Chief Executive Officers and the Value of US Airlines: The Moderating Effect of Carrier Type

Abstract

The purpose of this research is to explore the moderating effect of carrier type on the effects of upper echelon attributes. The study subject is the position of chief executive officer (CEO). The dependent variable is market-to-book value; the independent variables are CEO age, tenure, and educational background. The moderator is LCC. For the data collection, this study used COMPUSTAT and EXECUCOMP using the standard industry classification code 4512; the data are collected from Annual 10K and Linkedin. There are 15 airlines examined, and the study period is from 1999–2019; in total, 210 observations were used for the data analysis. Multiple regression analysis was the main instrument used to test the research hypotheses. The results indicate that both older CEOs in LCCs performed better in the market.

1. Introduction

The airline industry is very dynamic, and the survival of airlines is threatened by a variety of external factors, including political and economic factors, natural disasters, and epidemics. In fact, the stock price of United Airlines dropped from $87 to $36 per share due to the COVID-19 pandemic in 2020 [1]. The stock price of Southwest Airlines was also damaged ($55 to $36) by the global disaster in 2020 [2]. Such market conditions might lead shareholders to search for appropriate people who can maximize their stock value, because agents are expected and authorized to produce sound business conditions [3]. The chief executive officer (CEO) often becomes a representative person who is expected to serve the interests of stockholders; many scholars have researched the impact of the CEO on management outcomes [3,4,5,6,7,8,9]. This could also be applied to the case of airline business. For instance, Herb Kelleher, the former top manager of Southwest Airlines, and David Neeleman, the former top manager of JetBlue Airways, accomplished decent business growth and remarkable market performance during their times in charge [10,11]. This study also uses market-to-book value as the explained variable, which serves as an indicator for a firm’s market evaluations [12,13,14,15]. In short, this study aims to explore the effects of CEOs on the market performance of airlines.

Upper echelon theory—which alleges that the future of business is influenced by top managers—is the theoretical foundation of this research, because it aims to document the characteristics of top managers [3]. Upper echelon theory, as proposed by Hambrick and Mason [16] in 1984, explains the characteristics of top managers, who are considered to be powerful people in organizations. According to Hambrick [17], top managers cannot utilize all available information because their cognition capability is limited; their business outcome is therefore affected by their limited field of vision and selective perception. However, it is difficult to use individuals’ psychological mechanisms in research due to issues involving the measurement and accessibility of the data [16]. As a solution, upper echelon theory recommends observable attributes such as demographic information and working status in an organization, which allows researchers to predict organizational outcomes. In terms of the argument made by Hambrick and Mason [16], this research selects three observable elements: physical age, tenure, and educational background. In addition, Hambrick and Mason [16] alleged that age, tenure, and education are the basic observable attributes of CEOs from which their psychological mechanism for decision making may be inferred. Fertile research has also used age [5,6,18], tenure [4,19,20], and education [7,8,21] to investigate CEOs’ behavior in the extant literature. Thus, this study selects age, tenure, and education as the explanatory variables. In addition, this study uses market-to-book value to measure the value of airlines, since many studies contend that market-to-book value is an adequate piece to measure the market performance of business [14,15,22,23].

The LCC (LCC) business is another main aspect of this study. LCCs concentrate on offering cheap prices for customers, and they are therefore more focused on how to minimize cost and auxiliary service rather than risk taking [24,25,26,27,28]. This indicates that the LCC business is different from the full-service carrier business; this difference might require distinct leaders. Namely, given the differences between LCC businesses and full-service carrier businesses, it is worth aiming to identify the traits that make CEOs more suited for leading an LCC business. Indeed, Seo, Moon, and Lee [29] investigated LCC strategy and found that the strategic execution of LCCs needs to be different from that of full-service carriers, thus demonstrating a moderating effect of LCC. Further, the moderating effect of LCC regarding upper echelon attributes has scarcely been explored by scholars. Hence, this study adopts carrier type as a moderator to examine the relationship between CEOs’ observable attributes and the market performance of airlines.

