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This study aims to capture how the association between a multichannel relational communication strategy (MRCS) and customer performance is contingent upon such customer performance (low- versus high-performance customers) and to reconcile past contradictory results in this marketing-related topic. To this end, the authors propose and validate the method of quantile regression as an unconventional, yet effective, means to proceed to that reconciliation.

This study collected data from 4,934 customers of a private pension fund firm and accounted for both firm- and customer-initiated relational communication channels (RCCs) and for customer lifetime value (CLV). This study estimated a generalized linear model and then a quantile regression model was used to account for customer performance heterogeneity.

This study finds that specific RCCs present different levels of association with performance for low- versus high-performance customers, where outcome customer performance is the dependent variable. For example, the relation between firm-initiated communication (FIC) and performance is stronger for low-CLV customers, whereas the relation between customer-initiated communication (CIC) and performance is increasingly stronger for high-CLV customers but not for low-CLV ones. This study also finds that combining different forms of FIC can result in a negative association with customer performance, especially for low-CLV customers.

The authors tested the conceptual model in one single firm in the specific context of financial services and with cross-sectional data, so there should be caution when extrapolating this study’s findings.

This study offers nuanced and precise managerial insights on recommended resource allocation along with relational communication efforts, showing how managers can benefit from adopting a differentiated-customer performance approach when designing their MRCS.

This study provides an overview of the state of the art of MRCS, proposes a contingency analysis of the relationship between MRCS and performance based on customer performance heterogeneity and suggests the quantile method to perform such analysis and help reconcile past contradictory findings. This study shows how the association between RCCs and CLV varies across the conditional quantiles of the distribution of customer performance. This study also addresses a recent call for a more holistic perspective on the relationships between independent and dependent variables.

This paper aims to analyze the technical efficiency of Indian State governments in providing elementary education (EE) and to identify the determinants of their technical inefficiency.

The Generalized Stochastic Frontier Approach (GSFA) is used in the context of the Inefficiency Effects Model to simultaneously estimate the frontier production function and the technical inefficiency model. Panel data of 28 Indian States from 2009–10 to 2018–19 is used.

The mean efficiency of States stands at 86%. Efficiency varied between 67 and 97%. 96% of the inter-state disparity in EE outcomes can be explained by inefficiency. Arunachal Pradesh is the least efficient State, followed by Sikkim and Tripura. Efficiency estimates were observed to change across States over the study period. Proportion of government schools, rural population, and proportion of Schedule Caste and Schedule Tribe children are the major determinants of inefficiency.

This study emphasizes that efficient resource management is as important as adequate resource allocation for achieving positive EE outcomes. It distinguishes resource-poor States from inefficient ones, providing insights to enhance States’ efficiency, and aiding policymakers in formulating strategies for ensuring equitable and quality EE.

This is the first paper to apply GSFA (for Indian States), providing a more robust estimation of efficiency. The Inefficiency Effects Model is employed that address the limitations inherent in the two-stage approach.

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.

This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises (OEs).

The paper has used a multiple-step approach. At the first stage, the initial data has been collected from interviews with 164 top managers of SMEs in West Azerbaijan in Iran during two periods of 2013–2015 and 2017–2019. At the second step, multiple correspondence analysis has been used to summarize the relationships between variables and construct indices for different groups of TBs. Finally, the generalized structural equation model method was used to examine the impact of export barriers.

The results showed that the political legal index is the main TBs for BEs and NEs, but it had a more significant impact on BEs; the financial index was the second major TBs factor for BEs, while OEs did not have a problem in performance index, and the financial index was classified as a minor obstacle for them. All indicators of marketing barriers (except production index) had a negative and significant effect on all enterprises; the most important TBs for NEs was the information index.

The results indicated that if enterprises have a strong financial system and function, they can lessen the impact of sanctions and keep themselves in the market.

This multiple case study research aims to identify the characteristics of scaling up SMEs in Chile for exploring how and why some entrepreneurship in the information technology (IT) sector are able to scale up and develop sustainable strategies, based on three consecutive years. The average sales of the companies during the last period analysed was around US$1,323,579, with an average annual growth rate of 66.7%. Scaling up SMEs may require several attributes to achieve positive revenue and develop effective high growth rates that allow them to succeed over several years.

To discern the phenomenon of entrepreneurship, the methodology of multiple case study research was conducted in three parts. The first was to define and design the research process, in which the study should settle the theory analysis and then show that research propositions and questions. The second part of the research was to prepare, collect and analyse the data through crafting instruments and data collection protocols as a source of evidence to conduct the pilot and multiple case study. In this stage, interviews were scheduled, transcribed, analysed and coded to explore how individual attributes may create a scaling-up entrepreneurial process for maintaining or developing high performance in the IT sector. The last part of the research concludes and validates the research propositions for the identification for potential attributes, which were obtained during the qualitative study.

