Corporate social responsibility and firm performance in the airline industry: The moderating role of oil prices

Tourism Management 38 (2013) 20e30

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Corporate social responsibility and firm performance in the airline industry: The moderating role of oil prices Seoki Lee a, *, Kwanglim Seo b,1, Amit Sharma a, 2 a b

School of Hospitality Management, The Pennsylvania State University, 217 Mateer Building, University Park, PA 16802, USA School of Travel Industry Management, University of Hawaii, Manoa, Honolulu, HI, USA

h i g h l i g h t s < The study found operation-related CSR positively affected firm performance. < Findings support the positive moderating effect of oil prices on CSR in airport OR operations. < Findings support the negative moderating effect of oil prices on Non-OR CSR dimensions.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 September 2011 Accepted 5 February 2013

This study, first, proposes corporate social responsibility (CSR) dimensions as associated with operationrelatedness (i.e., operation-related [OR] and non-operation-related [Non-OR] CSR activities), following the Carroll CSR framework. In addition, the study examines and compares the effects of OR and Non-OR CSR dimensions on U.S. airlines’ performances, and the final examination concerns the moderating effect of oil prices on the relationship between the OR (Non-OR) CSR dimension and firm performance. Findings of this study support a positive main effect from OR on firm performance. In addition, findings support the positive (negative) moderating effect of oil prices on the relationship between OR (Non-OR) CSR dimension. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Corporate social responsibility Operation-related CSR Non-operation-related CSR Airlines Firm performance Moderating effect of oil prices

1. Introduction The underlying issue of this study is questioning socially responsible corporations’ ability to maximize owners’ wealth. Increasing concerns for corporations’ broader responsibilities, beyond those to their direct owners, have only intensified the debate begun with the seminal research of Friedman (1970). Researchers have keenly investigated the link between corporate social responsibility and owners’ wealth, but unfortunately, without gaining a clear consensus (Margolis, Elfenbein, & Walsh, 2007). One of the problems involved in claiming generalizable results has been the challenge of modeling firms’ varied Corporate Social Responsibility (CSR) behaviors, and their effects on corporate financial performances (CFP). The CSR literature examined effects

* Corresponding author. Tel.: þ1 814 863 7442. E-mail addresses: [email protected] (S. Lee), [email protected] (K. Seo), [email protected] (A. Sharma). 1 Tel.: þ1 808 956 4884. 2 Tel.: þ1 814 865 0126. 0261-5177/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tourman.2013.02.002

of CSR on CFP in various manners including a linear form of CSR, higher order-functional forms of CSR, and multiple dimensions of CSR. The tourism literature also addressed the CSR topic in relation to CFP with similar approaches. Several studies, in various functional forms, investigated the effect of CSR on CFP for airlines, hotels, casinos, and restaurants (for example, Lee & Park, 2009, 2010; Nicolau, 2008; Park & Lee, 2009). Regarding sub-dimensions of CSR, Kang, Lee, and Huh (2010) examined effects of positive and negative CSR on CFP, and Inoue and Lee (2011) tested five dimensions of CSR on CFP for tourism and hospitality companies. However, as stated earlier, neither the general nor tourism CSR literature has produced or formulated a conclusive consensus, and the literature continues to encourage researchers to explore the CSR-CFP link (Campbell, 2007; Rowley & Berman, 2000). In an attempt to enrich the CSR literature, especially for the context of the airline industry, for which empirical investigations of the CSR-CFP link are an apparent need, the current study investigates two newly proposed dimensions of CSR activities based on their relevance to operations in the airline industry: operationrelated (OR) and non-operation-related (Non-OR) CSR activities,

S. Lee et al. / Tourism Management 38 (2013) 20e30

following the CSR framework proposed by Carroll (1991), who suggested four CSR dimensions for economic, legal, ethical and philanthropic activities, then later in 2003, modified the framework by combining the philanthropic dimension with the ethical dimension, resulting in the three-dimensional framework: economic, legal and ethical (Schwartz & Carroll, 2003). Based on Carroll’s CSR framework, mainly following the two dimensions (economic and ethical), the current study proposes OR and Non-OR CSR dimensions, which are those activities that have direct or clear implications for firms’ core business operations, representing much of the economic, and perhaps, some legal dimensions proposed by Carroll. OR CSR activities include improvements to product quality, employee relationships or treatment, and corporate governance. In contrast, this study defines Non-OR CSR activities as those CSR activities that firms ought to engage as ethical or responsible, societal citizens, despite a lack of direct implications for a firm’s operations, closely following Carroll’s ethical and possible legal applications. Such Non-OR CSR activities may include activities that promote human rights, develop community relationships, support environmental issues, and encourage diversity. With these newly proposed CSR dimensions, the current study, first, hypothesizes that both OR and Non-OR CSR activities positively impact firms’ performances according to the stakeholder theory (Freeman, 1984) and instrumental theory (Garriga & Melé, 2004). Expanding this notion, this study also proposes that the positive effect of OR CSR is greater than the effect of Non-OR CSR on firms’ performances because the OR CSR dimension has a more direct relationship with airlines’ operations than Non-OR CSR dimension. After examinations of the main effects of, and a comparison between, the two CSR dimensions, this study hypothesizes that oil prices moderate the positive effect of the two CSR dimensions on firms’ performances. In detail, hypothetically, oil prices negatively moderate the effect of both CSR dimensions on firms’ performances, due to the significant, negative impact increased oil prices impose on airlines (Clarke, 2008). From the difficulty of rising fuel costs, financial markets may perceive that airline companies’ investments in CSR activities are inappropriate or deteriorate value. Despite the fact that the airline industry has embraced the CSR phenomenon, empirical studies in academic literature for such a corporate strategy’s value have been sparse, especially in the CFP context. Lee and Park (2010) produced one of a few studies, but it focused only on the aggregated CSR measure which might result in an inappropriate validity issue for CSR measurement in the construct (Mattingly & Berman, 2006). Cowper-Smith and de Grosbois (2011) provided rich findings for airlines’ CSR activities by examining airlines’ CSR reports, but their examination was qualitative and lacked a direct link to CFP. Moreover, considering that an industry-effect apparently exists in the CSR literature (Cottrill, 1990), an examination of the proposed hypotheses specifically for the airline industry, a homogenous group, should be able to provide more valid and relevant findings. By further incorporating the industry-specific factor (i.e., oil price) in the model, the current study’s unique contributions to the literature become clear. Therefore, this study enriches the tourism CSR literature by exploring the relatively unexamined link for the industry, and the general CSR literature, in terms of CSR and CFP, by proposing newly defined dimensions of CSR and the links to CFP along with the moderating effect of oil prices. This study performs a Two-Way Random-Effects Model by firm and year to accomplish its goals, by applying the model to the airline industry for the period, 1991 to 2009 and using KLD STATS (further information appears in Section 4.2, the Main Variables Section) for CSR data, widely accepted by a majority of recent CSRCFP studies (Mattingly & Berman, 2006).