In sum, the purpose of this study is to investigate the moderating effect of carrier type in the domain of the airline business to investigate the association between CEOs’ observable attributes and market performance. This study is expected to contribute to the literature by demonstrating a moderating effect of carrier type in the area of top management, therefore filling a research gap. The results may also provide airline businesses with information they can use to hire more adequate CEOs.

2. Review of Literature and Hypotheses Development

2.1. Market-to-Book Value

Market-to-book value is an indicator of business performance; it reflects the market evaluation of a business [12,13,22]. Market value includes more diverse elements: reputation, potential business growth rate, market reputation, brand value, quality of human capital, risk, and amount of assets, whereas book value includes visible aspects of a business [30,31,32]. Hence, scholars have contended that market value reflects both the past and future of a business, while profitability in financial statements concentrates on past short-term performance [14,15,22]. Therefore, numerous studies have selected market-to-book value as the outcome variable. For instance, Liang and Yao [33] investigated the antecedents of market-to-book value by examining electronic firms. Dita and Murtaqi [23] scrutinized corporations in the domain of the consumer goods business, while using market-to-book value as the main attribute. In another study in the banking sector, Simoens and Vander Vennet [32] chose market-to-book value as the dependent variable. Tekin [34] examined the determinants of market-to-book value in the real estate investment trust business. The extant literature has also used market-to-book value as the explained attribute in the context of airline business [23,29]. To sum up, market-to-book value has been used in many prior studies as an essential element to measure business performance. Therefore, the present work adopts market-to-book value as the dependent variable.

2.2. Low-Cost Carriers (LCC)

LCC focus on providing the core functions of the airline business rather than lavish service which results in more costs to airlines [25,28,35]. Scholars have also stated that the main concern of LCC is how to minimize costs and offer consumers the cheapest prices; customers have lower expectations for service quality from LCC [36,37,38]. In addition, Brüggen and Klose [39], Klophaus, Conrady, and Fichert [40], and Budd et al. [41] alluded that LCC use a single type of aircraft which has a two-engine and narrow body, so that it not only saves costs for maintenance and training for engineers, but purchasing and leasing equipment also costs less. Therefore, the strategic implementation of LCC is likely to be simpler, as LCC do not need to take risks by pursuing new service strategies [27,28,29]. LCC also adopt point-to-point systems rather than hub-and-spoke systems, which leads to minimizing competition with other airlines at hub airports; the simplified service minimizes the employee training burden [24,26,42,43,44]. Further, LCC are more likely to operate in domestic areas than international areas; compared to full-service carriers, their business model is less likely to be influenced by external conditions, such as political conflict, infectious diseases, oil prices, economic conditions, and natural disasters [37,45,46,47,48].

All in all, LCC are likely to conduct business as defenders who focus more on protecting their market rather than prospectors who pursue state-of-the-art business items and pioneer new markets, because the minimization of lavish service and the focus on relatively short-distance operations lead LCC to become less risky for management [26,29,42,47,49,50]. This indicates that conservative top managers could be more appropriate for LCC. Indeed, Seo et al. [29] and Florido-Benítez [51] documented that pursuing efficient and parsimonious resource allocation in the LCC business plays a significant role in increasing market value.

