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This paper investigates whether style migration affects industry evolution. The study documents industry evolution in terms of market weights, returns, and risks over the sample period from 1966 to 2000. The study shows that investment styles migrate in different degrees across different industries over time. In addition, the relation between industry evolution and style migration is neither simple nor static. The paper shows that growth‐value migration has predictability about the industries' returns and changes in volatility. Furthermore, style migration in the industry is mainly driven by existing firms changing their investment styles, not by new entrants to the industry causing style shifts. Both investment theory and its application to investment management critically depend on our understanding of stock return persistence anomalies. The ability to outperform buy‐and‐hold strategies by acquiring past winning stocks and selling past losing stocks, commonly referred to as “individual stock momentum,” remains one of the most puzzling of these anomalies. Moskowitz and Grinblatt (1999) attribute the bulk of the observed momentum in individual stock returns to industry momentum—the tendency for stock return patterns at the industry level to persist. It is well known that there are hot and cold IPO markets, and hot and cold sectors of the economy. Investors may simply herd toward (away from) these hot (cold) industries and sectors, causing price pressure that could create return persistence. The recent attraction to internet stocks is perhaps the latest manifestation of such behavior, which is not unlike a similar pattern biotechnology firms and railroad firms witnessed in 1980s and 1900s, respectively. For the active portfolio manager, rotation among different industries may provide opportunities for portfolio performance enhancement. As a result, understanding both the evolution of industries and the style factors causing cyclical variation in industry returns and risk plays an important role in professional portfolio management. Given the fact that a number of researchers have found consistent differences among the returns of various equity classes, investment styles of size and growth‐value are natural candidates for studying what causes cyclical variation in industry returns and risks. Individual investment styles perform differently during various stages of a cycle of bull market and bear market. For example, small cap stocks outperformed large cap stocks in the 1970s, but large cap stocks outperformed small cap stocks in the 1980s. Growth stocks outperformed value stocks in 1998 while the opposite occurred in 1997. Although it is well documented that the cross‐sectional variation in expected returns can be captured by three factors: market, size, and book‐to‐market, it is not yet clear whether cyclical variations in style attributes, not style returns, influence cross‐sectional variation in expected returns and return variance. In the investment industry, cyclical variation in style attributes is commonly called style migration. Perez‐Quiros and Timmermann (2000) provide a rational suggestion that small firms are most strongly affected by tighter credit market conditions in a recession and thus cyclical variations in style performance result from business cycles. As certain equity classes took off and others fell out of favor, investors overreacted, thereby causing cyclical variations in returns and risks of industries where firms are similarly sensitive to the fundamental shocks. In a recent study of behavioral finance, Barberis and Shleifer (2003) argue that in the presence of switchers who can affect asset prices by moving funds across styles, a style‐level momentum strategy could be successful because good performance by a style attracts switcher flows, which then drive the prices even higher. Analyzing the extent of interaction between style migrations and industry evolution may shed light on understanding the sources of predictable components in industry returns and risk. This paper provides such a contribution to the literature. The rest of the paper is organized as follows. Section I describes the sample data and summarizes industry evolution in terms of market capitalization weights in the entire market over time. Section II analyzes style migration within each industry. Section III examines the effect of style migration on industry evolution. Section IV concludes.

The purpose of this paper is to propose that transportation modal mix in global supply chains is a result of the strategic alignment between industry characteristics and supply chain strategies.

Using annual US trade statistics and manufacturing industry data for the years 2002-2009 between the USA and its top 12 Asian trading partners, this study applies various regression methods to examine key factors associated with the transport modal decision.

The results show that industry characteristics have an impact on the transportation modal mix in global supply chains. Manufacturing industries use more air freight and less ocean freight when facing positive sales surprises, high-monthly demand variation, a high-contribution margin ratio, a high cost of capital, and increased competition.

The findings provide important insights for logistics managers and freight forwarders. While transportation cost remains an important concern, a logistics manager must also consider non-cost factors such as competition, working capital, and demand uncertainties in their modal decisions. Freight forwarders should be supply chain solution providers who consider all of these industry factors and suggest a proper mix of transportation modes for their customers.

