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The objective of this chapter is to study the symmetric and asymmetric impact of macroeconomic variables on the Indian stock prices (SPs) of the Bombay Stock Exchange index. This chapter further investigates whether the asymmetric impact of macroeconomic variables on SP is due to the impact of any tail events like the global financial recession. An autoregressive distribution lag and non-autoregressive distribution lag approach is used for the full sample covering the period from January 2000 to June 2019 and later this sample is further subdivided into before and after the crisis period to study the variations in result. The findings show that macroeconomic variables and SP have a symmetric relation in the long run whereas an asymmetric relationship in the short run when the whole sample is analyzed. However when data are segregated into “before and after” crisis period this relationship turns to be asymmetric in long run too, meaning that in the long run, the negative and positive changes in a macroeconomic variable do not affect SPs similarly.

We examine whether the hedging effectiveness of crude oil futures is affected by asymmetry in the return distribution by applying tail-specific metrics to compare the hedging effectiveness of both short and long hedgers. The hedging effectiveness metrics we use are based on lower partial moments (LPM), value at risk (VaR) and conditional value at risk (CVaR). Comparisons are applied to a number of hedging strategies including ordinary least square (OLS), and both symmetric and asymmetric GARCH models. We find that OLS provides consistently better performance across different measures of hedging effectiveness as compared with GARCH models, irrespective of the characteristics of the underlying distribution.

The introductory chapter includes how to design-in good practices in theory, data collection procedures, analysis, and interpretations to avoid these bad practices. Given that bad practices in research are ingrained in the career training of scholars in sub-disciplines of business/management (e.g., through reading articles exhibiting bad practices usually without discussions of the severe weaknesses in these studies and by research courses stressing the use of regression analysis and structural equation modeling), this editorial is likely to have little impact. However, scholars and executives supporting good practices should not lose hope. The relevant literature includes a few brilliant contributions that can serve as beacons for eliminating the current pervasive bad practices and for performing highly competent research.

This chapter elaborates on the usefulness of embracing complexity theory, modeling outcomes rather than directionality, and modeling complex rather than simple outcomes in strategic management. Complexity theory includes the tenet that most antecedent conditions are neither sufficient nor necessary for the occurrence of a specific outcome. Identifying a firm by individual antecedents (i.e., noninnovative vs. highly innovative, small vs. large size in sales or number of employees, or serving local vs. international markets) provides shallow information in modeling specific outcomes (e.g., high sales growth or high profitability) – even if directional analyses (e.g., regression analysis, including structural equation modeling) indicate that the independent (main) effects of the individual antecedents relate to outcomes directionally – because firm (case) anomalies almost always occur to main effects. Examples: a number of highly innovative firms have low sales while others have high sales and a number of noninnovative firms have low sales while others have high sales. Breaking-away from the current dominant logic of directionality testing – null hypothesis significance testing (NHST) – to embrace somewhat precise outcome testing (SPOT) is necessary for extracting highly useful information about the causes of anomalies – associations opposite to expected and “statistically significant” main effects. The study of anomalies extends to identifying the occurrences of four-corner strategy outcomes: firms doing well in favorable circumstances, firms doing badly in favorable circumstances, firms doing well in unfavorable circumstances, and firms doing badly in unfavorable circumstances. Models of four-corner strategy outcomes advance strategic management beyond the current dominant logic of directional modeling of single outcomes.

The aim of this chapter is to review and illustrate a step-by-step guideline in conducting fuzzy-set Qualitative Comparative Analysis (fsQCA) in tourism and hospitality studies. As an emerging method, fsQCA is simultaneously quantitative and qualitative in nature which makes it an appropriate method for social science disciplines including tourism and hospitality area because of complex nature of relationships between multiple variables where theories and models are underdeveloped. Unlike conventional statistical techniques, fsQCA is an asymmetrical analysis technique that provides a holistic view and interrelationships among several conditions using Boolean algebra. The fsQCA analyses produce comprehensive assessment by revealing causal combinations of antecedents to predict an outcome; and identify sufficient configurations (i.e., causal combinations and recipes) and necessary condition/s. By utilizing this method, researchers would be able to produce complex, comprehensive, and robust results.

Organizational studies of time tend to be done by academic researchers rather than practitioners. This chapter builds on academic research to provide a practitioner perspective by reviewing time situated in theory and constructing two phenotypes: timescapes of business and social time. These timescapes are defined by six dimensions, each with a social and business time parameter. Organizational business and social timescapes have different functions and applications. Timescapes, with their concomitant dimensions and sets of parameters, are used differently by senior managers, middle managers, and entry-level managers. Three multi-level approaches (self, dyadic, and social relationships), composition theory, and compilation theory confirm these three managerial timescape usages. After a review of the theoretical bases of the timescape constructs and a brief discussion of the grounded, anthropological, research methodology used in the study, this chapter applies timescape theory and models to an extended time case study of the Procter & Gamble Company that frames the company's timescape understanding and use from a practitioner's view.

This chapter proposes moving beyond relying on the dominant logic of multiple regression analysis (MRA) toward thinking and using algorithms in advancing and testing theory in accounting, consumer research, finance, management, and marketing. The chapter includes an example of testing an MRA model for fit and predictive validity. The same data used for the MRA is used to conduct a fuzzy-set qualitative comparative analysis (fsQCA). The chapter reviews a number of insights by prominent scholars including Gerd Gigerenzer’s treatise that “Scientists’ tools are not neutral.” Tools impact thinking and theory crafting as well theory testing. The discussion may be helpful for early career scholars unfamiliar with David C. McClelland’s brilliance in data analysis and in introducing business research scholars to fsQCA as an alternative tool for theory development and data analysis.

This primer defines and describes conscious and nonconscious perception and assessment processes by tourists. The primer links the field of tourism perception studies to the literature of experimental social psychology. The primer describes the important roles that metaphors play in connecting conscious and nonconscious thinking and how both tourism brand managers and tourists use metaphors to use stories to enable enactments and favorable outcomes of archetypal motivations. The primer introduces formal implementable models of the major tenet in Urry’s tourist gaze – visitors’ home culture automatically and mostly nonconsciously profoundly influences their perceptions, assessments, and interpretations of what they see when traveling and visiting away destinations. Model implementation includes applying Boolean algebra-based asymmetric tests instead of symmetric matrix algebra-based statistical tests – the asymmetric tests examine for the consistency of high scores in perceiving, assessing, and behaviors of complex configurations of antecedent conditions. A detailed empirical example of asymmetric testing includes consistent high scores for Americans, Brits, Canadians, and Germans for not shopping for gifts to take home during their visits to Australia. This primer also introduces the concept of the tourist meta-gaze – seeing and assessing outside the automatically activated culturally based tourist gaze.