2.3. Upper Echelon Attributes and Chief Executive Officers

Upper echelon theory argues that organizational performance is affected by top managers, and that top managers are influential individuals because they are authorized to make decisions regarding organizational directions which are based on value, cognitive bases, and selective perception [17]. The theory also outlines that individuals are not always rational; instead, individual decision making is dependent on filtered information [5,52]. Hambrick and Mason [16] claim that observable demographic information can be used to anticipate the direction of top managers’ decision making, on the basis that it is difficult to measure the psychological mechanisms of top managers for use in research. Prior studies have also shown that the chief executive officer (CEO) is authorized to make decisions regarding the direction of the business and is responsible for the outcome of business; many studies have considered the CEO as the research subject with upper echelon theory ([4,6,8,17]. The first observable domain the study demonstrates among CEOs’ attributes is age. Individuals experience weakened mental energy as they age; scholars have alleged that older top managers are therefore more cautious in their decision making. Prior studies have also shown that older top managers are more conventional. For instance, Herrmann and Datta [18] found that older top managers are more cautious in their strategic execution. Serfling [6] found that older CEOs value more stable organizational conditions. Barker and Muller (2002) demonstrated that older top managers showed decreased investment in research and development. Similarly, Andreou, Louca, and Petrou [53] found that younger CEOs showed more variations in stock market performance. Considering the characteristics of LCC pursuing fewer strategic risks, it can rationally be anticipated that cautious top managers are more appropriate for enhancing the market performance of LCC because indiscriminate business expansion is more likely to damage the market value of airlines. Therefore, the following research hypothesis is proposed:

Hypothesis 1 (H1): 

Low-cost carriers significantly moderate the association between a CEO’s age and market performance.

The second CEO variable to be addressed is tenure, which is the working period of a given top manager in a business organization [3]. Hambrick and Mason [16] claim that longer-tenured top managers become more conservative because they place increased value on protecting their achievements and reputation. Boeker [4] scrutinized top managers in the semi-conductor industry and found that CEO tenure is negatively associated with changes in organizational strategy. Zhou, Dutta, and Zhu [20] revealed that longer-tenured top managers pursue the execution of safer strategies. Lee and Moon [5] conducted a study examining airline CEOs and reported that longer-tenured CEOs generally avoid strategic risk taking. Chen, Hsu, and Huang [19] showed that longer-tenured top managers are less dependent on debt financing, which could increase the likelihood of business uncertainty. Regarding defender characteristics of LCC—short-distance operations, reducing costly service, and avoiding unnecessary competition at hub airports [24,27,37,43]—the conservative management style of longer-tenured top managers is more likely to be appropriate for better market reputation of the business. Given this literature review, this study proposes the following hypothesis:

Hypothesis 2 (H2): 

Low-cost carriers significantly moderate the association between a CEO’s tenure and market performance.

The third element of this research is educational background, because it is linked with intellectual capability [3]. Prior literature has shown that top managers with Master of Business Administration (MBA) degrees are conservative and number-centered rather than intuitive, because they consider textbook knowledge, short-term performance, and managing visible assets to be worthwhile pursuits [8,54,55]. Further, previous studies have argued that CEOs possessing Juris Doctor (JD) degrees consider lawsuits in their decision making; as such, their decision making is likely to become more cautious [56,57]. Both academic degrees are awarded by master level programs, and they are each related to practical knowledge in social science fields. Indeed, previous research has categorized JD and MBA degrees as comprising a practical master’s degree variable [5,56]. Empirical studies have shown a significant link between educational background and business outcome [7,9,21,58]. In particular, in a study examining Chinese CEOs, Sun et al. [59] reported that CEOs with MBAs pursued strategic decisions in a more parsimonious manner. King, Srivastav, and Williams [60] showed that CEOs with MBAs performed better in the domain of banking, which prioritizes stable business conditions. Browsing the extant literature finds that top managers who have the educational background of an MBA or a JD place increased emphasis on stable organizational performance. Such a characteristic could become more adequate for administering LCC business because parsimonious managers are likely to be the right person for LCC, considering the feature of avoiding operational complexity in LCC. Thus, this research proposes the last hypothesis as follows:

Hypothesis 3 (H3): 

Low-cost carriers significantly moderate the association between a CEO’s educational background and market performance.

3. Method

3.1. Data Collection

The sample in this research consists of airlines that are publicly traded on the stock market: American Stock Exchange (AMEX), National Association of Securities Dealers Automated Quotations (NASDAQ), and New York Stock Exchange (NYSE). This study collected the data using various information sources. Annual report 10-K was used as the source for business strategy information. COMPUSTAT was used as the source for the financial information. Information on executive age, tenure, share ownership, and the value of stock options was collected using EXECUCOMP. The standard industry classification code 4512 was used to attain the airline industry information. Linkedin (http://www.linkedin.com (accessed on 4 December 2021) was the information source for the CEOs’ educational background. The data is panel data which consists of multiple firms and years. The study period was 1999–2019 and the number of airlines was 15. Table 1 lists the airlines considered in this study. The data appeared as an unbalanced panel, which indicates that all airline information is not fully offered during the study period.

