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To overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).

The authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.

Using the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.

The authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

Researchers often make use of SIC (Standard Industrial Classification) codes when gathering and analysing data about the activities of companies. The use of these codes is, however, fraught with potential difficulties, errors easily creeping in and consequently distorting results. This paper outlines the major SIC code systems in use on both sides of the Atlantic – which, despite efforts to standardise them (and thus to make them worthy of their name), still present levels of inconsistency and unreliability both internally and comparatively – and discusses various problems associated with using the codes as data sources.

All firms operating in the global economy are exposed to a multitude of risks including financial crisis, cyberattack, social instability, governance failure, extreme weather events, etc. As a consequence, international organizations assume many (new and evolving) exposures that must be addressed, where some firms are able to adjust and thrive against these adverse odds, whereas many others fail. It appears like some (a few) firms are able to repeatedly outperform the market, where a great many of them struggle, and quite a few register negative returns every year. As a consequence, the authors typically observe leptokurtic negatively skewed distributions of financial returns with extreme negative tails of poor performing firms, where the performance data fall way beyond the requirements of a normal distribution. The authors investigate this phenomenon based on a comprehensive dataset of European firms retrieved from Compustat Global for the 25-year period 1995–2019. The analysis shows that there is indeed a consistent pattern of many underperforming firms across different industry classifications and time intervals and a few outperformers. This provides evidence of a regularly observed phenomenon that often is overlooked in mainstream management studies. The results have implications for academic research that often relies on assumptions of data normality in statistical analysis and for corporate management that has to deal with a risk-prone business environment.

Self-inflicted crises (SIC)– either intentionally induced or at least carelessly accepted – can tremendously damage a corporation’s reputation and legitimacy in the eyes of the stakeholders. While academia usually advices companies to accept full responsibility, practice shows that by far not all companies rely on such a responsible strategy. In practice, corporations choose various response strategies ranging from apologies, over diminishing approaches to full denials. By investigating a large data set embracing several countries and industries covering 696 cases of SIC, the authors analyze how corporations respond to such events and compare these response strategies across countries and types of crises.

This book chapter follows a domain-spanning approach by combining corporate social responsibility (CSR), crisis management, and stakeholder management to investigate how companies aim at solving crises. Drawing on attribution theory and situational crisis communication theory, the results reveal that corporations often do not follow the prevailing recommendation to take responsibility. The authors find that in the majority of cases, internationally active corporations try to deny or diminish their responsibility for the crises. Hence, the findings suggest that the concept of CSR is not working in the case of SIC since not only the existence of such corporate behavior but also the use of denial and diminish strategies contradicts the idea of corporate responsibility. Moreover, the authors shed light on possible differences and preferences toward a specific response strategy between countries and between different types of crises.

The authors contribute to the growing literature in the field of crisis management and crisis response strategies by investigating a large data set embracing several countries and industries. In this regard, the study differs from previous qualitative studies and experimental research as it is based on a large cross-country and cross-company set of secondary data. Thereby, the study allows drawing conclusions for a wide range of corporations and countries, hence increasing its general applicability.

The purpose of this paper is to extend understanding of business relatedness, a concept that is central to diversification issues. These questions are put forward: What characterizes existing types of measurements of business relatedness? What are the weaknesses of these types? What would be the features of a model for measurement of business relatedness?

As relatedness concerns specific business attributes, common attributes used in measurements are presented. A review of previous studies on types of measurements of business relatedness (codes or indices, researcher assessments and managerial perceptions) is followed by a discussion on correlations between perceptual and objective measurements.

The review shows that application of standard industrial classification codes/indices and researcher assessments suffer from weak content validity of the measurements, and underestimation of the multidimensionality of the construct. Use of managerial perceptions needs to address the uncertainty inherent in managerial self‐assessments; previous research has found a major divergence between perceptual and objective measurements.

A model is proposed for the measurement of business relatedness using perceptual data. It is stressed that the context of the comparisons (i.e. reason for comparison and units to be compared) has a major influence on the outcomes. Business attributes to be subjectively compared by managers are those that have been singled out as important for financial performance.

The paper is unique as it represents a continuation of the most recent research on measurement of business relatedness, i.e. those measurements that are based on managerial perceptions. Another key value is that the review of research leads to a measurement model.

This paper aims to propose a novel approach to constructing an economic taxonomy that demonstrates the complex relationships between firms, which are not fully revealed by traditional industry classification systems such as the NAICS or ICB.

Based on narrative economic theory, data from CNBC news reports between 01/01/2019 and 03/27/2019 regarding four selected firms, namely, Walmart, Amazon, Netflix and Boeing, were analyzed and coded as the basis to guide the construction of a firm-to-firm relationship taxonomy.

The relationships between firms are more complex than the simple relationships defined by the traditional classification systems with yes or no in terms of production process (NAICS) or major profit resource (ICB). Based on the sample firms, the authors proposed a four-layer hierarchical taxonomy framework that quantitatively reveals the inherent contradictory relationships between firms, which the authors defined as competition vs consistency. The proposed taxonomy framework is sufficiently flexible to accommodate complex relationships between firms, and it is also adaptable to new information. Under both the competition and consistency categories in the taxonomy model, more detailed subcategories are further coded into two more layers quantitatively to represent the firms' nuanced relationships.