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Next, the study reviews the relevant CSR literature, and describes the methodology including the models, study variables, and data. Results and discussions follow, and the study concludes with identified limitations and suggestions for future research. 2. Literature review 2.1. Corporate social responsibility and corporate financial performance Increasingly, many firms have begun to consider their roles in society and to attend closely to the application of social standards to their businesses (Lichtenstein, Drumwright, & Braig, 2004). Academicians have responded to this interest in corporate social responsibility (CSR) and examined its impact on corporate financial performance (CFP) at both macro-social and organizational levels (Lindgreen & Swane, 2010). CSR refers to “actions that appear to further some social good, beyond the interest of the firm and that which is required by law” (McWilliams & Siegel, 2001, p. 117). This implies that CSR investment does not particularly serve the best interests of the firm, profit maximization (McWilliams & Siegel, 2001). That is, firms may need to engage in socially responsible activities despite potential decrease in firm value (Mitchell, Agle, & Wood, 1997; Paine, 2002). Donaldson and Davis (1991) provided theoretical arguments for this view: The importance for firms’ doing “the right thing” transcends the impact on CFP. Consequently, firms may confront the conflict of CSR investments not coinciding with economic objectives (Donaldson & Preston, 1995; Freeman, 1984; Mitchell et al., 1997; Paine, 2002; Wood & Jones, 1995). Therefore, identifying a positive link between CSR and CFP effectively resolves the conflict (Mackey, Mackey, & Barney, 2007). Many scholars posited that firms can benefit from a competitive advantage created by CSR. For example, consumers reward socially responsible firms in the market by showing higher purchase intentions (Mohr & Webb, 2005) and a willingness to pay higher prices (Kang, Stein, Heo, & Lee, 2012; Laroche, Bergeron, & BarbaroForleo, 2001) for firms’ products and services. CSR can also contribute positively to CFP by increasing product recognition (Parket & Eilbirt, 1975), enhancing employee motivation (Stodder, 1998), creating positive employee attitudes (Brammer, Millington, & Rayton, 2007; Rupp, Ganapathi, Aguilera, & Williams, 2006), and improving a firm’s public image (Fombrun & Shanley, 1990). Hence, the interest in management literature for CSR has focused primarily on discovering whether or not firms can “do well by doing good” (McWilliams, Siegel, & Wright, 2006). A large number of studies empirically tested the relationship between CSR and CFP, but little consistency arose from the findings. Using different methodologies, these studies found a positive, negative, and no relationship between CSR and CFP. First, some scholars predicted a negative impact of CSR on financial performance because CSR represents additional costs to the firm (McGuire, Sundgren, & Schneeweis, 1988; Riahi-Belkaoui, 1992; Shane & Spicer, 1983; Vance, 1975). From an agency theory perspective, Friedman (1970) asserted that CSR may cause firms to misallocate corporate resources that might otherwise increase shareholders’ value. For example, managers can exploit CSR to fulfill their own personal interests such as building their careers and reputations. In addition, McGuire et al. (1988) maintained that firms incur extra costs in an effort to benefit society as a whole, and these costs often do not directly relate to firms’ operations or generating profits (Lindgreen & Swane, 2010). For instance, communicating concern for social and environmental issues through advertising may enhance a firm’s reputation, but provides no guarantee for immediate sales increases. A strongly supported perspective asserts that accounting-based performance does not

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reflect economic values of intangible assets such as customer satisfaction, brand, and reputation (Brown & Perry, 1994; Hopwood, 1972; Ittner & Larcker, 1998; McGuire et al., 1998). Since social initiatives do not generate immediate profits, non-operational related CSR activities may negatively relate to firms’ accounting performances. Empirical evidence provided support for these views. Vance (1975), using surveys of managers and students who rated 50 leading firms according to perceived degree of social responsibility, found a negative association between rankings and stock performances. Wright and Ferris (1997) confirmed this association by examining US firms’ stock performances after announcing divestment of South African assets. Second, other scholars argued that CSR does not increase or decrease profitability of a firm (Abbott & Monsen, 1979; Alexander & Buchholz, 1978; Aupperle, Carroll, & Hatfield, 1985; Chand, 2006; Griffin & Mahon, 1997; Teoh, Wong, & Rao, 1998). These studies raised concern for any link found between CSR and financial performance might be misleading because of misspecification in the research design. For instance, in prior studies, the nature of the industry in which firms operate may have a strong influence on the results (Chand, 2006). These researchers contended difficulty for justifying a direct and clear link between CSR and financial performance. Finally, most empirical research found a positive impact from CSR on a firm’s financial performance (Aragón-Correa, HurtadoTorres, Sharma, & García-Morales, 2008; Bird, Hall, Momente, & Reggiani, 2007; Grave & Waddock, 1994; Judge & Douglas, 1998; Nicolau, 2008; Orlitzky, Schmidt, & Rynes, 2003; Parket & Eilbirt, 1975; Preston & O’Bannon, 1997; Rey & Nguyen, 2005; Russo & Fouts, 1997). The findings of these studies demonstrated that CSR can help firms meet interests of shareholders and other stakeholders. Freeman (1984) argued that sometimes firms should forgo the interest of shareholders to satisfy numerous stakeholders such as employees, suppliers, consumers, and communities. This, however, conflicts with a firm’s wealth-maximizing purpose. On the other hand, some scholars asserted that firms employing CSR can earn excess returns by integrating non-economic factors into creating competitive advantages (Belkaoui, 1976; Godfrey, 2004; McWilliams & Siegel, 2001; Porter & Kramer, 2003; Waddock & Grave, 1997), thereby supporting the argument that socially responsible actions may enable firms to maximize shareholders’ value while satisfying other stakeholders (Mackey et al., 2007). Inconclusive findings for an effect from CSR on financial performance identify a need to investigate this relation. In particular, some scholars raised concerns for a spurious correlation due to omission of variables (Cochran & Wood, 1984), biases in measurements (Abbott & Monsen, 1979; McGuire et al., 1988; Ullmann, 1985), and a lack of theory connecting CSR with market influences (McWilliams & Siegel, 2001). Among them, McWilliams & Siegel, 2001 emphasized the influence of industries’, products’ and firm’s characteristics, arguing that the levels and types of CSR that firms undertake vary among these factors. In other words, McWillams and Siegel maintained a critical consideration for the nature of the markets and industries to assess CSR attributes. Therefore, following those arguments, this study focuses on investigating the influence of CSR on CFP in a particular sector: the airline industry. 2.2. CSR and CFP in the tourism literature Recently, the tourism industry exhibited a growing interest in corporate social responsibility (CSR) (Inoue & Lee, 2011). Many tourism-related firms are attempting to enhance their public images through CSR activities, such as recycling, supporting local communities, promoting diversity in the workplace, producing

more products organically, and donating to charities (McGehee, Wattanakamolchai, Perdue, & Calvert, 2009). For example, McGehee et al. (2009) maintained benefits for tourism firms’ undertaking CSR from improving employees’ morale and retention, customers’ loyalty, and brand image. A widely accepted notion asserts that these improvements positively impact financial performance (Campbell, 2007). However, despite popularity among tourism firms, few studies empirically test the impact of CSR on CFP (Kang et al., 2010). Further, the findings of existing studies have been inconclusive. A number of scholars found a positive relationship between CSR and CFP in the lodging and restaurant industries (Garcia &Armas, 2007; Kang et al., 2010; Lee & Park, 2009; Nicolau, 2008; Rodriguez & Cruz, 2007; Tse & Ng, 2003). Others found no significant association between CSR and CFP for the casino industry (Kang et al., 2010; Lee & Park, 2009). The Kang et al., 2010 study, investigating separate effects of positive and negative CSR activities on accounting-based and market-based performances among four industries: hotels, restaurants, casinos, and airlines, provided controversial results. First, positive CSR activities increased firms’ market-based performance while no significant relationship appeared between negative CSR activities and market-based performance for hotels and restaurants. Neither positive nor negative CSR activities seemed to relate to accounting-based performance in these two industries. Second, no particular relationships appeared between CSR activities and financial performance in the casino industry. Third, positive CSR activities decreased accounting-based performance, while negative CSR activities had no influence on accounting-based performance in the airline industry. On the other hand, negative CSR activities deteriorated market-based performance; whereas, positive CSR activities and market-based performance presented no significant relational effect. Therefore, the findings of the Kang et al. study supported the positive effect of CSR on CFP in the sectors of hotels and restaurants. In the airline industry, a negative impact from positive CSR activities on accounting-based performance suggests that undertaking CSR activities adds extra costs to airline companies that may cause a short-term decrease in financial performance. Lee and Park (2010) partially supported this view, finding no significant impact of CSR on accounting-based performance in the airline industry. However, they also found a positive and linear impact of CSR on market-based performance. McWilliams & Siegel (2001) posited that in equilibrium, CSR will have no impact on firm profits. McWilliams et al. (2006) referred to this finding as a “neutrality result” and argued for the impossibility of firms’ using CSR to out-perform competitors and achieve abnormal profits. In particular, McWilliams et al. predicted that firms in a monopolistically competitive industry, such as that of restaurants, will not earn abnormal returns by engaging in CSR. Some of the characteristics that typify a monopolistically competitive industry are: multiple sellers, differentiated but similar products, and free entry and exit (Chamberlin, 1933; Dixit & Stiglitz, 1977; Robinson, 1933). Restaurant industry is arguably monopolistically competitive since it contains a large number of small firms selling a differentiated product but not perfect substitutes (Boland, Crespi, Silva, & Xia, 2012). For instance, while many restaurants sell a popular cheeseburger some charge higher prices based on physical and perceived differences. Under monopolistic competition, restaurants with differentiated products tend to achieve economic profits in the short term. That is, these economic profits will not endure over the long-term because new restaurants can easily enter the market to capitalize on profitable opportunities (Elhauge, 2009). This characteristic is, however, less likely for airlines, for which entry to the market is relatively difficult due to high initial fixed costs (Borenstein, 1992). Airlines tend to be more oligopolistic that a