3.2. Illustration of Variables

Table 2 presents variable information. MTB is the dependent variable, while AGE, TEN, and EDU are the explanatory elements. LCC is the moderating attribute in this research. Moreover, this study adopted SHO and STO as the control variables because CEOs’ decision making is likely to be influenced by the compensation package. ROA, SIZE, and DEBT were controlled because the corporation’s financial performance, resource amount, and debt dependency all potentially affect the market evaluation.

3.3. Data Analysis

For the initial data analysis, this study computed the mean, standard deviation, minimum, and maximum values. Then, this study conducted a correlation matrix analysis to examine the correlations between variables. Next, this research used multiple regression analysis for hypothesis testing. Various econometric instruments were used to ensure consistency. This study initially used the two-way fixed effect model to minimize omitted variable bias in the panel data estimation [61,62]. The two-way fixed effects model incorporates multiple dummy variables into the regression model to control both firm and year effect [62,63,64]. Additionally, this research implemented feasible general least square regression analysis, which minimizes the bias caused by heteroscedasticity and autocorrelation in panel data analysis [62,63]. To test the moderating effect, this study created variables by multiplying LCC to AGE, TEN, and EDU. The regression equation is as follows:

MTB_it = β0 + β1 ROA_it + β2 SIZE_it + β3 DEBT_it + β4 SHO_it + β5 STO_it + β6 AGE_it + β7 TEN_it + β8 EDU_it + β9 LCC_it + β10 AGE × LCC_it + β11AGE × LCC_it + β12 AGE × LCC_it+ ε_it(1)

where t: t_th year, i: i_th firm, ε: residual. MTB is market-to-book value, AGE is CEO’s age, TEN is CEO’s tenure, EDU is CEO’s educational background. SHO stands for CEO’s value of share, STO denotes CEO’s value of stock option, ROA is return on assets, SIZE is total assets, DEBT is debt to total assets, LCC is low-cost carrier.

4. Results

4.1. Descriptive Statistics and Correlation Matrix

Table 3 lists the results of the descriptive statistics. The mean value of MTB is 0.57 and the standard deviation is 0.56. For AGE, the mean value and standard deviation are 53.49 and 6.29, respectively. The mean value of TEN is 7.14 and the standard deviation of TEN is 8.35. Next, 56% of CEOs in the sample were female. Moreover, Table 3 presents the information on SHO (Mean = 967.25 SD = 1575.43) and STO (Mean = 320.40 SD = 1018.20), as well as ROA (Mean = 0.01 SD = 0.12), SIZE (13,793.98 SD = 15,989.26), and DEBT (Mean = 0.78 SD = 0.24). Lastly, 29% of observations are LCC.

Table 4 presents the correlation matrix. MTB positively correlates with AGE (r = 0.232), TEN (r = 0.144), SHO (r = 0.336), ROA (r = 0.393), and LCC (r = 0.416). However, MTB negatively correlates with SIZE (r = −0.243) and DEBT (r = 0.586). AGE positively correlates with TEN (r = 0.366) and SIZE (r = 0.199). TEN also positively correlates with EDU (r = 0.193) and SHO (r = 0.238), whereas TEN negatively correlates with SIZE (r = −0.224) and DEBT (r = −0.279). Furthermore, ROA negatively correlates with DEBT (r = −0.628). LCC also negatively correlates with EDU (r = −0.160), STO (r = −0.144), SIZE (r = −0.265), and DEBT (r = −0.382), whereas LCC positively correlates with SHO (r = 0.344). In addition, SIZE positively correlates with DEBT (r = 0.348).