This study provides a novel atheoretical approach to reveal complex firm relationships utilizing narrative text data gathered from news media. The framework of the firm relationship taxonomy constructed in this study provides an alternative and supplementary approach to the classical industry classification systems that can quantitatively specify comprehensive and dynamic connections between firms.

The theory of managerial discretion and the direct insights it provides in the understanding of the varying impact strategic and operational actions have on organizational change and business fortunes is an area of research potential underexplored in the UK. This study aims to establish whether the measurement of managerial discretion is constant between the two similar societal corporate frameworks of the UK and the USA listed markets.

The extant managerial discretion ranking model, established in the USA, is empirically assessed for its validity and effectiveness across a sample of high- and low-discretion companies from the FTSE 350.

Using accounting measures, a clear and significant difference is established between UK high and low managerial discretion entities. The results prove to be significant in enabling the differential comparative analysis of the institutional characteristics of corporates.

To the best of the authors’ knowledge, no study of this nature has been conducted previously in the UK context. While the original model developed in the USA is now several decades old, the UK results reflect similar industry rankings as found originally in the USA, subject to some differences considered to be a result of the changing nature of global business since the 1990s. This study opens a new seam of novel research, which has the potential to uncover, at a granular level, the differential mores and character of management ethics, styles and practices in such issues as organizational change, corporate culture, governance and social responsibility.

We examine the entry mode choice for Canadian firms entering the United States (U.S.). Entry options are categorized into three competing modes: mergers and acquisitions; joint ventures; and subsidiaries. The unit of analysis is the foreign direct investment (FDI) transaction between a Canadian firm and an American counterpart during the period from January 1980 through December 1989. Using canonical discriminant analysis, we develop a set of variables that characterize the entry mode choice. We find transaction specific information available to senior management provides important information regarding the entry mode choice. The importance of mergers and acquisitions is particularly apparent over this sample period. Empirical evidence strongly supports our measures of resource commitment, dissemination risk, and liquidity position as important measures determining mode of entry. Joint ventures display meaningful differences related to these measures in contrast to both mergers and acquisitions, and subsidiary investments.

Acquisition is one of key corporate strategic decisions for firms’ growth and competitive advantage. Firms: (1) diversify through acquisition to balance cash flows and spread the business risks; and (2) eliminate their competitors through acquisition by acquiring new technology, new operating capabilities, process innovations, specialized managerial expertise, and market position. Thus, firms acquire either unrelated or related business based on their strategic motivations, such as diversifying their business lines or improving market power in the same business line. These different motivations may be related to their assessment of market growth, firms’ competitive position, and top management’s compensation. Thus, it is hypothesized that firms’ acquisition decisions may be related to their industry growth potential, post-acquisition firm growth, market share change, and CEO’s compensation composition between cash and equity. In addition, for the two alternative acquisition accounting methods allowed until recently, a test is made if the type of acquisition is related to the choice of accounting methods. This study classifies firms’ acquisitions as related or unrelated, based on the standard industrial classification (SIC) codes for both acquiring and target firms. The empirical tests are, first, based on all the acquisition cases regardless of the firm membership, and then, deal with the firms acquiring only related businesses or unrelated businesses exclusively.

The type of acquisitions was more likely related to industry growth opportunities, indicating that the unrelated acquisition cases are more likely to be followed by higher industry growth rate than the related acquisition cases. While there were a substantially larger number of acquisition cases using the purchase method, the related acquisition cases used the pooling-of-interest method more frequently than in the unrelated acquisition cases. The firm-level analysis shows that the type of acquisition decisions was still related to acquiring firms’ industry growth rate. However, the post-acquisition performance measures, using firm’s growth and change in market share, could support prior studies in that the exclusive-related acquisitions helped firms grow more and get more market share than the exclusive-unrelated acquisitions. CEO’s compensation composition ratio was not related to the types of acquisition.

This study aims to investigate the effect of product market competition on the exposure of firms’ returns to consumption fluctuations (C-CAPM beta).

The C-CAPM beta comes from a regression of a stock’s returns against consumption growth, with controls for the Fama–French three factors and momentum. The Herfindahl–Hirschman index of concentration measures competition, with other measures like deregulation and tariff reductions used for robustness tests. Industries are categorized using different SIC digits, with the NAICS measure used for robustness tests. The C-CAPM beta is regressed to competition, with appropriate control variables, to find its relationship.

Higher levels of competition reduces the C-CAPM beta. The results are consistently robust to different measures of product market competition and industry identification.

Product market competition influences the sensitivity of systematic risk, as measured by the C-CAPM beta, to consumption, such that higher levels of competition reduce systematic risk.

This research contributes to a literature that admittedly is still murky, as the relationship between competition and systematic risk is still unsettled. No study (to the authors’ knowledge) examines the effect of competition on firms’ exposure to consumption. This research adds to the literature on the role of competition in risk, specifically with respect to consumption.