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market structure is dominated by a small number of large firms (Aguirregabiria, 2012; Brander, 1993). In addition, airlines are highly interdependent and offer relatively identical products and services in similar price ranges (Barkin, Hertzell, & Young, 2007). For instance, most airlines match competitors’ reduced airfares for the same route, thereby generating high levels of competition in non price-related areas such as marketing (Yip, 1982), and suggesting that employing CSR-based strategies could improve competitive advantages and operational profit margins. The mixed findings of the relationship between CSR and CFP in the existing literature compel further investigation of the CSR-CFP relationship for airlines. The current study, therefore, attempts to examine the impacts of CSR on value performance while separating CSR into operational and non-operational related activities. 2.3. CSR dimensions The multidimensionality of CSR is an important area for discussion in the CSR literature. Dimensionality issues also exist at different levels: some exist at the macro-level (Margolis & Walsh, 2003), while others are at the micro-level (for example, Dahlsrud, 2008; Garriga & Melé, 2004). Margolis and Walsh (2003) adopted an organizational theory and empirical perspective for identifying descriptive and normative dimensions of business’ responses to the need for social initiatives. They identified five areas for research: The first is “appraising the stimuli” or understanding which social ills (requiring social initiatives) garner attention by which firms. The second is the options businesses have to generate responses to these stimuli. The third is the methods by which companies evaluate these responsive options and the nature of management decision-making criteria. The fourth is the techniques for implementing options to become socially responsible, after evaluating the options. And the final area is consideration of the consequences of companies’ socially responsible acts. At the macro-level, Garriga and Melé (2004) presented a clarification of various theoretical approaches to studying corporate social responsibility. They identified four groups for organizing these approaches: instrumental theories, political theories, integrative theories, and ethical theories. Instrumental theories view firms as instruments directed toward achieving economic goals. Therefore, their socially responsible activities are simply a means of accomplishing these goals. In political theories, firms’ concerns are for firms’ power in society and the responsible exertion of that power. The view taken by integrative theories is that firms focus on the satisfaction of social demands. And, finally, the ethical theories emphasize firms’ ethical responsibilities to society. Each of these theories represents a distinct dimension of CSR related to profits, political performance, social demand, and ethical value. Similarly, Dahlsrud (2008) developed five dimensions for CSR through a content analysis of existing definitions. The five dimensions identified are the ones most likely to appear in a definition of CSR. These five dimensions are: stakeholder, social, economic, voluntariness, and environmental dimensions; the order represents the hierarchy or inclusion in a definition of CSR. The stakeholder dimension refers to various groups of stakeholders, not limited to shareholders. The social dimension includes the relationship between business and society. The economic dimension refers to the socio-economic or financial aspects of the business defined from an operational perspective. Voluntarism referred to actions not prescribed by law, while the environmental dimension denotes activities that conserve the natural environment. However, only about 8% of all CSR definitions include all five dimensions. Dahlsrud emphasized confusion in understanding CSR arises less from definitions and more from the social construct of CSR in a specific context.

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Another perspective, at the micro level, is that of Burke and Logsdon (1996) who identified five strategic dimensions of CSR that may help assess the value generated from CSR programs for a firm. These dimensions are: centrality, specificity, proactivity, voluntarism, and visibility. Centrality represents the closeness of fit between the CSR program and the firms’ central mission. Specificity refers to the firms’ ability to realize specific benefits from CSR. The third dimension of proactivity reflects the firms’ initiative rather than reactive behavior. Voluntarism indicates whether or not the firms’ CSR decisions are independent of externally imposed compliance requirements. And finally, visibility refers to firms’ abilities to gain recognition from CSR activities among internal and external stakeholders. 3. Hypothesis development According to the CSR dimensionality literature, operationrelated (OR) CSR dimension proposed by the current study may be likely to satisfy the CSR definition for which instrumental CSR theorists argued (Garriga & Melé, 2004), specifically that a firm’s socially responsible activities, should be a means for accomplishing the firm’s economic goals, those closely related to the economic dimension of the Carroll’s CSR framework (Carroll, 1991). Similar to the instrumental CSR viewpoint, perhaps, OR CSR activities assist airlines’ improving their operational efficiencies because such investments directly relate to companies’ operations and economic benefits. Consequently, the expectation is that the OR CSR dimension has a positive impact on airlines’ performances. H1. OR CSR activities improve airlines’ performances. The non-operation-related (Non-OR) CSR dimension may receive great support from ethical CSR theories (Garriga & Melé, 2004) because those theories argued that a firm’s ethical activities should be, exclusively, considerations of CSR activities, closely following the ethical dimension of the Carroll’s CSR framework (Carroll, 1991). Although Non-OR CSR activities may not directly increase airlines’ operational efficiencies, according to the stakeholder theory (Freeman, 1984), such investments would, expectedly, improve companies’ brand recognition and customers’ satisfaction (Kang et al., 2010; Lee & Park, 2010), both of which, consequently, enhance airlines’ performances. H2. Non-OR CSR activities improve airlines’ performances. Despite the current study’s proposing a similar, positive effect regarding the effect of OR and Non-OR CSR activities on firms’ performances, considering that airlines are perceived to be risky and competitive (Zea, 2003), especially in view of bankruptcies in the 2000s (Air Transport Association, 2011), the markets may more willingly approve of the airlines’ CSR strategies that directly influence operations (OR CSR), rather than those with indirect or ethical implications on business (Non-OR CSR). Based on this argument, this study proposes that the positive effect from OR CSR is greater than from Non-OR CSR. H3. The positive effect of OR CSR activities on airlines’ performances is greater than Non-OR CSR activities. Next, this study argues that the positive effect of OR and Non-OR CSRs on firm performance varies according to changes in oil prices. Oil prices significantly impact airlines’ operations (Clarke, 2008), and operational challenges caused by oil prices’ increases are likely to force airlines to focus on improving their operational efficiencies and cost controls by minimizing other expenditures, including CSR activities. In such an environment, financial markets may perceive airlines’ investments in CSR activities in general as inappropriate or undesirable, and consequently, devalue the companies. However, this argument may not hold for OR CSR practices because of their