4.2. Results of Hypotheses Testing

Table 5 presents the results of the regression analysis. Both models are significant, given the F-test and Wald χ² test (p < 0.05) results. SHO is positively associated with MTB (β = 0.001, p < 0.05). MTB is negatively influenced by AGE (β = −0.017, p < 0.05), whereas AGE × LCC (β = 0.051, p < 0.05) exerted a positive effect on MTB. It indicates that older CEOs in LCC performed better. LCC (β = 1.491, p < 0.05) positively affects MTB. The results appeared consistent in two regression results in terms of direction and significance. Thus, H1 is supported by the results of the multiple linear regression analysis.

This study additionally performed median split for AGE (Median = 54) to inspect the direction of the moderating effect, because age appeared to be the only significant attribute to account for MTB [65]. Further, this study calculated the mean value of Q using the criteria of both LCC or FSC and AGE (young vs. old). Table 6 presents the mean values for the additional examination of the moderating effect. The mean value of MTB in the case of older CEOs in LCC is 1.30, whereas the mean value of MTB in the case of young CEOs in FSCs is 0.49. In addition, Figure 1 graphically depicts the results of the moderating effect of LCC on airline value in terms of the top managers’ age. Therefore, only H1 is supported, and it is ensured that older CEOs are the right person for the LCC business model.

5. Discussion

This study used upper echelon theory as a theoretical underpinning to determine the market performance of airlines. Although prior research explored the effect of CEO attributes on strategic decisions, strategic risk taking, and corporate social responsibility [5,66], how CEOs could enhance the market value of airlines has been sparsely explored. Given the research gap, this study investigates the moderating effect of LCC to determine the relationship between market performance and CEOs’ observable attributes: age, tenure, and educational background. The results suggest that older CEOs perform better when they lead LCC, meaning that older CEOs could be the right people to protect the wealth of shareholders in LCC because they have the talent for stable management [6,18]. Namely, older CEOs could be the right people to manage LCC because their business models require avoiding entering into risky conditions to improve their market values. Moreover, LCC showed superior market performance in the results. This might be explained by the minimized risk taking in business by decreasing lavish service in the business model. However, the results suggest that tenure and background of education appear as uninfluential attributes on market-to-book value, depending on the carrier type of the airline.

Control variables, including share ownership, firm size, and financial leverage, also had significant effects on market performance. Share ownership is positively related to market value, meaning that CEOs owning a larger share showed better market performance. Next, firm size measured by total assets is negatively associated with market performance, and it could be inferred that reckless business expansion and asset acquisition, such as purchasing new aircraft and maintaining old aircraft, might result in poor market assessments. In particular, firm size of airlines is highly affected by the aircraft; large firm size implies that airlines possess old versions of aircraft, which causes costs for the maintenance of a lowering market reputation. The results also showed that the market-to-book value of airlines is negatively affected by financial leverage. This means that debt financing could represent a bad signal from the perspective of both current and potential stockholders, as it could increase the cost of debt and market risk in regard to bankruptcy.

6. Conclusions

6.1. Theoretical Contribution

This study has theoretically contributed to the literature by scrutinizing the moderating effect of LCC to capture the relationship between the market performance of airlines and CEOs’ observable characteristics. The results show a significant moderating effect of carrier type between attributes of CEOs and market-to-book value, which represents the major contribution of this research. To elaborate, while prior airline research has mainly focused on the simple linear relationship between CEOs’ demographic elements and their decision making in the domain of the airline business, it has been hardly explored by scholars [5,66]. Namely, there has been little exploration of any potential moderating effects of carrier type by considering attributes of top managers and the market performance of airlines. By documenting the significant moderating effect of LCC, this study expands upon the understanding of the characteristics of upper echelon attributes. Further, this research could validate the findings of Seo et al. [29], in that the moderating effect of carrier type (LCC vs. FSC) appeared significant regarding the attributes derived from upper echelon theory. This suggests that the findings of this research externally validate the results of Seo et al. [29], in that the strategies of LCC need to be different from those used in FSCs. By clarifying the relationship between CEOs’ observable attributes, carrier type, and market-to-book value, this research sheds light on the upper echelon literature by demonstrating its accountability.