This study is among the first efforts to examine the impact of industry characteristics on the transportation modal mix in global supply chains. This study first develops a theoretical framework for the modal choice decision for international transportation movements and then, using an extensive and innovative data set, provides new findings regarding current air freight practices in global supply chains.

Considerable research efforts have been made to investigate the relative importance of firm‐specific vs industry structure factors in relation to performance variation among firms in the past. However, the vast majority of the research comes from the USA and very little is known about results outside of this domain. The aim of this study was to investigate industry and firm factors producing performance differences among Turkish firms. In order to explore the contributions of firm‐level factors and structural characteristics of industries, the study decomposes the relative impact of industry and firm effects on overall performance which includes the performance items such as sales turnover, market share and profitability.

A quantitative, positivistic approach was adopted with respect to the methodological choice for this study. In order to measure the relative impact of industry and firm effects on performance, the questionnaire developed by Galbreath and Galvin was sent to the e‐mail addresses of the general managers or the other executives at the top level as a web‐link with a covering letter. Because unit of analysis is at the firm level, a single informant is used in the study and the questionnaire was mailed to only one executive from each firm. Having collected the data, the effects of firm‐level factors (resources and capabilities) and industry structure on performance variation were analyzed by hierarchical regression method.

A total of 259 firms from different industries were analyzed and the findings revealed that firm‐level resources had a greater effect in explaining performance variation than industry structure in the Turkish business context. The results of this study confirm that in the resource‐based view of the firm, the firms in Turkey “demonstrated a quite developed form of organizational learning” just like the other emerging economies (i.e. Taiwan, Brazil, Poland and South Korea). Within this framework, Turkish firms especially in automotive, textile, food, tourism and construction industries became important players in the global arena.

This study contributes to the strategic management literature, particularly, in terms of providing comparable data from an emerging country, which is significant in verifying resource‐based theory and generalizing results in a global context. The findings also suggest that the firms need to focus on their unique resources rather than try to control and manipulate structural forces in their industries since “the economies today might best be viewed as resource‐based economies”. It should be noted that, in this business era, the key challenge for the managers is the optimal deployment of existing strategic resources in order to make their organizations achieve sustainable competitive advantage and superior firm performance.

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.

Administrative data on the universe of employees, firms, and unemployment insurance (UI) recipients in Canada over an 11-year period are used to examine the operation of UI using the firm as the unit of analysis. Persistent transfers through UI are present at both industry and firm levels, and an analysis using firm fixed effect indicates that an important fraction of variation in them can be attributed to firm effects. Calculations of overall efficiency loss are very sensitive to the degree to which firm-level information is used. A full appreciation of how UI interacts with the labour market requires recognition of the characteristics and human resource practices of firms.

The purpose of this paper is to propose a proactive public policy approach to complement relatively reactive existing policies addressing gender‐related employment disparities in the USA, and to provide an initial empirical illustration of the proposal.

The paper provides a conceptual application of theories of total quality management (TQM) to the topic of gender‐related employment disparities, followed by an empirical illustration using US Current Population Survey data and a gender equal employment opportunity (EEO) scorecard.

Using the TQM framework, company outliers were conceptualized on the EEO scorecard as “special” causes of economy‐wide equal employment variation and the industries in which companies are situated as “common” causes. The paper identifies two underperforming industries on gender‐related employment outcomes: Mining and Construction, and Transportation, Communication and Utilities.

Further conceptual work on the application of TQM to gender disparities in employment is recommended. Also, the study considered broad industry categories; future research should refine these categories further.

It is recommended that US enforcement agencies incorporate industry considerations more explicitly into their activities. Employer insights may be beneficial to improving equal employment opportunity performance at the industry level.

The application of TQM theory to the topic of gender‐related employment disparities is a novel approach that may motivate new public policies.

The purpose of this paper is to analyze the influence of firm-, industry- and country-level determinants on real annual sales growth in the context of a cross-classified multilevel perspective.

The authors studied 11,381 firms from 17 industries in six Latin American countries based on the data collected up to 2015. Since the data are nested in two levels (level 1: firms; level 2: cross-classification of industries and countries), the authors use a cross-classified multilevel model. The significant variability in all levels of analysis confirms the option for the multilevel model.