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direct and positive implications for companies’ core business operations. In fact, financial markets may reward the companies that heavily practice OR CSR activities in relation to oil price changes. Therefore, this study proposes a positive (negative) moderating effect from oil prices on the relationship between OR (Non-OR) CSR activities and firms’ performances. H4. Oil prices positively moderate the effect of OR CSR dimensions on airlines’ financial performances. H5. Oil prices negatively moderate the effect of Non-OR CSR dimensions on airlines’ financial performances. 4. Methodology 4.1. Data This study uses five sources to collect required data: retrieved annual financial data, such as total assets, total liabilities and stock prices, are from Compustat; KLD STATS provided corporate social responsibility data; collected GDP data is from U.S. Bureau of Economic Analysis, and fourth, the U.S. Energy Information Administration provided oil price data. The fifth source is the consumer price index (CPI) information from the U.S. Bureau of Labor Statistics to adjust GDP and oil prices for inflation. The sample period is from 1991 to 2009, the period with available KLD STATS data. The major determinant for this study’s sampled observations is KLD STATS because the data set does not provide CSR information for all publicly traded U.S. airlines, but only those public U.S. airlines included in either the S&P 500 or the Russell 3000 indices. After completion of data collection with the initial sample size of 162 observations, the study identified outliers, based on the cut-off of standardized residuals calculated from the full model at the 0.01 significance level (Anderson, Sweeney, & Williams, 2005). The outlier check applies to each of the two models, individually. After removing outliers for each of the two models, the final sample remaining is 157 observations for both models. The data is unbalanced panel data with 17 sampled airlines including number of observations ranging from two (Hawaiian Airlines) to 19 (American Airlines) per each airline. 4.2. Main variables This section describes the current study’s dependent variable and main factors. First, the study examines firms’ performances as a dependent variable and uses Tobin’s Q (Q) to represent a firm’s performance. In particular, the study uses approximate q according to Chung and Pruitt (1994). The calculation is: approximate q ¼ (MVE þ PS þ DEBT)/TA, where MVE is the product of a firm’s stock price and the number of common shares outstanding; PS represents the liquidating value of outstanding preferred shares; DEBT is the value of short-term liabilities, net of short-term assets plus the book value of long-term assets, and TA represents the book value of total assets. The literature suggests that Tobin’s Q (Q) may represent a firm’s performance better than other performance measures such as accounting measures (e.g., ROA and ROE) or stock growth rates (i.e., stock returns) (Lang & Stulz, 1994). One of the main reasons for the preference for Q is that this measure not only shows the firm’s past performance, like ROA and ROE, but also represents the firm’s value including its future prospects. Moreover, it is not necessary to adjust Q for risks (Lang & Stulz, 1994). This study adopts a log transformation of Q to alleviate non-normality problem. Next, the study includes two main factors in the models: operation-related (OR) and non-OR CSR activities. The study uses

the KLD STATS data set provided by KLD Research & Analytics, Inc. that has been providing comprehensive ratings of U.S. corporations’ socially responsible activities. The database, established in 1991, includes companies of the S&P 500 and the Domini 400 social indices, and evaluates multiple dimensions, such as employee relations, product quality, the natural environment, diversity, community relations, corporate governance, human rights, and other controversial business issues (e.g., alcohol, military, tobacco, and gambling). In 2001, KLD Research & Analytics, Inc. extended its listings to the Russell 1000 and in 2003, to the Russell 3000. For airlines, the CSR data retrieved from KLD STATS reveals that the seven dimensions have relevance: employee relations, product quality, the natural environment, diversity, community relations, and corporate governance. OR CSR activities include three dimensions: employee relations, product quality, and corporate governance, while Non-OR CSR activities include the other four dimensions: the natural environment, diversity, human rights and community relations. The ratings provide a binary value for each category as either strength or concern. Values of strength categories are considered as positive numbers while values of concern categories are considered as negative numbers. Summation of values of all categories for each CSR dimension represents the dimension. This study’s proposal expects both OR and Non-OR CSR activities to have a positive effect on an airline’s performance. Another main factor of the study is oil prices (OIL) imposing an expected negative impact on airlines’ performances because oil prices directly increase airlines’ operational costs. However, the main theme of this study regarding oil prices coincides with two interaction terms of OIL with OR (OR  OIL) and Non-OR (NonOR  OIL). This study proposes a positive (negative) moderating effect from OIL on OR (Non-OR) CSR dimensions. OIL represents the oil price per barrel for a given year, measured by the log of the annualized monthly-average oil price adjusted by the consumer price index (CPI). However, there is an important issue in this measurement. Airlines usually engage in fuel hedging (Carter, Rogers, & Simkins, 2006), and each airline may have different exposure or net position to oil price. Assuming that the market is efficient, oil price may have different implications for the airlines. Some airlines would have net negative cash flows, while some may even have net positive cash flows when oil appreciates, depending on their position on oil. Considering the reality that airlines hedge extensively, the airlines’ estimated exposure weighted by oil price (OILEXP) may be a better proxy. However, when this study estimated such estimated oil exposure (OILEXP), the sample size significantly reduced to 92 from 157 due to lack of data availability. To be more comprehensive, this study performs the analyses using the both sample sets. Previous studies showed a number of measurement issues that could affect exposure beta estimates (Brown & Warner, 1985; Dimson, 1979; Fowler, Jog, & Rorke, 1980; Fowler & Rorke, 1983; Scholes & Williams, 1977; Tufano, 1998). In particular, Tufano (1998) argued that when the return on an infrequently traded stock is used to estimate the beta, the correlation between the return on the security and the market index can be reduced resulting in biased and inconsistent beta estimates. The bias can be significant when shorter return intervals such as daily and weekly are used. However, several adjustment techniques have been introduced to control for this infrequent trading problem (Dimson, 1979; Fowler & Rorke, 1983; Scholes & Williams, 1977; Tufano, 1998). Following the approach used in Tufano’s study (1998), this study regresses the security return of each airline firm against lagged, synchronous and leading values of the market return and oil return using daily data for each firm for each year. The regression model is as follows:

S. Lee et al. / Tourism Management 38 (2013) 20e30 1 X

Rit ¼ ai þ

bio;k Ro;tþk þ

k ¼ 1

1 X

bim;k Rm;tþk þ εit

k ¼ 1

where Rit is the security return of firm i in year t, Ro, tþk is the oil return with the appropriate lag and lead, and Rm, tþk is the market return with the appropriate lag and lead. Tufano (1998) examined the exposure of gold mining firms in the US and Canada to changes in the price of gold. In his study he estimated gold betas using the approach by Scholes-Williams (1977). The Scholes-Williams betas are defined as:

