direct interaction with bank staff has increased customers' apprehensions about the perceived risk. To reduce the customers concern about perceived risk because of security and privacy concerns, the bank should improve the quality of interaction with the customers to alleviate these apprehensions.

Lack of personal interaction generates doubts and queries in the minds of the people, especially those unaware or less aware of these technology-based services. Such a situation is quite prevalent in the developing nations (like India), where still a large number of people are apprehensive about using the latest technologies.