6.2. Practical Implication

This research has certain practical implications. Above all, the information of this research could be used for the recruitment of CEOs in the airline business. That is, LCC could select their CEOs based on the information presented herein. Specifically, LCC might be able to consider older top managers because such CEOs are expected to enhance corporate market value through their cautious mode for strategic decision making. Conversely, such people might not be as well-suited to serving as CEOs at FSCs. This is because FSCs might consider more complicated elements than LCC, and they may require riskier decision making. The results of this research could also provide signals for both current and potential investors in airlines. In detail, potential or present stockholders could use this information to manage their stock portfolios. Specifically, if a certain LCC’s CEO is older, then investors might be able to purchase more stocks in that LCC. The information for stock value possessed by the CEO would become the signal for more investment in stock. To be specific, if certain CEOs own more stock, the market value of airlines is more likely to increase because the CEOs may be more responsible for its management. Further, investors could use this information because the market value of LCC is better than FSC.

6.3. Limitations

This study has certain limitations. First, the sampled corporations are constrained to America-based airlines due to data availability. Future research needs to validate the results of this study by examining airlines in other market areas. In addition, this study only examined three attributes (age, tenure, and educational back ground) to attain the results. Future research should consider either more diverse psychological mechanisms or observable attributes of top managers to further elucidate the market performance of airlines. Furthermore, future research might be able to consider different domains which contain a low-price product market sector to determine further top managers’ characteristics, in order to ensure the accountability of observable attributes. Such efforts could make the literature of upper echelon literature more fruitful.

Author Contributions

Formal analysis, J.S.; Writing—original draft, J.M.; Writing—review & editing, W.S.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figure and Tables

Figure 1. Moderating effect of low-cost carrier for age.

Table 1

List of sampled airlines.

Airline Name	Carrier Type	Current Company Status
American Airline Group Inc.	Full-service carrier	Trading at stock market
Alaska Air Group Inc.	Full-service carrier	Trading at stock market
Northwest Airlines Inc.	Full-service carrier	Merged with Delta
Delta Airlines Inc.	Full-service carrier	Trading at stock market
Southwest Airlines	Low-cost carrier	Trading at stock market
US Airway Group Inc.	Full-service carrier	Merged with American airline
Mesa Air Group Inc.	Full-service carrier	Trading at stock market
United Continental Holding Inc.	Full-service carrier	Trading at stock market
Frontier Airlines Holding	Low-cost carrier	Trading at stock market
Midwest Air Group Inc.	Full-service carrier	Merged with Republic airway holding
Skywest Inc.	Full-service carrier	Trading at stock market
FLYi Inc.	Full-service carrier	Bankrupt in 2005
Airtran Holding Inc.	Full-service carrier	Merged with Southwest airlines
JetBlue Airways Corp	Low-cost carrier	Trading at stock market
Allegiant Travel Co	Low-cost carrier	Trading at stock market

Table 2

Variable illustration.

Variable	Description	Data Source	Measure
MTB	Market-to-book value	Compustat	Market value total/Book value total
AGE	CEO’s age	Execucomp	Physical age of CEO
TEN	CEO’s tenure	Execucomp	Working period as CEO (Unit: Year)
EDU	CEO’s education	Linkedin	CEO academic terminal degree (0 = Others, 1 = MBA or JD)
SHO	CEO’s value of share	Execucomp	Value of share possessed by CEO (Unit: Thousand: US dollars)
STO	CEO’s value of stock option	Execucomp	Value of stock option possessed by CEO (Unit: Thousand: US dollars)
ROA	Return on assets	Compustat	Net income/Total assets
SIZE	Firm size	Compustat	Total assets (Unit: Million: US dollars)
DEBT	Financial leverage	Compustat	Total debt/Total assets
LCC	Low-cost carrier	Compustat	0 = non- low-cost carrier, 1 = low cost carrier

Note: MBA stands for master of business administration, JD denotes juris doctor.