bi0;t ¼ bio;0 þ

 1 þ r1 þ r2  bio;1 ; bio;þ1 1 þ 2r1

where r1is the first order serial correlation coefficient of Ro and r2is the second order serial correlation coefficient of Ro. Hence, this study estimates oil exposure (OILEXP) by applying the equation above, using daily data. The models section will present models including only OIL, not OILEXP, to avoid unnecessary complexity. Results of the models including OILEXP will be presented and discussed in the results and subsequent sections. 4.3. Control variables The model includes five control variables: firm size, capital structure, profitability, dividend payout, and economic conditions. First, firm size (SIZE) controls for any confounding effects that a firm’s size may impose on the relationship between CSR activities and the firm’s performance. According to the economies of scale, large firms perform better than small firms because large firms tend to achieve better efficiencies in their operations including greater purchasing power and reduced costs (Gelles & Mitchell, 1996). However, an opposite effect may exist; large firms may incur greater costs due to their more complex operations and systems (Canbäck, Samouel, & Price, 2006). This study, therefore, does not expect a particular direction for the control variable for size (SIZE), measured by the log of total assets. Second, this study controls for a firm’s capital structure. Based on the trade-off theory (Kraus & Litzenberger, 1973), a firm’s capital structure (measured by leverage ratio e total liabilities divided by total assets) has implications for the firm’s performance. Tax benefits accrue from increasing a firm’s leverage (i.e., interest expenses are tax deductible) while a firm’s high leverage ratio may indicate that the firm is highly risky, and consequently applies a negative implications on the firm. Since the effect of a firm’s leverage (LEV) is dependent on the level of this variable, this study does not maintain a one-directional prediction for this variable. Third, the model includes ROA because a firm’s short-term profitability (often reflected by accounting performance) generally has positive implications for value performance. The fourth control variable is dividend payout ratio (DIV), and this study expects a positive coefficient for DIV, because, based on the Gordon growth model (Gordon,1959), higher dividends mean the firm’s value is higher. Last, the study controls for economic conditions since general economic conditions positively correlate with firms’ performances. The study uses real gross domestic product chained to 2005 dollars (retrieved from the U.S. Bureau of Economic Analysis) to represent economic conditions (GDP). 4.4. Models The current study uses regression methods for the main analysis. Two models examine four (H1, H2, H4, and H5) of the five hypotheses; Equation (1) tests H1 and H2 while Equation (2) tests H4 and H5. The study performs an independent t-test to compare

25

the positive effect between OR and Non-OR CSR dimensions (H3). The two equations are:

Qt ¼ a0 þ a1 ORt þ a2 Non  ORt þ a3 OILt þ a4 SIZEt þ a5 LEVt þ a6 ROAt þ a7 DIVt þ a8 GDPt þ εt (1) and

Qt ¼ b0 þ b1 ORt þ b2 Non  ORt þ b3 OIL þ b4 OR  OILt þ b5 Non  OR  OILt þ b6 SIZEt þ b7 LEVt þ b8 ROAt þ b9 DIVt þ b10 GDPt þ et

(2)

where, Q represents a firm’s value performance, measured by the log of Tobin’s Q; OR represents operation-related CSR activities, measured by aggregating values for three CSR dimensions (corporate governance, employee relations and product quality) from KLD STATS (details appear in 4.2. Main Variables Section); Non-OR represents non-operation-related CSR activities, measured by aggregating values for four CSR dimensions (community relations, diversity, natural environment, and humanity) from KLD STATS; OIL represents oil prices adjusted by consumer price index (CPI); OR  OIL represents an interaction term for OR and OIL; NonOR  OIL represents an interaction term for Non-OR and OIL; SIZE represents a firm’s size, measured by the log of total assets; LEV represents leverage, measured by total liabilities scaled by total equity; ROA represents return on assets, measured by net income scaled by total assets; DIV represents dividend payout ratio; GDP represents economic conditions, measured by real gross domestic product chained to 2005 dollars (U.S. Bureau of Economic Analysis); ε and e represent error terms, and subscript, t, represents time. Panel data often suffers from group effects, time effects, or both. Two widely adopted methods account for these issues: FixedEffects and Random-Effects Models. Since one of the current study’s main study variables is oil prices which are invariant among firms within a given year, the Two-Way Fixed-Effects Model by firm and year is not appropriate for this study (Wooldridge, 2002), and therefore, analysis performs a Two-Way Random-Effects Model (TWOREM) to correctly deal with these effects in the process of estimating coefficients (Gujarati, 2003; Wooldridge, 2002). To determine whether or not TWOREM is more appropriate than a pooled OLS, this study performs a Breusch Pagan Test for the proposed two models The results suggest that TWOREM is more appropriate than a pooled OLS; Equations (1) and (2) produce the statistic of 2338.82 and 2486.87, respectively from the test, with both p-values less than 0.0001. 5. Results 5.1. Descriptive analysis The study performs a descriptive statistics analysis of the data set, and Table 1 presents results. Tobin’s Q (Q) shows a mean value of 0.766, ranging from 0.371 to 1.789, indicating that the sampled U.S. airlines, on average, present firms’ market values at less than replacement costs (approximately, 0.8 times). Accounting performance (ROA) has a minimum (maximum) value of 0.306 (0.226) with a mean value of 0.010. The operation-related CSR activity variable (OR) shows a mean value of 0.401 with the minimum (maximum) value of 7 (5) while non-operation-related CSR activity variable (Non-OR) shows a mean value of 0.681 with the minimum (maximum) value of 2 (4). Total assets range from $229 million to $45,014 million with a mean value of $9955 million, while revenues range from $361 million to $28,063 million with a mean value of $7997 million. Net income has a mean value of -$135

26

S. Lee et al. / Tourism Management 38 (2013) 20e30

Table 1 Summary of descriptive Statisticsa.

Table 3 Test

Variable

N

Mean

S.D.

Minimum

Maximum

Q ROA OR Non-OR Total assets (in millions) Revenues (in millions) Net income (in millions) LEV DIV OIL GDP (in billions)

157 157 157 157 157 157 157 157 157 19 19

0.766 0.010 0.401 0.681 9955 7997 135 0.787 0.064 39.40 10,604

0.309 0.068 2.097 1.144 9719 7083 1137 0.175 0.242 22.28 1826

0.371 0.306 7.000 2.000 229 361 8922 0.324 0.000 15.48 8015

1.789 0.226 5.000 4.000 45,014 28,063 2093 1.253 2.150 94.24 13,229

a Q represents a firm’s value performance, measured by Tobin’s Q; ROA represents return on assets, measured by net income scaled by total assets; OR represents a firm’s level of operation-related corporate social responsibility (CSR) activities, measured by KLD STATS; Non-OR represents a firm’s level of non-operation-related CSR activities, measured by KLD STATS; LEV represents leverage, measured by total liabilities scaled by total assets; DIV represents dividend payout ratio; OIL represents oil price per barrel for a given year, measured by annualized monthly-average price adjusted by consumer price index (CPI), and GDP represents gross domestic product, measured by chained 2005 dollars provided by U.S. Bureau of Economic Analysis.

million and a firm’s leverage ratio (LEV) shows a minimum (maximum) value of 0.324 (1.253) with a mean value of 0.787. Dividend payout per share (DIV) is, on average, $0.064 with a minimum (maximum) of $0 ($2.15). Next, the study performs Pearson’s correlation analysis for an examination of a bivariate relationship among study variables, and results appear in Table 2. Q positively correlates with OR (r ¼ 0.484) and ROA (r ¼ 0.330) while negatively correlating with SIZE (r ¼ 0.311) and LEV (r ¼ 0.428). While OR negatively correlates with SIZE (r ¼ 0.198) and LEV (r ¼ 0.457), Non-OR positively correlates with SIZE (r ¼ 0.310). Non-OR also negatively correlates with LEV (r ¼ 0.229). Oil price (OIL) shows a positive correlation with LEV (r ¼ 0.232) and GDP (r ¼ 0.817), while negatively correlating with ROA (r ¼ 0.162). In addition to the correlation with Q, ROA shows a positive correlation with OR (r ¼ 0.246) and a negative correlation with SIZE (r ¼ 0.406), LEV (r ¼ 0.589), and DIV (r ¼ 0.328). An examination of correlations among independent variables indicates no severe correlation that may cause a potential multicollinearity problem. The highest r-value is 0.817 between OIL and GDP. 5.2. Main analysis Before conducting the main analysis, this study performs normality test and also checks skewness of the two models. Table 3 shows results of the ShapiroeWilk test for checking normality; W

Statistic

P Value

Tests for normality for the model without interaction terms ShapiroeWilk W 0.99429 Pr < W Tests for normality for the model with interaction terms ShapiroeWilk W 0.99237 Pr < W

