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This article aims at investigating knowledge transfer and exchange (KTE) between founder firms and corporate foundations (CFs), looking at mechanisms able to enhance CFs’ orientation of effectiveness.

We analyze 50 questionnaires collected through a survey of CFs based in Italy (redemption of 42.7% of the total). We use a number of regressions to verify the change of explained variance moving from a basic model with control variables (enter method) and a model including KTE mechanisms with an impact on orientation to effectiveness (stepwise method).

The analysis produced a model which underlines the influence of knowledge transfer mechanisms in stimulating orientation to effectiveness. The adoption of specific knowledge transfer mechanisms by founder firms can have a significant influence on how CFs manage their effectiveness. Three mechanisms emerge from the study as elements with a positive impact.

The results apply to nonprofit or public bodies, especially if we consider partnerships or organizational networks. The individuated criteria for selecting a positive KTE could drive similar choices of other nonprofit bodies.

The study individuates a set of practices that are potentially able to influence positively the orientation to effectiveness of CFs, and the capacity to perform their activities and respond to social needs more successfully.

This research considers CFs as founder firms’ subsidiaries, with a growing strategic importance. This research reveals how KTE mechanisms can foster the development of orientation to effectiveness if implying interaction, firms’ commitment, autonomy, and alignment with the firms’ strategy and CFs’ purposes.

Total factor productivity growth (TFPG) is not only the technological progress. It is a boarder concept. It may be the increase in the productivity of inputs, economies of scale, capacity utilization, technological progress, etc. In this study, we have tried to estimate TFPG and its components for the manufacturing industries of West Bengal and overall India, for the period 1980–1981 to 2016–2017, using stochastic frontier approach. The main data source of this study is the Annual Survey of Industries (ASI), which is published by the Central Statistical Organization, Government of India. The entire period has been sub-divided into two phases; pre-World Trade Organization (WTO) regime (1980–1981 to 1995–1996) and post-WTO regime (1995–1996 to 2016–2017). This study also tries to make a comparative analysis between the TFPG of manufacturing sector of West Bengal and all India level for the time period mentioned above. For West Bengal, TFPG has decreased for the post-WTO regime and it has increased in post-WTO regime for overall.

Purpose – To discover how SRI develops in the Asian context.

Methodology/approach – Extended search of SRI initiatives analyzed with Scandinavian neo-institutional approach on how ideas travel and Buddhist Economy.

Findings – Chinese SRI-initiatives imitate western peers, but the imitation results in partial isomorphism that will probably have a weak influence on Chinese companies in ESG.

Research limitations/implications – A limitation of the study is a lack of information and transparency on Chinese homepages.

Practical implications – Chinese SRI is in an early state, and will need back-up and push to become active if it will be able to influence Chinese companies.

Social implications – It is important to have a critical stance, and not trusting optimistic statements about SRI in China as a mean to integrate ESG activities in Chinese companies.

Originality/value of chapter – One of the first overviews and critical analysis of SRI in China.

Panel data provide the possibilities of estimating individual treatment effects for multiple individuals. Two issues are considered: (1) differences in the estimated individual treatment effects are due to heterogeneity or a chance mechanism? (2) what is the best way to estimate the average treatment effects? Testing and aggregation methods are suggested. Monte Carlo simulations are also conducted to shed light on these two issues. An empirical analysis on the involvement of underground organization in China’s Peer-to-Peer (P2P) activities through the “anti-gang” campaign is also provided.

This study attempts to analyze energy intensity, capacity utilization (CU), output and productivity growth of aggregate manufacturing sector in India during the period 1980–1981 to 2016–2017. A decadal analysis as well as a comparison between pre- and post-liberalization period of productivity growth is also made. Total factor productivity growth (TFPG) is also adjusted with CU to obtain adjusted TFPG. The trend in energy intensity is also analyzed to answer the question of sustainability. Results shows that TFPG declined in the post-reform period, highlighting the fact that liberalization process has its adverse impact on productivity growth. From the study it is observed that a declining trend in adjusted TFPG in the post-reform period, but the rate of decline is higher. Energy intensity and CU of the Indian manufacturing industries is found to be increasing over the study period. Increasing energy intensity, quite significantly, would increase the level of pollution generated by the manufacturing industries. So, interestingly enough, this may lead to conclude that the growth of the manufacturing industries is not in line with the basic essence of sustainable development.

In this chapter we investigate the finite sample properties of a Hausman test for the spatial error model (SEM) proposed by Pace and LeSage (2008). In particular, we demonstrate that the power of their test could be very low against a natural alternative like the spatial autoregressive (SAR) model.

Pharmaceutical industry is one of the sunrise industries in the Indian manufacturing sector. It has flourished in the recent past. This chapter makes a comparative analysis of the productivity growth of Indian pharmaceutical industry using production function approach and adopting two distinct measures of labour input and also explains whether the growth and productivity is eco-friendly or not. Annual Survey of Industries (ASI) data is considered as data base and the time period 1980–1981 to 2016–2017 is considered which is sub-divided into four periods (1980–1981 to 1989–1990; 1990–1991 to 1999–2000; 2000–2001 to 2009–2010; and rest of the period). The pattern of result for both the measures are more or less in the same direction. A remarkable growth in total factor productivity (TFP) is observed after the initiation of new economic policy for both the method used. So far as the environmental issues are concerned, this industry seems to have been polluting the environment, as per unit use of energy is increasing over time.

Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

Introduction: When people need to take decisions, being economic decisions or otherwise, their decisions tend to rely on information the brain has already processed, and this includes the resources that the person has already invested. This is called sunk cost bias in the behavioural economics literature. On the other hand, mental practices could lead to the mental accounting bias, where people allocate a different value to a fixed amount of money, depending on circumstances.

Purpose: In this chapter, both biases mental accounting and sunk cost are investigated for the tourism industry in Turkey.

Methodology: The topic is researched through scenario-based questions and the Chi-square Automatic Interaction Detector (CHAID) method is applied.

Findings: As a result, it could be reported that people, regardless of gender, fall into sunk cost and mental accounting biases in decisions relating to their vacations. Mental accounting biases can be primarily explained using the scenario questions posed rather than gender, education, and income while sunk cost bias is explained by status, ‘being s university student’ and ‘income level’.

Practical implications: Rapid price changes in the tourism industry can disturb consumers who are mental accounting and sunk cost biased. So, they can change their holiday preferences or be dissatisfied with it and give negative feedback.

In this study, the authors have tried to estimate and examine the relationship between energy intensity (EI), capacity utilisation (CU) and total factor productivity (TFP) of Indian Iron and steel industry for the time period 1980–1981 to 2016–2017. The estimation of TFP and its growth is based on three inputs capital (K), labour (L) and energy (E) using transcendental logarithmic cost function. Besides, capital and labour factor inputs, the estimation of CU also take into account the input, energy. It also considers the variable, price of energy and uses a variable cost (VC) function. This study also takes into account the concept of adjusting total factor productivity growth (TFPG) by CU. From our result, this study observes that CU increased over time. Again, Iron and Steel industry has become energy intensive (increasing EI) over the time period taken up for the study. On the other hand, TFPG has declined in the liberalised era. The preliminary idea that we get from our result is that TFPG is negatively related to the extent of pollution generated by energy intensive nature of the Indian Iron and Steel industry, which is in line with reality. To come out of this situation, we may prescribe the use of green technology and green energy sources for this industry.