Table 3

Descriptive statistics.

Variable	Mean	SD	Minimum	Maximum
MTB	0.57	0.56	0	2.53
AGE	53.49	6.29	37	69
TEN	7.14	8.35	1	41
EDU	0.56	0.49	0	1
SHO	967.25	1575.43	0	10,783.28
STO	320.40	1018.20	0	10,377.85
ROA	0.01	0.12	−1.09	0.90
SIZE	13,793.98	15,989.26	170.08	64,532.00
DEBT	0.78	0.24	0.28	2.32
LCC	0.29	0.45	0	1

Note: SD stands for standard deviation. MTB is market-to-book value, AGE is CEO’s age, TEN is CEO’s tenure, EDU is CEO’s educational background. SHO stands for CEO’s value of share, STO denotes CEO’s value of stock option, ROA is return on assets, SIZE is total assets, DEBT is debt to total assets, LCC is low-cost carrier.

Table 4

Correlation matrix.

Variable	1	2	3	4	5	6	7	8	9
1.MTB	1.
2.AGE	232 *	1
3.TEN	0.144 *	0.366 *	1
4.EDU	0.012	0.131	0.193 *	1
5.SHO	0.336 *	0.038	0.238 *	0.083	1
6.STO	−0.057	0.110	−0.032	−0.031	−0.039	1
7.ROA	0.393 *	0.092	0.114	0.061	0.114	0.346 *	1
8.SIZE	−0.243 *	0.199 *	−0.224 *	0.109	−0.104	0.371 *	0.018	1
9.DEBT	−0.586 *	−0.011	−0.279 *	0.005	−0.144 *	0.047	−0.628 *	0.348 *	1
10.LCC	0.416 *	0.011	−0.039	−0.160 *	0.344 *	−0.144 *	0.169 *	−0.265 *	−0.382 *

Note: * p < 0.05. MTB is market-to-book value, AGE is CEO’s age, TEN is CEO’s tenure, EDU is CEO’s educational background. SHO stands for CEO’s value of share, STO denotes CEO’s value of stock option, ROA is return on assets, SIZE is total assets, DEBT is debt to total assets, LCC is low-cost carrier.

Table 5

Results of regression analysis (n = 210).

Variable	Model1Two-Way FEβ(t-Stat)	Model2FGLSβ(Wald)	Results
InterceptROASIZEDEBTSHOSTOAGETENEDULCCAGE × LCCTEN × LCCEDU × LCCF-valueWald χ2R2Adjusted R2	1.477(2.85) *0.313(0.99)−0.371(−0.89)0.171(−1.61)0.001(3.24) *−0.001(−0.11)−0.017(−2.02) *−0.004(−0.69)−0.001(0.01)1.491(4.66) *0.055(3.23) *−0.021(−0.93)0.204(1.18)10.76 *-0.74690.6775	1.477(3.22) *0.313(1.12)0.001(1.01)−0.372(−1.82)0.001(3.67) *−0.001(−0.12)−0.017(−2.29) *−0.004(−0.69)−0.001(0.01)1.491(5.27) *0.055(3.65) *−0.021(−1.06)0.207(1.34)-619.83 *--	H1 SupportedH2 Not supported H3 Not supported

Note: * p < 0.05 Dependent variable: MTB; OLS stands for ordinary least square, FE stands for fixed effect, FGLS stands for feasible generalized least square. MTB is market-to-book value, AGE is CEO’s age, TEN is CEO’s tenure, EDU is CEO’s educational background. SHO stands for CEO’s value of share, STO denotes CEO’s value of stock option, ROA is return on assets, SIZE is total assets, DEBT is debt to total assets, LCC is low-cost carrier.

Table 6

Mean values of MTB in moderating test.

	AGE
	Young	Old
LCC	0.48	1.30
FSC	0.49	0.54

Note: LCC stands for low-cost carrier, FSC stands for full-service carrier, Median split value: AGE is 54.

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