Differences in industries account for the largest proportion of variance (77.2 percent). This finding indicates that industry-level characteristics should be explored in the sales growth literature (it seems to the authors that they were neglected). This finding also calls attention to the roles of policy-makers in facilitating firm growth. The final model indicates that the considered variables explain approximately 55 percent of the differences in real annual sales growth in the same industry and country after having accounted for the impacts of the differences in firms. After accounting for the impacts of the differences in firms’ and countries’ characteristics, 43 percent of the variation in average real annual sales growth is due to differences in industries. The obtained results indicate that while firms from countries with higher GDP growth and more effective corporate boards present higher real annual sales growth, firms that operate in commodity producer industries have worse performance in this indicator. With respect to firm’s characteristics, larger firms (contradicting Gibrat’s law) and exporters grew less. Some results could be explained by the decrease in commodities’ prices and global purchases between 2012 and 2015.

The paper fills some gaps in the firm growth literature by testing Gibrat’s law in non-developed countries (not yet done, to the best of the authors’ knowledge) and exploring variables other than size in the explanation of firm growth (rarely used, to the best of the authors’ knowledge). Moreover, the adopted model correctly estimated the origin of the variability in firm growth in its natural cross-classified distinct levels.

The authors analyzed the evolution of working from home (WFH) within industries in 12 European countries in the period 2008–2017 and studied its relationship with information and communication technologies (ICT).

The authors used data from the European Union Labour Force Survey (EU-LFS) to document the trends and levels of WFH within industries in 12 European countries. The authors further used the EU-KLEMS database and a difference-in-difference approach to study whether the fall in prices of ICT is associated with a higher share of employees who work from home in industries that depend more on ICT relative to industries that depend less.

The authors show that WFH has increased almost everywhere and that there is significant heterogeneity across industries. The authors provide evidence that the fall in prices of ICT is associated with a higher share of employees who work from home in industries that depend more on ICT relative to industries that depend less. This result also holds within age, gender and occupation groups. While the authors find no significant differences among gender and occupation groups, the positive association between the fall in ICT prices and WFH increases with age.

This paper has two main contributions: First, it reports that WFH has increased in European countries in the period 2008–2017. Second, it provides new explorations about the relationship between ICT and WFH by using the price variation of ICT.

The purpose of this paper is to identify some of the major issues relating to the conceptualization and operationalization of industry globalization.

Globalized industries have four important characteristics: cross-border product flows, cross-border capital flows, dispersal of global value chains and global competition. However, lack of availability of data limits our ability to develop an operationalization that encompasses all these four aspects of globalization.

The authors identify some of the most important factors driving industry globalization as well as the major impediments to globalization.

Although the term “globalization” has attained a nearly “taken for granted” status, its meaning is rather vaguely specified and is often context dependent. This paper delineates the domain of the construct and identifies many of the practical issues in operationalizing the construct.

The purpose of this paper is to examine the evolution in subsidiary performance and the factors influencing this performance based on a unique database of approximately 800 multi-national company (MNC) subsidiaries in developing countries. Developed-country multi-national companies (MNCs) are increasingly establishing subsidiaries in developing countries. The potential gains are high; however, so are the risks. While the issue of subsidiary performance should be at the heart of any international business (IB) enquiry into MNC activity in developing countries, surprisingly little research has examined this issue.

Based on a comprehensive literature review of the IB performance literature, it is hypothesized that subsidiary performance essentially is shaped by five clusters of factors: location, industry, MNC capabilities, subsidiary role and entry strategy. These factors’ ability to explain variance in subsidiary performance is tested through a multiple regression analysis.

MNC subsidiary performance in developing countries has improved enormously in recent decades. Especially, MNC capability and subsidiary role-related factors appear to explain variance in performance, while location factors appear to have less explanatory power. This suggests that strong MNC capabilities and organization can make MNCs succeed regardless of location.

The key preparatory work for MNCs contemplating entry into developing countries is to carefully scrutinize internal capabilities and organization.

The paper presents a model for explaining variation in subsidiary performance in developing countries specifically. The paper offers unique empirical insights into the state and drivers of subsidiary performance in developing countries.