Attributes were selected when 13 or more SMEs reported the importance of this initiative for the process of scaling up their SMEs. As a result of the data analysis, the empirical findings suggest on the importance of the academic background, budgetary control, negative entrepreneurial experiences, building teams, geographical expansion and first critical experience as key attributes for scaling-up. Additionally, the data propose that constructive entrepreneurial ecosystem and reforms financing markets and programmes are two additional components that could moderate the interaction between the scaling-up process and the achievement of rapid sales results as a key outcome measure.

The first limitation was the lack of consensus on the phenomenon of the scaling up of entrepreneurship. Information in Latin America and emerging countries is scarce, which also represents an opportunity for other researchers to deepen and validate the results reported here. Even though it was an attempt to understand the issue of environmental change, this additional limitation did not allow the evaluation of these adjustments over time that can positively or negatively drive the strategies corresponding to the evolution in each of the moderator variables.

Because of the characteristics of the sample in terms of size of the SMEs, industrial sector, location, culture, socio-economic environment and years of establishment of the company, the study cannot be generalised in terms of other industrial sectors or countries. The results of this research are also limited to SMEs in Chile, and to the extent that it can be applied to emerging countries IT sectors with similar sample characteristics, it must be done so with caution. Yin states that eight cases “are sufficient replications to convince the reader of a general phenomenon”.

Policymakers have the option to identify what skills and knowledge the entrepreneur requires to be trained to scale up their established ventures. In this context, they will also benefit from the empirical contribution of knowing what the restrictions that limit this process are, such as adverse tax systems and public strategies. Additionally, it is of public interest because no national records exist on the presence of theoretical terms.

Even though the literature promotes the present findings, it shows that there is an absence of empirical evidence in emerging economies to better comprehend which factors may affect the development process of scaling up entrepreneurship in the IT sector. Both deliberate and emergent strategic initiatives are necessary for the scaling-up process where six critical factors are the basis of the scaling-up. This empirical contribution for entrepreneurs will support the achievement of rapid and sustained sales results for scaling up their ventures.

Integrating the resource-based view (RBV) with pay dispersion research, the authors examine how the allocation of resources between hiring new employees and compensating current employees, as well as the allocation of resources among new employees, affects organizational performance.

The authors use panel data on Major League Baseball teams. The authors also use system generalized method of moments (GMM) estimations to control for the impact of past performance on current performance, unobserved individual heterogeneity and omitted variable bias.

The authors find that the larger the portion of the human resources (HR) budget allocated to hiring new employees, the poorer organizational performance becomes unless the focal organization has already significantly underperformed. The authors also find that pay concentration among new employees has a positive impact on organizational performance unless the focal organization has already significantly overperformed.

This study extends RBV research by examining how resource allocation patterns affect organizational performance, which has rarely been studied. Moreover, by showing the organizational context's significant effect on the outcome of financial allocation for resource acquisition, this study extends both the RBV research and the pay dispersion research.

This paper aimed to shed light on the relationship between blockchain technology intensity and tax evasion and whether this relationship is moderated by good governance.

Data from a sample of 50 European companies selected from the STOXX 600 index between 2010 and 2019 were used to test the model via panel data and multiple regression. Here, we used the generalized least squares method estimated on panel data. A multivariate regression model was used to analyze the moderating effect of good governance on the association between blockchain technology intensity and tax evasion. For the robustness analyses, we included the comparative study of legal systems. We performed an additional analysis by testing the dynamic dimension of the data set using the generalized method of moments to control for the endogeneity problem.

Expectedly, the results showed a negative relationship between blockchain technology intensity and tax evasion. Furthermore, the findings suggest that the moderating variable negatively affects the relationship between blockchain technology and tax evasion.

To our knowledge, this study supports the existing literature. Firstly, it expands the scientific debate on tax evasion. Secondly, it extends the scope of the agency theory, which is used to explain the phenomena associated with tax evasion. This study is one of the first to examine the moderating effect of good governance on the association between blockchain technology intensity and tax evasion.

This study aims to examine the factors that influence the relationship between sustainability and financial performance (FP) of the European listed companies.

This study analyzed data from 795 companies in 21 European countries by applying linear mixed-effects multilevel regressions, a two steps system generalized method of moments and quantile regression models to uncover the links between sustainability and FP.