0.7994 0.5672

statistics are 0.99429 (p-value of 0.7994) and 0.99237 (p-value of 0.5672) for the models without and with interaction terms, respectively. These findings fail to reject the null hypothesis so that the normality assumption incurs no violation. Three other tests (KolmogoroveSmirnov, Cramer-von Mises, and Anderson-Darling) for checking normality confirm the findings. Next, the study estimates values of skewness for the models with and without interaction terms, 0.01 and 0.07, respectively; the near zero value represents a symmetrical normal distribution. For conducting the main analysis that tests the five proposed hypotheses, this study performs a Two-Way Random-Effects Model. Table 4 presents results of main effects from the model with OIL (i.e., raw oil prices) in three panels. Panel I shows the results of testing the model asserting that OR (H1) and Non-OR (H2) CSR activities improve airlines’ performances: the main effect of OR and Non-OR. OR shows a positive and statistically significant coefficient (t-value ¼ 2.97; p-value ¼ 0.0035), supporting H1 while Non-OR presents an insignificant coefficient (tvalue ¼ 1.45; p-value ¼ 0.1503), thus failing to support H2. Although the oil price variable (OIL) shows a negative coefficient, as expected, (coefficient ¼ 0.155), it is not statistically significant (t-value ¼ 1.57; p-value ¼ 0.1186). Other significant control variables are SIZE (t-value ¼ 4.59), LEV (t-value ¼ 5.40) and ROA (t-value ¼ 4.51). There are, however, two additional panels included to more comprehensively examine the two main effects. Panel II shows the results of the two main effects from the model with OILEXP (i.e., oil exposure as discussed in 4.2. Main variables). This additional model is tested to control for airlines’ real exposure to oil price changes, but as can be seen in Panel II, the sample size significantly reduced from 157 to 92. The results show an insignificant coefficient for both OR (t-value ¼ 0.44) and Non-OR (t-value ¼ 0.10). These results, however, could be derived from the small sample size. Therefore, this study performs the third analysis on the main effects using the reduced sample (N ¼ 92) with OIL variable (Panel III). If this third analysis shows the same results from Panel I (the model with OIL using the total sample size), then we may conclude that the change in oil price variable (i.e., from OIL to OILEXP) actually causes the different results. However, the results from Panel III show consistent findings with Panel II (the model with OILEXP), leading us to carefully conclude that it is likely that the insignificant

Table 2 Summary of pearson’s correlationa. Variable

OR

Non-OR

OIL

SIZE

LEV

ROA

DIV

GDP

Q OR Non-OR OIL SIZE LEV ROA DIV

0.484***

0.018 0.075

0.133 0.026 0.155

0.311*** 0.198* 0.310*** 0.085

0.428*** 0.457*** 0.229** 0.232** 0.413***

0.330*** 0.246** 0.069 0.162* 0.406*** 0.589***

0.112 0.049 0.009 0.016 0.160* 0.070 0.328***

0.106 0.017 0.100 0.817*** 0.112 0.148 0.026 0.161*

and *** represent a significance level of 0.05, 0.01, and less than 0.0001, respectively. Q represents a firm’s value performance, measured by log of Tobin’s Q; OR represents a firm’s level of operation-related corporate social responsibility (CSR) activities, measured by KLD STATS; Non-OR represents a firm’s level of non-operation-related CSR activities, measured by KLD STATS; OIL represents oil price per barrel for a given year, measured by log of annualized monthly-average price adjusted by consumer price index (CPI); SIZE represents a firm’s size, measured by log of total assets; LEV represents leverage, measured by total liabilities scaled by total assets; ROA represents return on assets, measured by net income scaled by total assets; DIV represents dividend payout ratio, and GDP represents gross domestic product, measured by chained 2005 dollars provided by U.S. Bureau of Economic Analysis. *,** a

S. Lee et al. / Tourism Management 38 (2013) 20e30 Table 4 Summary of main effects resultsa. Variable

Coefficients

t-value

Panel I. Main effects with OIL e total sample (N ¼ 157) OR 0.046 2.97** Non-OR 0.029 1.45 OIL 0.155 1.57 SIZE 0.188 4.59*** LEV 1.044 5.40*** ROA 1.852 4.51*** DIV 0.038 0.49 GDP <0.001 0.33 Panel II. Main effects with OILEXP e reduced sample (N ¼ 92) OR 0.007 0.44 Non-OR 0.003 0.10 OILEXP 0.098 1.47 SIZE 0.104 2.79** LEV 0.627 4.39*** ROA 0.557 4.13*** DIV 0.067 0.90 GDP <0.001 3.63*** Panel III. Main effects with OIL e reduced sample (N ¼ 92) OR 0.007 0.42 Non-OR 0.008 0.33 OIL <0.001 0.42 SIZE 0.105 2.85** LEV 0.591 4.11*** ROA 0.515 3.78*** DIV 0.066 0.86 GDP <0.001 1.96 *,**

p-value 0.0035 0.1503 0.1186 <0.0001 <0.0001 <0.0001 0.6256 0.7444 0.662 0.918 0.143 0.005 <0.001 <0.001 0.371 <0.001 0.673 0.744 0.677 0.004 <0.001 <0.001 0.392 0.051

***

and represent a significance level of 0.05, 0.01, and less than 0.0001, respectively. a The dependent variable is Tobin’s Q, representing a firm’s value performance, measured by log of Tobin’s Q; OR represents a firm’s level of operation-related corporate social responsibility (CSR) activities, measured by KLD STATS; Non-OR represents a firm’s level of non-operation-related CSR activities, measured by KLD STATS; OIL represents oil price per barrel for a given year, measured by log of annualized monthly-average price adjusted by consumer price index (CPI); OILEXP represents exposure to oil price changes, following Tufano’s estimation (Tufano, 1998); SIZE represents a firm’s size, measured by log of total assets; LEV represents leverage, measured by total liabilities scaled by total assets; ROA represents return on assets, measured by net income scaled by total assets; DIV represents dividend payout ratio, and GDP represents gross domestic product, measured by chained 2005 dollars provided by U.S. Bureau of Economic Analysis.

coefficients of OR and Non-OR from the model with OILEXP in Panel II are derived from the reduction in sample size, not from the variable change. Based on all these three results, we conclude that OR has a positive significant effect on firm value, supporting H1 and Non-OR does not impact firm value, failing to support H2. To test Hypothesis 3 (H3) that the positive effect of OR CSR activities on airlines’ performances is greater than Non-OR CSR activities, this study compares coefficients of OR and Non-OR variables. Although the coefficients presented in Panel I of Table 4 are not standardized beta, since measurement of the two variables of OR and Non-OR occur in the same manner with the same unit (from KLD database), a comparison of the two coefficients is applicable. They are 0.046 for OR and 0.029 for Non-OR, showing that the OR coefficient is greater than the Non-OR coefficient, providing supporting evidence for H3. However, the existence of differences between the two coefficients does not automatically indicate a statistically significant difference. Therefore, this study performs a t-test to examine the statistical difference between the two coefficients using the information of coefficients, standard errors, and sample size. The results show t-value of 0.66 and p-value of 0.5099, thus fail to reject the null hypothesis of no difference. This suggests that although there is difference in face value between the two coefficients, the difference is not statistically significant, failing to support H3. Table 5 shows results of the model that tests H4 and H5 in three panels: a negative moderating effect from OIL (or OILEXP) on the

