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Item type:Publication, A novel two-phase approach to computing a regional social progress index(Springer, 2020-01-01)In recent decades, concerns have emerged regarding the fact that standard macroeconomic statistics (such as gross domestic product) do not provide a sufficiently detailed and accurate picture of societal progress and well-being and of people’s true quality of life. This has further translated into concerns regarding the design of related public policies and whether these actually have the intended impact in practice. One of the first steps in bridging the gap between well-being metrics and policy intervention is the development of improved well-being measures. The calculation of a regional Social Progress Index (SPI) has been on the policymakers’ agenda for quite some time, as it is used to assist in the proposal of strategies that would create the conditions for all individuals in a society to reach their full potential, enhancing and sustaining the quality of their lives, while reducing regional inequalities. In this manuscript, we show a novel way to calculate a regional SPI under a two-phase approach. In the first phase, we aggregate the item-level information into subfactor-level indices and the subfactor-level indices into a factor-level index using an objective general index (OGI); in the second phase, we use the factor-level indices to obtain the regional SPI through a pure data envelopment analysis (DEA) approach. We further apply the method developed to analyse a single period of social progress in Peru. The manuscript is a contribution to the practical measurement of social progress. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A stratified bootstrapping approach to assessing the success of TQM implementation in Peruvian companies(Taylor & Francis, 2020-09-08)Total Quality Management represents a management approach to long-term success used by organisations worldwide to improve their products and processes and achieve customer satisfaction, being perceived as a source of competitive advantage. A look at the existent literature points towards a lack of studies dedicated to examining TQM implementation in developing countries, especially in Latin America and particularly in Peru. The current industrial needs in Peru, however, require further research on this topic because a larger number of organisations choose nowadays to obtain quality certifications to improve their products. In this context, the objective of the present paper is to provide a snapshot of the current state of TQM implementation in Peru, aiming to identify which key quality factors are the most and least developed for successful TQM implementation. To this aim, the present paper uses a bootstrapping approach, within the framework of a nine-factor model of TQM in business. The study is performed on a sample of 4,668 Peruvian companies, across 52 industry sectors. Findings reveal that the most developed key quality factors in the companies surveyed are: Top Management, Quality Planning, Quality Audit and Assessment, Education and Training, and Process Control and Improvement. Implications for practice are provided. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Stochastic scale elasticity(Springer Science+Business Media, 2021-12-11)To analyze the performance of firms (decision-making units, DMUs) in the literature, several economic concepts such as economies of scale (returns to scale, RTS), economies of scope, marginal rates of technical substitutions, etc., have been used. Banker et al. (2004) studied RTS in different DEA models. In this chapter, however, we concentrate on determining and measuring RTS. Scale elasticity (SE) is a quantitative measure of the RTS characterization of the firms operating on the production frontier, which is used to determine improvement or deterioration in their productivities by resizing their scales of operation. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stochastic data envelopment analysis(Springer Science+Business Media, 2021-12-11)In traditional DEA models, the technologies are developed using the premise that inputs and outputs are precisely measured and are, therefore, deterministic. However, in practical situations, the general production processes are often stochastic. The stochastic production relationship in a DEA setting may arise in different situations, for example, when stochastic variations in inputs and outputs affect the production frontier; when inputs and outputs are faced with stochastic prices while measuring allocative efficiency; when the slacks obtained from the DEA efficiency frontier are analyzed in terms of their statistical distribution; when an economic method is applied to estimate the stochastic production frontier; etc. (Sengupta, 1990). Over the last two decades, many researchers have proposed DEA-based models with stochastic data. Sengupta (2000) applied a stochastic DEA model using mathematical expectations for random inputs and outputs. Banker (1993) added statistical elements to DEA and developed an approach aimed at influencing statistical noise in inference. Many studies (e.g., Cooper et al., 1996, 1998; Land et al., 1993; Olesen & Petersen, 1995, 2016) have introduced chance-constrained programming in DEA to accommodate random changes in data. Banker (1986) proposed a related semi-parametric stochastic frontier analysis (SFA) based on a minimization of the sum of the absolute value of all composed error terms. For parametric and semi-parametric models, see Banker (1989, 1996), Banker and Chang (1995), Banker et al. (1994, 2015), and Banker and Maindiratta (1992). Additional approaches and applications can be found in Charles and Cornillier (2017), Charles and Udhayakumar (2012), Charles et al. (2018), Grosskopf (1996), Horrace and Schmidt (1996), Simar (1996), Simar and Wilson (1998), and Udhayakumar et al. (2011), among others. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Expert decision-making: a Markovian approach to studying the agency problem(Elsevier, 2021-12-01)In this paper, we study the agency problem in an organisation within a Markovian framework. More specifically, the paper presents the case of a principal imposing an incentive-control structure upon an agent to force him to follow the principal's interests for which he was hired, against the tendency of the agent to follow his own interests. Findings point toward the principal's difficulty in controlling the behaviour of the agent through incentives and monitoring; instead, best results are obtained when hiring agents who care for their reputation and refrain from unprofessional behaviours. The implication is that if we consider that it might be difficult to identify this characteristic at the time of the agent's hiring, the best criterion will be to look for low levels of greed in the agent. This conclusion goes in some way against current practices of looking for aggressive agents for the generation of higher profits. Nevertheless, it should be noted that these potential benefits might actually fade away if the agent follows his own interests, instead of the principal's. Another interesting result points to the restricted, although necessary, role of monitoring to control the agent's behaviour, a result that goes against current research interests on measures of corporate governance. The paper is a contribution to expert decision-making. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Benchmarking(Springer Science+Business Media, 2021-12-11)Benchmarking is an efficiency performance measurement procedure that allows firms to compare their performance to the top competitors. In this chapter, benchmarking (as an efficiency performance measurement tool using a specific indicator) and key performance indicators are first briefly introduced. Then, we introduce efficiency and productivity and the ways for measuring the different types of efficiencies. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An introduction to data envelopment analysis(Springer Science+Business Media, 2021-12-11)Following the seminal work of Farrell (1957), Charnes et al. (1978) introduced DEA as a deterministic and nonparametric efficiency evaluation tool. DEA is a linear programming-based technique that has been widely accepted as a competing methodology to evaluate the relative efficiency of entities or decision-making units, DMUs (Charles et al., 2016, 2018; Tsolas et al., 2020). DEA is a data-oriented technique (Zhu, 2020) that is used to construct an empirical production frontier to measure efficiency. Note that the original DEA program of Charnes et al. (1978) is based on the CRS specification of technology and is used to measure the technical and scale efficiency of DMUs. However, Banker et al. (1984) extended this program to the case of VRS to estimate purely technical efficiency. Over the past three decades, DEA has been widely used to evaluate the relative efficiency of production firms, the nature of the returns-to-scale, and the productivity changes. The DEA literature has seen a wide variety of applications across a plethora of domains, having become a powerful management science tool (Charles et al., 2018). In this chapter, we briefly review the fundamental concepts in DEA, along with the basic technologies and programs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A DEA and random forest regression approach to studying bank efficiency and corporate governance(Palgrave Macmillan, 2021-05-10)We employ Data Envelopment Analysis to estimate the new technical, new cost, and new profit efficiency of Indian banks over the period 2008–2018. Then, we use Random Forest Regression to examine the impact of corporate governance (Board Size, Board Independence, Duality, Gender Diversity, and Board Meetings), bank characteristics (Return on Assets, Size, and Equity to Total Assets), and other characteristics (Ownership and Years) on bank efficiency. Among others, we found that board characteristics play a significant role particularly in new profit efficiency; therefore, policymakers and regulators should consider Board Size, Board Independence, Board Meetings, and Duality while framing guidelines for enhancing bank new profit efficiency. We also found that Board Independence plays a vital role in bank new cost efficiency, while Gender Diversity contributes to both new technical and new cost efficiency. This study makes methodological contributions by employing Machine Learning based Random Forest Regression in tandem with Data Envelopment Analysis under a two-phase model to examine corporate governance and bank efficiency, which is a pioneering attempt. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Bayesian resampling approach to estimate the difference in effect sizes in consumer social responses to CSR initiatives versus corporate abilities(John Wiley and Sons Ltd, 2021-11-01)We expand previous analyses and advance our understanding of the difference in the impact on consumer purchasing behaviour between pursuing corporate social responsibility (CSR) initiatives and improving corporate abilities. To this aim, a Bayesian bootstrapping simulation is applied to selected consumer samples for 123 homogeneous choice‐based conjoint studies. We develop a simulation‐based approach to estimate the empirical distributions of effect sizes under two Bayesian bootstrap resampling schemes. This approach permits us to evaluate the results for two predefined classifications: Foote‐Cone‐Belding (FCB) and gender. The results indicate that females exhibit higher concern for CSR initiatives. Furthermore, we found that managers can exploit the classification of their product in the FCB grid and obtain more efficient consumer responses by implementing strategies focused on certain attributes. This is the first application of the FCB grid to identify differences in consumer responses across different products with different levels of rational consideration and involvement.1