The past four decades have witnessed abundant research to determine the relationship between corporate sustainability and FP. Thus, conducting further research in 2023 could be seen as “reinventing the wheel.” Yet, earlier research considered firms as isolated entities with sustainability and FP being dependent only on that firm’s actions. By contrast, with the help of network governance theory, this study shows that a firm’s sustainability and FP depend on an interplay among interorganizational actors, such as institutional qualities, macroeconomic factors and an embrace of sustainability. Here, large firms play an essential role. Three significant findings are drawn. First, sustainability performance has a significant impact on FP in the European context. Second, the institutional quality (IQ) of the rule of law and control of corruption plays a crucial role in enhancing sustainability and FP, and finally the interaction of IQ and economic growth helps to increase companies’ market value (Tobin’s Q). The consistent and empirically robust findings offer key lessons to policymakers and practitioners on the interplay among multiple actors in corporate sustainability and FP.

A synergetic multifaced relationship between governmental institutions and corporations is inevitable for ensuring sustainable development. The degree of intimacy in the relationship, of course, will be determined by the macroeconomic environment.

In this research, this study theoretically and empirically identified that corporate sustainability and FP are not solely dependent on corporate operation. Rather, it is transformed, modified and shaped through an interaction of multiple actors’ trajectories in the macro business environment.

The main objectives of this study are to (1) develop and test a cost contingency learning model that can generalize initially estimated contingency amounts by analyzing back the multiple project changes experienced and (2) uncover the hidden link of the learning networks using a curve-fitting technique for the post-construction evaluation of cost contingency amounts to cover cost risk for future projects.

Based on a total of 1,434 datapoints collected from DBB and DB transportation projects, a post-construction cost contingency learning model was developed using feedforward neural networks (FNNs). The developed model generalizes cost contingencies under two different project delivery methods (i.e. DBB and DB). The learning outputs of generalized contingency amounts were curve-fitted with the post-construction schedule and cost information, specifically aiming at uncovering the hidden link of the FNNs. Two different bridge projects completed under DBB and DB were employed as illustrative examples to demonstrate how the proposed modeling framework could be implemented.

With zero or negative values of change growth experienced, it was concluded that cost contingencies were overallocated at the contract stage. On the other hand, with positive values of change growth experienced, it was evaluated that set cost contingencies were insufficient from the post-construction standpoint. Taken together, this study proposed a tangible post-construction evaluation technique that can produce not only the plausible ranges of cost contingencies but also the exact amounts of contingency under DBB and DB contracts.

As the first of its kind, the proposed modeling framework provides agency engineers and decision-makers with tangible assessments of cost contingency coupled with experienced risks at the post-construction stage. Use of the proposed model will help them evaluate the allocation of appropriate contingency amounts. If an agency allocates a cost contingency benchmarked from similar projects on aspects of the base estimate and experienced risks, a set contingency can be defended more reliably. The main findings of this study contribute to post-construction cost contingency verification, enabling agency engineers and decision-makers to systematically evaluate set cost contingencies during the post-construction assessment stage and achieving further any enhanced level of confidence for future cost contingency plans.

Based on institutional theory, this paper aims to examine whether, and if so which, institutional forces influence the quality of China’s listed financial institutions’ (FIs) sustainability disclosures.

Using univariate statistical and multiple regression analyses, this study quantitatively examines the impacts of coercive pressure from the government and stock exchanges, imitation within subsectors and normative pressure from industry associations and regulators on the quality of China’s listed FIs’ sustainability disclosures. Assessment of the robustness of regression results uses panel random-effects and generalized methods of moments estimation.

Financial sector corporate social responsibility (CSR) disclosure quality did not increase dramatically following issue of the “Guiding Opinions on Establishing a Green Finance System.” However, a convergence in quality is found over time. State ownership concentration and state links to dominant shareholders negatively impact the quality of financial sector sustainability disclosures, whereas stock exchange index listing requirements and industry association reporting guidance have positive influences.

First, data availability limits the sample to listed financial firms with RKS quality scores. Thus, results may not be generalizable to the broader listed and unlisted financial sector. Second, this study only examines the influence of external forces based on institutional theory. However, internal institutional forces, such as corporate governance, may require examination. This study’s results indicate that coercive pressure, as represented by issue of the “Green Finance” policy, has not yet prompted the financial sector to improve reporting quality; however, normative pressure has had significant influence in influencing FIs’ CSR practices, with China’s banks potentially taking a leading role.

The financial sector has a lower direct environmental impact than traditional polluting industries and different operating and reporting structures, features often used to argue for its exclusion in prior studies. However, its indirect environmental impact via lending and investing activities is significant, suggesting evidence on the determinants of sustainability disclosure quality is required. This study uses evidence from China’s financial sector to reduce this gap in the literature.