27

relationship between OR (Non-OR) and firm performance. Results show a negatively significant coefficient (t-value ¼ 4.41; p-value <0.0001) for the OR  OIL variable, which fails to support H4 (Panel I), and an insignificant coefficient (t-value ¼ 0.01; pvalue ¼ 0.9926) for the Non-OR  OIL, inconsistent with the proposal, thus failing to support H5 (Panel I). However, this study again performs two additional analyses with a reduced sample (N ¼ 92). Panel II presents results from the model with OILEXP which are inconsistent with the results from the model with OIL: a positive coefficient for OR  OILEXP (t-value ¼ 3.78) while a negative coefficient for Non-OR  OILEXP (t-value ¼ 2.25). To see if these changes occur because of the reduction in sample size or the change in the oil price variable, this study performs the third analysis using OIL variable with the reduced sample (N ¼ 92). Results of the third analysis are qualitatively same with the results from the model using OIL with the total sample (N ¼ 157). Therefore, we carefully conclude that the different results are likely derived from the oil price variable change, not from the reduction in sample size. In such case, it is believed that results of the model with OILEXP should be Table 5 Summary of interaction effects resultsa. Variable

Coefficients

t-value

Panel I. Interaction effects with OIL e total sample (N ¼ 157) OR 0.274 5.05*** Non-OR 0.023 0.18 OIL 0.176 1.78 OR  OIL 0.062 4.41*** Non-OR  OIL <0.001 0.01 SIZE 0.150 3.93** LEV 0.523 2.47* ROA 0.712 1.78 DIV 0.045 0.63 GDP <0.001 0.20 Panel II. Interaction effects with OILEXP e reduced sample (N ¼ 92) OR 0.033 1.96* Non-OR 0.025 0.81 OILEXP 0.085 1.14 OR  OILEXP 0.097 3.78*** Non-OR  OILEXP 0.149 2.25* SIZE 0.137 3.76*** LEV 0.604 4.66*** ROA 0.425 3.42** DIV 0.055 0.82 GDP <0.001 4.29*** Panel III. Interaction effects with OIL e reduced sample (N ¼ 92) 0.066 3.50*** OR Non-OR 0.056 1.41 OIL <0.001 0.31 OR  OIL 0.001 5.43*** Non-OR  OIL 0.001 1.32 SIZE 0.118 3.37** LEV 0.600 4.89*** ROA 0.471 3.98*** DIV 0.057 0.87 GDP <0.001 2.80**

p-value <0.0001 0.8569 0.0771 <0.0001 0.9926 0.0001 0.0146 0.0775 0.5301 0.8405 0.050 0.417 0.253 <0.001 0.025 <0.001 <0.001 0.001 0.413 <0.001 <0.001 0.158 0.754 <0.001 0.186 0.001 <0.001 <0.001 0.384 0.005

*, ** and *** represent a significance level of 0.05, 0.01, and less than 0.0001, respectively. a The dependent variable is Tobin’s Q, representing a firm’s value performance, measured by log of Tobin’s Q; OR represents a firm’s level of operation-related corporate social responsibility (CSR) activities, measured by KLD STATS; Non-OR represents a firm’s level of non-operation-related CSR activities, measured by KLD STATS; OIL represents oil price per barrel for a given year, measured by log of annualized monthly-average price adjusted by consumer price index (CPI); OILEXP represents exposure to oil price changes, following Tufano’s estimation (Tufano, 1998); OR  OIL represents an interaction term of OR and OIL; Non-OR  OIL represents an interaction term of Non-OR and OIL; OR  OILEXP represents an interaction term of OR and OILEXP; Non-OR  OILEXP represents an interaction term of Non-OR and OILEXP; SIZE represents a firm’s size, measured by log of total assets; LEV represents leverage, measured by total liabilities scaled by total assets; ROA represents return on assets, measured by net income scaled by total assets; DIV represents dividend payout ratio, and GDP represents gross domestic product, measured by chained 2005 dollars provided by U.S. Bureau of Economic Analysis.

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S. Lee et al. / Tourism Management 38 (2013) 20e30

used instead of the ones with OIL.3 Therefore, results support that OILEXP positively and negatively moderates the effect of OR (H4) and Non-OR (H5), respectively, on firm performance. It should be noted that the OR coefficient in this model does not represent a main effect from OR on a firm’s performance. Rather, the coefficient represents a slope for OR when the value of the OIL variable is zero, which, economically, is not particularly logical in the current case (Friedrich, 1982). This study, in addition, examines lagging effects of OR (lagOR) and Non-OR (lagNon-OR) along with the two interaction terms for a sensitivity analysis, which reveals no main effects from lagOR and lagNon-OR, and an insignificant moderating effect of lagOR  OIL, while lagNon-OR  OIL shows a negative significant coefficient. The sensitivity analysis does not seem to add significantly to the results of the main findings of the current study. Last, the study performs a Durbin-Wu-Hausman (DWH) test to check whether or not a potential simultaneous relationship between Q and (Non-) OR exists based on some CSR literature’s support for such relationship (Lee & Park, 2009; Preston & O’Bannon, 1997). Since the test cannot be accomplished for the model, concurrently, including both independent variables (OR and NonOR), the study alternatively performs a separate DWH test for each of the two variables with Q. The DWH test fails to reject the null hypothesis that no simultaneity exists for both OR (F-value ¼ 3.83; p-value ¼ 0.92) and Non-OR (F-value ¼ 0.61; p-value ¼ 0.99) with Q. 6. Discussion The main purpose of this study is, first, to propose a new CSR dimension regarding its operation-relatedness (i.e., operationrelated [OR] and non-operation-related [Non-OR] CSR activities). Second, the study examines effects of OR and Non-OR CSR dimensions on U.S. airlines’ performances and compares the effects between OR and Non-OR CSR dimensions. Last, the study investigates the moderating effect of oil prices on the relationship between the OR (Non-OR) CSR dimension and firms’ performances. In summary, findings of this study support H1 (the positive effect of OR on firm performance), H4 (the positive moderating effect of oil prices on the relationship between the OR and firm performance), and H5 (the negative moderating effect of oil prices on the relationship between the Non-OR and firm performance), but the study fails to support the other hypotheses: H2 (positive effect of Non-OR) and H3 (greater effect of OR CSR on firm performance than Non-OR CSR). Findings of a positive effect of OR on an airline’s performance imply that the financial markets recognize an airline’s OR CSR activities as value-added practices, and consequently, incorporate the information into estimations of an airline’s value, reflected in the estimation of an airline’s Tobin’s Q. The OR CSR dimension includes an airline’s activities regarding product quality, employee relations, and corporate governance. The findings of Lee and Park (2010) for a positive effect of CSR on value’s performance may be partially consistent with the current study’s findings. However, the earlier study used an aggregated CSR measure by commingling both OR and Non-OR dimensions, thus the previous findings cannot elaborate differing effects of OR and Non-OR dimensions. In fact, the current study’s findings reveal the Non-OR dimension’s (H2) insignificant effect, which the Lee and Park findings cannot elaborate with the aggregated CSR dimension. The current finding, however, gains clearer support from the Inoue and Lee (2011)

3 This is because, as discussed earlier, the reality is that airlines extensively hedge against oil price volatility, real exposure that the airlines have to oil price fluctuations should be dependent on their hedging positions; Depending on the position, some airlines may benefit from oil price increases. Therefore, OILEXP should be a better proxy to represent airlines’ real exposure to oil price changes.

