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This chapter explores other theoretical explanations to the commonly observed phenomenon of negatively skewed performance outcomes and inverse risk-return relationships in empirical firm data. The analysis conducted in many prior studies have implicated direct causal dependencies between performance and risk, or vice versa, with the possibility of simultaneous two-way relationships that are harder to discern. It is also shown how spurious artifacts deriving from the arithmetic links between mean and variance associate left-skewed distributions with negative mean variance correlations. However, the heterogeneous display of response capabilities among firms that compete in the same industry contexts may provide an alternative explanation for the observed performance characteristics. This is expressed as strategic responsiveness where performance outcomes with high negative skewness and excess kurtosis derive from heterogeneous adaptive processes among firms as they respond to a dynamic environment with different degrees of success. We test these results in different simulated competitive contexts disrupted by major unexpected events and find robust results across different environmental scenarios. The analysis looks at two different response processes, one modeled as conventional adaptive planning following an annual budget cycle, and another modeled as interactive updating where executives have frequent informative budget discussions with operating managers in the firm. The computational simulations show that interactive updating generates outcomes with higher returns and lower performance risk for moderate learning levels and restructuring costs. However, the resulting performance distributions are not as left-skewed as those observed in the empirical data that show higher resemblance to the adaptive planning outcomes.

This chapter investigates the effect of financial performance and corporate governance on market performance, using evidence from the companies listed on the IDX30 Index of the Indonesia Stock Exchange (IDX) from 2015 to 2018. The authors use six main independent variables and one dependent variable, controlled by using control variables in the regression analysis. Ordinary least square (OLS) regression methods are used to model the relationship between the dependent variable and the independent variables. The results show that the current ratio (CR) and Board Size (BS) have a significant negative effect on stock return (SR). In contrast, the quick ratio (QR) and debt to equity ratio (DER) have a significant positive impact on SR. Both the debt to asset ratio (DAR) and Independent Board of Commissioners (BOC) have an insignificant effect on SR. This evidence suggests that the CR, QR, DER, and BS are essential factors affecting SR.

This chapter takes a closer look at outliers and extreme outliers identified in the data derived from a complete case treatment of missing values in the European and North American datasets and consistently observe significant negatively skewed distributions with high excess kurtosis across all industries. We then plot the density functions for return on assets (ROA) across different industries in the two datasets and find pervasive observations in the tails where negative returns and outlying observations constitute a frequent and recurring phenomenon. We analyze the persistency of outliers and find noticeable percentages of outlying over- and underperformers hovering around 3–6% dependent on industry context. We further analyze potential size effects associated with extreme negative skewness but do not find that (even sizeable) elimination of extreme values reduce the phenomenon. Finally, we analyze the percentage of firm observations that must be eliminated to reach at distributions that fulfill the characteristics of a normal distribution and reach at a substantial percentage of around 5–10% dependent on industry. To conclude, the often-assumed normally distributed performance outcomes are typically wrong and discards the substantial number of outliers in the samples.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

In recent years, the airline industry has been growing and transforming rapidly in the Asia-Pacific area. This study analyzes and benchmarks the comparative operational efficiencies of the major Asian air carriers. Data envelopment analysis model and disaggregate output efficiency measures are used to evaluate the operational efficiencies of 31 Asian airlines from 2015 to 2019. The findings suggest that nonflag carriers, low-cost carriers, and high-income regions' carriers have significantly higher levels of efficiency than flag carriers, full-service carriers, and low-income regions' carriers in overall, revenue, and passenger traffic efficiencies. The efficiencies between alliance carriers and nonalliance carriers along with those of ASEAN and non-ASEAN carriers are not significantly different.

Digitalization is considered an emerging phenomenon that enables businesses to develop new markets and business models. It helps firms expand the scope of their activities and enables organizations to become more efficient. It is now a reality for firms and contributes to their value creation. Thus, the aim of this paper is to investigate the relationship between digitalization and firm performance through the mediating role of business model innovation (BMI) and to do so, a self-administered questionnaire with a sample of 70 Tunisian firms was conducted for this purpose. The main result of the analysis, confirms that digitalization is positively related to business performance, with BMI mediating this relationship.

This book chapter aimed to examine the relationships between green supply chain practices and environmental performance in Malaysian Small and Medium Enterprises (SMEs). The practices of green supply chain in achieving environmental performance have been one priority concerns in Malaysia. However, green supply chain practices adoption remains emergence.

This manuscript adopted the multiple regression analysis in investigating the green supply chain (GSC) practices and environmental performance's variables. The self-administered surveys were randomly disseminated to Malaysian SMEs and 59 responses were returned. The result theoretically ascertained the positive relationship between GSC practices and environmental performance measures.

The findings are in aligned with the Resource-Based View (RBV) theory that conceptualized the GSC practices and strategies to sustain the environmental performance within the SMEs. In a nutshell, these findings would serve as the research implications and recommendations to the scholars, industrial practitioners, and policymakers who are interested in these GSC practices and environmental performance. This would further serve as a guideline for companies that tend to implement these GSC practices for improving its environmental performance.

The research revealed that ‘eco-design and packaging’ and ‘reverse logistics’ are significant to environmental performance, but both ‘green procurement’ and ‘investment recovery’ are not significant to environmental performance. The literature gaps exhibited for this manuscript; hence, future studies should be carried out on supply chain practices and environmental performance since there is no prescriptive method in sustaining environmental performance that matches all conditions in Malaysian SMEs.

Prior work has suggested that individual decision-making is influenced by the emotions and cognition of the decision-maker. Within the firm context, the chief executive officer (CEO) is required to make many decisions that will meaningfully impact the firm. However, little is known about how CEO emotions and cognition influence not only firm decision-making but also the performance consequences of those decisions. Within this chapter, the authors conceptually explore (1) how CEO affect intensity moderates the relationship between performance below aspirations and risk-taking; and (2) how CEO cognitive complexity determines the directional effect of the risk-performance relationship.

The need to design buildings with due consideration for bioclimatic and passive design is central to promoting sustainability in the built environment from an energy perspective. Indeed, the energy and atmosphere considerations in building design, construction and operation have received the highest consideration in green building frameworks such as LEED and BREEAM to promote SDG 9: Industry, Innovation and Infrastructure and SDG 11: Sustainable Cities and Communities and contributing directly to support SDG 13: Climate Action. The research literature is rich of findings on the efficacy of passive measures in different climate contexts, but given that these measures are highly dependent on the prevailing weather conditions, which is constantly in evolution, disturbed by the climate change phenomenon, there is pressing need to be able to accurately predict such changes in the short (to the minute) and medium (to the hour and day) terms, where AI algorithms can be effectively applied. The dynamics of the weather patterns over seasons, but more crucially over a given season means that optimum response of building envelope elements, specifically through the passive elements, can be reaped if these passive measures can be adapted according to the ambient weather conditions. The use of representative mechatronics systems to intelligently control certain passive measures is presented, together with the potential use of artificial intelligence (AI) algorithms to capture the complex building physics involved to predict the expected effect of weather conditions on the indoor environmental conditions.