Determinants of the allocative,cost and scope efficiencies of Indian banks

ABSTRACT

This study aims to investigate the impact of competition on determinants of allocative, scope and cost efficiencies of Indian scheduled commercial banks (SCBs). Specifically, the study, analyzes the impact of the second round of licensing on the efficiency of Indian SCBs. This is the first paper to measure scope efficiency of Indian banks and analyze its determinants. A two-stage analysis is performed on a balanced panel dataset of Indian SCBs for the period 1999–2016. In the first stage, the allocative, cost and scope efficiencies for each bank are estimated following the data envelopment analysis approach. In the second stage, internal determinants of the stated efficiency measures are estimated following the system of the generalized method of moments approach. The findings suggest that competition has not resulted in enhancing the efficiency of Indian SCBs. Among factors that can influence efficiency, it is seen that size does matter. Larger banks can enhance the efficiency of SCBs. It is also seen that having more foreign banks improves the overall efficiency of SCBs. However, before embarking on further rounds of licensing, the study posits that market-driven correction to succeed, it is imperative to address sunspots in the form of investor or borrower repression.     

Employing structural topic modelling to explore perceived service quality attributes in Airbnb accommodation

This study employs the structural topic model to extract service quality attributes from 242,020 Airbnb reviews in Malaysia. 22 service related topics were extracted from the corpus and four topics have not appeared in previous Airbnb studies. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated in this study by comparing its service quality attributes with the results of the topic modelling, which indicates that this MSQ can cover general Airbnb service quality attributes. This study also examines the different preferences of Malaysian and international Airbnb users and the changing patterns of the top six service quality attributes during a five-year period. The findings reveal that Malaysian Airbnb users care more about the appearance and location of the property, and international Airbnb users pay more attention to whether the property can accommodate a group of people. In addition, communication with the host is found to play an increasingly important role in Airbnb users’ lodging experiences.     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

On the measurement of investment types: Heterogeneity in corporate tax elasticities

This paper contributes to debates about the appropriate characterisation of heterogeneous investment types and to what extent different investment motives affect the responsiveness to corporate taxation. In particular, we employ and refine a methodology to better evaluate the tax elasticity of investment types. Using a combination of both firm‐specific information and sector‐specific information from input–output tables, we discuss how to classify investment as non‐related, horizontal, vertical and complex types. First, we point out to what extent the resulting classification depends on assumptions made by the researcher. Second, we employ an ample set of classifications and find that non‐related investment reacts stronger to corporate taxation, whereas horizontal investment is less responsive, though, significant negative tax semi‐elasticities turn out for the subset of manufacturing industries. To address inherent characteristics of vertical and complex investment, we extend the methodology and find that, by and large, stronger business motives reduce the tax responsiveness of investment to a larger extent. Given the current debates about substantial corporate tax reforms, it is all the more important to recognise that corporate tax effects can vary fundamentally between countries, driven by country‐specific differences in their composition of industries and investment types.     

Are firms ready to use big data analytics to create value? The role of structural and psychological readiness

In this study, we leverage Information Technology (IT) readiness literature and resource-based view (RBV) to investigate the impact of firm structural and psychological readiness on firm value creation, as mediated by big data analytics usage. The proposed research model is empirically validated using survey data from 179 senior IT managers. The findings demonstrate the importance of both structural (i.e. IT infrastructure capability, tools functionality, employee analytical capability, and bigness of data) and psychological readiness (i.e. IT proactive climate) in enhancing firm value creation through big data analytics usage. These results provide interesting theoretical and practical insights.     

Doing more and doing better are two different entities: Different patterns of family control and environmental performance

Family firms bear two types of agency costs, including type I and type II agency problems, in corporate environmental practices: (1) Outside executives at family firms hesitate to engage in environmental strategies, which can lead to drops in profits; (2) Controlling families employ opportunistically environmental management to achieve their interests. We argue that a primary cause for the agency problems lies on ineffective internal corporate governance at family firms, which can cause loss of managerial (or power) balance between outside executives and family executives. Our findings show that family firms with ownership and strategic control (FSC), which family executives and outside executives monitor and constrain each other, can achieve the highest environmental performance. Moreover, external controls, including product market competition and provincial environmental regulations, substitute effective internal control of FSC. The environmental performance premium of FSC is more prevalent when the production market competition is lower. Family firms with ownership, operational, and strategic control (FOSC) can achieve higher environmental performance within a province with more stringent environmental regulations.     

The development trinity: How regional integration impacts growth,inequality and poverty

Developing countries today have become more active participants in regional trade agreements. This raises questions about how the benefits of integration are distributed, and the extent to which lower‐income countries are able to capture development gains. Historically, such impacts have been difficult to identify with precision. This paper seeks to address this gap by empirically analysing the impact of regional integration on development, particularly the effects on growth and welfare. Using both bilateral and regional integration measures, we show that the ability to capture gains from integration varies across developing country regional groups, with developing Asia benefiting on par with developed countries. The findings in the paper indicate that trade and trade policy play an important role in reducing inequality and poverty in developing countries. It also shows that regionalism can function as a channel to make multilateralism a more adept way of addressing national challenges.     

Defining Supply Chain Management: In the Past,Present, and Future

The article titled “Defining Supply Chain Management” published in 2001 in the Journal of Business Logistics has been cited over 4,900 times in the last 17 years. In this paper, we first provide a historical review of how the article originated and the contributions the article made to both the theory and practice of supply chain management (SCM). Next, we highlight the key market and technological changes that have emerged in SCM followed by how the theory proposed in the 2001 article can still be relevant to support SCM research and practice going forward. We also propose ways of configuring a supply chain and partnering across companies to serve customers in an optimal way. We conclude with a call for research on developing new frameworks to better describe, explain, predict, and shed light on the evolving nature of SCM.     

M5 competition uncertainty: Overdispersion,distributional forecasting,GAMLSS, and beyond

The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data face strong overdispersion and sporadic demand, especially zero demand. We discuss modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fail to address the data characteristics, due to the considered objective functions. Distributional forecasting provides a suitable modeling approach to overcome those problems. The GAMLSS framework allows for flexible probabilistic forecasting using low-dimensional distributions. We illustrate how the GAMLSS approach can be applied to M5 competition data by modeling the location and scale parameters of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawbacks, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.     

关于铁路单线隧道接触网弓形腕臂预配平台的相关研究

为降低铁路单线隧道接触网弓形腕臂预配施工难度,论文研究了一种接触网弓形腕臂预配平台,结合单线隧道内弓形腕臂数据精测技术及计算软件,精确测量预配,保证弓形腕臂的安装精度和质量,使弓形腕臂在现场施工中一次成优。论文结合实际,就某铁路单线隧道接触网弓形腕臂预配平台作具体分析。