findings, that employee- and product-related CSR activities (which are part of the OR CSR dimension in the current study) have a positive impact on firms’ performances (when measured by Tobin’s Q, which is the measure of a firm’s performance in this study). The insignificant effect of the Non-OR CSR dimension on an airline’s performance may suggest that financial markets do not believe that airlines’ non-operation-related or ethical aspects of CSR investments have significance for a firm’s performance. When many major airlines incur bankruptcies and the industry is highly competitive, the markets may deem other corporate strategies that have direct implications on firms’ operations as more desirable. Moreover, the purchasing decisions of airline customers may involve higher economic values than some other industries, such as restaurants. Therefore, pricing or other service benefits may have greater influence on customers’ decisions than do perceptions of an airline’s corporate image or reputation, especially regarding the company’s investments in CSR activities that have no direct implications for its operation (e.g., Non-OR CSR dimension). However, also worthy of note is that the small sample size and/ or the lower variability of Non-OR as compared to OR, appearing in Table 1, could drive the insignificant result. More research, using bigger sample sizes to provide validity of the current findings should be in order. Also, economic relevance of the findings should be discussed. The current study’s findings suggest that a firm can increase Tobin’s Q value by 4.6% as the firm improves its OR activity level by one unit, according to KLD criteria. The magnitude of this impact may seem marginal; however, this represents only one strategy that a firm can adopt to improve its value. Firms constantly seek various strategies to enhance their values and with other positive strategies implemented together, firms can substantially increase firms’ values in a collective manner. In this regard, the positive impact of 4.6% on Tobin’s Q should be economically relevant. Moreover, a firm can certainly make a significant improvement to its operation-related CSR investments (i.e., more than one unit improvement) to accrue greater benefit for value. The current study reveals that Non-OR CSR activities deteriorate a firm’s performance as airlines’ exposure to oil prices become worse, while an opposite moderating role from exposure to oil prices affects OR CSR activities. As proposed by this study, this difference between the two CSR dimensions may exist because the market perceives OR CSR practices as more efficient for coping with an increase in airlines’ exposure to oil prices than Non-OR CSR activities because of the remote relevance of Non-OR CSR activities for a firm’s core business operations when compared to OR CSR activities. However, researchers are encouraged to investigate the issue further in more details as future research. This study contributes to the general CSR literature by proposing operation-related CSR dimensions and providing different effects between the two dimensions, closely following the Carroll’s CSR framework (1991). In addition, the study’s examination of the moderating effect of oil prices provides results that are unique for airlines, thus providing a specific contribution to the literature directed at airlines and tourism. Moreover, the current study may contribute, methodologically, especially compared to previous CSR-CFP literature concerning airlines. For example, Inoue and Lee (2011) and Lee and Park (2010) perhaps suffer from methodological issues because they used the ordinary least squares (OLS) method. To overcome these methodological issues, the current study employs a Two-Way Random Effects Model, as discussed in the Methodology Section. Findings of this study have practical implications. For airlines’ executives and managers, the results may be interesting and useful for their decision-making for CSR investments. Everything else being equal, these executives may prefer investing in OR CSR activities, for example, to improve employees’ benefits and training, create new and innovative services for better products, and create

S. Lee et al. / Tourism Management 38 (2013) 20e30

independent boards of directors for better corporate governance. However, this strategy requires cautious implementation because a long-term negative effect may arise from an intense focus on OR CSR activities with little in Non-OR CSR activities. A balanced strategy between the two, or at least maintaining a minimal level of investment in Non-OR CSR activities may be a viable consideration. In addition, airlines’ managers may closely follow their exposure to oil price changes to allow necessary adjustments to CSR investments. If desiring to maintain a similar CSR investment levels, despite increased exposure to oil price changes, managers may plan to switch from Non-OR to OR CSR activities. However, if forced to reduce the level of CSR investment during such difficult times, managers may choose to reduce Non-OR CSR investments, first, before considering decreases for OR CSR investments. Airline investors or analysts may also use this study’s findings in their investment strategies. They may incorporate the findings into investment portfolios’ development or evaluations. However, they should be aware that the findings provided by this study may be only one potential factor, and many other critical factors impact their investing decisions. It should be noted that, with the complex interrelation among many other situations and factors, solely depending on the main effect of OR CSR and oil price’s moderating effect may mislead investors and analysts. 7. Limitations and suggestions for future research The current study is not free from limitations. First, the findings are applicable to U.S. airlines and may not be generalizable to the non-U.S. airlines. A replication of the study with non-U.S. airline data in the future will enhance external validity of the current study’s findings. Second, the study suffers from a relatively small sample size. Although the current study’s sample is the best available for publicly traded U.S. airline companies, especially KLD STATS, a future study with a larger sample size should be able to provide a more confirming picture of effects of the two CSR dimensions examined in the current study. Next, some corporate strategies and phenomena, especially for airlines, may be missing from this study’s models. For example, yield management has been a crucial factor for an airline’s success, and an incorporation of that factor into the model may improve the specification. Moreover, a future study may incorporate the bankruptcy factor into the model. Last, although this study performs a sensitivity analysis for the potential lagging effect, it should be noted that such analysis was limited only to one-year lagging due to the sample size issue. References Abbott, W. F., & Monsen, R. J. (1979). On the measurement of corporate social responsibility: self-reported disclosures as a method of measuring corporate social involvement. Academy of Management Journal, 22, 501e515. Aguirregabiria, V. (2012). A dynamic oligopoly game of the US airline industry: estimation and policy experiments. Journal of Econometrics, 168(1), 156e173. Air Transport Association. (2011). U.S. airline bankruptcies and service cessationsAvailable at http://www.airlines.org/Economics/DataAnalysis/Pages/USA irlineBankruptciesServiceCessations.aspx. Alexander, G. J., & Buchholz, R. A. (1978). Corporate social responsibility and stock market performance. Academy of Management Journal, 21(3), 479e486. Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2005). Statistics for business and economics. Mason: Thompson South Western. Aragó n-Correa, J. A., Hurtado-Torres, N., Sharma, S., & Garcí a-Morales, V. J. (2008). Environmental strategy and performance in small firms: a resource-based perspective. Journal of Environmental Management, 86(1), 88e103. Aupperle, K., Carroll, A., & Hatfield, J. (1985). An empirical examination of the relationship between corporate social responsibility and profitability. Academy of Management Journal, 28, 446e463. Barkin, T., Hertzell, S., & Young, S. (1995). Facing low-cost competitors: Lessons from U.S. Airlines. The McKinsey Quarterly. Retrieved 11.04.12, from. http://www. mckinseyquarterly.com/Facing_lowcost_competitors_Lessons_from_US_ Airlines_117. Belkaoui, A. (1976). The impact of the disclosure of the environmental effects of organizational behavior on the market. Financial Management, 5, 26e31.

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Seoki Lee is Associate Professor of the School of Hospitality Management at the Pennsylvania State University. He received his M.S. in both Hospitality Business and Accounting from Michigan State University, and Ph.D. in Hospitality Management from the Pennsylvania State University. His research mainly focuses on hospitality and tourism issues in the financial and strategic management, and revenue management (RM) context, including following topics: a) corporate social responsibility, b) internationalization, c) diversification, and d) customers’ perceptions about RM.

Amit Sharma is Associate Professor of Finance in the School of Hospitality Management at the Pennsylvania State University since 2006. Earlier he was an Assistant Professor at Iowa State University (2002e06). His education includes: Ph.D., Virginia Tech. (2002); Masters, Institut de Management Hotelier International (IMHI) (France); Higher National Diploma in Hospitality Management, University of Salford (England); Bachelor in Economics (Honors), University of Delhi (India). Dr. His research focuses on Decision-Making and Cost-Benefit Analysis of hospitality, particularly in context of food safety, nutrition, and food security decisions. His research also incorporates financial literacy to improve hospitality decisions.

Kwanglim Seo is Assistant Professor of the School of Travel Industry Management at the University of Hawaii, Manoa. He received his M.S. in International Hospitality Management from Strathclyde University, and Ph.D. in Hospitality Management from the Pennsylvania State University. His research looks into corporate investment decision-making in the Hospitality and Tourism industries, which describes a process of investing driven by managerial biases. He also takes an interest in areas such as capital structure, corporate governance and ownership structure, and franchising.