Corporate Governance Mechanisms and Exploration-Exploitation: The
role of Incentives and Control
Henrique Costa Pereira Gomes Pereira S3465144
MSc. Strategic Innovation Management University of Groningen
Supervisor: Prof. Dr. Jana Oehmichen Co-assessor: Prof. Dr. Pedro de Faria
ABSTRACT
Researches on the innovative orientation of firms are blossoming. However, the complete understanding of antecedents and mechanisms that impact both exploration and exploitation is still imprecise. We build on the agency theory to achieve an in-depth analysis on how the mechanisms that are used to align shareholders and managerial interests affect the exploratory or exploitative orientation of a firm. This study aims to investigate the role of incentives and the control on the amount of relative exploration pursued by the company. To analyse the role of incentives in our dependent variable, we used annual cash bonuses, stocks, and option awards as independent variables. The goal was to use incentives that have different objectives in terms of length. While cash bonus act on a more short-term basis, both stock and options may work towards long-term goals of the company. In order to scrutinise the role of control on exploration/exploitation orientation, we assess the number of independent directors on the board and remuneration committee as a moderating variable. Our findings suggest that stock awards may not have a long-term emphasis as expected, increasing managerial risk aversion in certain occasions. Moreover, independent directors have an important role in the monitoring and delineating the compensation plan of an executive.
3
Corporate governance mechanisms and exploration-exploitation: the role of incentives and control
INTRODUCTION
Governance mechanisms were found to accentuate the problems when conflicting goals between the agent and the principal exist (Eisenhardt, 1989). Further, Jensen & Meckling (1976) proposed a theory of a company based on the conflicts of interest between shareholders and corporate managers. Agency problems exist when the decisions that are taken by the agent does not affect both his welfare and the welfare of the principal (Brennan, 1995). Moral hazards are related to the attitude of a manager acting on private interest, feeding his private desires, without a consideration on what is the best option for the company as a whole (Jensen & Meckling, 1976). Managerial risk aversions can arise due to the constraints of the manager towards the firm. While shareholders may diversify their portfolio, managers have a deep, direct relation with the firm’s performance (Denis, Denis, & Sarin, 1997). Corporate governance mechanisms have the purpose of achieving an alignment between principal and manager.
5
RQ: How corporate governance mechanisms (incentives and control) impact the innovative orientation of the firm.
This thesis contributes to research by extending the agency theory, testing the impact of corporate governance mechanisms on the innovative orientation of a firm. We also contribute to organisational learning management research by considering how these processes impact the orientation of a firm, along with certain measures that can affect exploration and exploitation differently.
CONCEPTUAL BACKGROUND AND HYPOTHESIS
Exploration-exploitation orientation
element for a firm’s long-term success, the support for radical projects is not frequently given (McDermott, 2002). Resources within a firm are scarce, forcing managers to make resource allocation choices towards projects. Exploitation-oriented activities aim their attention on improving existing processes and responding to existing demand, which will lead to short-term gains. Exploration activities are based on experimentation and the search for novel knowledge, which is costly and may lead to short-term losses. Although exploratory innovations may hamper existing capabilities and products, it will increase the chance of long-term survival and prosperity (Benner & Tushman, 2003). Consequently, and due to resource scarcity, managers have to make crucial decisions in delineating the path of the firm.
Corporate governance mechanisms and exploration-exploitation orientation
Organizations use various corporate governance mechanisms in order to guarantee efficient decision making (Cuervo, 2002). These mechanisms allow for an improved alignment between managers and owners (Sanchez-Marin, Lozano-Reina, Baixauli-Soler, & Lucas-Perez, 2017). They can be drawn as policies and procedures that the firm uses to protect and control the interests of internal and external stakeholders. However, these mechanisms can have different impacts on the innovative orientation of a firm. Therefore, we examine two mechanisms: (1) incentive plans and (2) monitoring.
7
Garcia & Calantone (2002) analysed that returns from exploratory innovation were more risky and distant in time (typical research endeavours take 12-36 months). In contrast, exploitative activities were more proximate (approximately 3-9 months). Companies have to balance the trade-offs between the outcomes of these activities. The trade-off between short-term productivity and long-term innovation has to be managed by analysing the opportunities derived from the search of new knowledge and existing knowledge that match current needs (March, 1991). By allocating resources to the refinement of current technologies and competencies rather than searching for new knowledge and capabilities, firms may be risking the future health of the business (Holmqvist Mikael, 2004). Contrary to shareholders, managers are often risk-averse, something that is related with their direct linear relationship with firm performance (E. Fama, 1980). The annual results of the firm are often indicators of managerial success. As such, performance-based compensation may be used to award these outcomes, and this may materialise in several forms. Firstly, we will evaluate the effects that annual cash bonuses have on the innovative activities of a firm. Annual cash bonuses are represented as a term incentive in this study. Cash awards are commonly rewarded for achieving a short-term business strategy. Revenue growth, return on capital or the maximizing of profit are often metrics used to measure these goals. Ultimately, when performance is measured in short periods of time with the indicators being primarily financial, the annual cash bonus will encourage managers to seek exploitative activities, due to their ability to produce short-term results.
9
company in the future. In this study, we analysed two LTIP tools, namely the stock and option awards. By conceding a long-term stimulant, managers will probably change the actions derived from their involvement in the future path of the firm, investing more in innovations that will strive in the future and ensure a strong valuation of the company in the long-term.
H2: An increase on the executive´s annual stock and option awards will lead to a more exploratory (versus exploitative) orientation of the firm.
when monitoring and deciding (Jiraporn & Nimmanunta, 2017). We analysed the number of independent directors on the board and remuneration committee because these are responsible for monitoring the executive management and for the oversight of the remuneration policy of the correspondents, including the guidelines on the executive incentive payment. Board independence may reduce moral hazard problems and induce managers to have less risk-averse behaviours towards long-term projects. Furthermore, independent directors may also facilitate contact to crucial resources for innovative activities, thus also being a source of heterogeneity to the firm (Castro, De La Concha, Gravel, & Periñan, 2009. Jenwittayaroje & Jiraporn (2017) found that a higher proportion of independent directors reduce managerial myopia, leading to higher investments on exploratory innovation.
H3: Incentives are more positively associated with exploratory (versus exploitative) orientation of the firm when the number of independent directors on the board and remuneration committee increase.
Conceptual Model
11
METHODOLOGY
Sample
For the conducted study, we used a sample that collects information from three datasets that were merged together. We acquired executive compensation and ownership data from Compustat® ExecuComp database, which collects data about compensation (McGuire, James, & Papadopoulos, 2016). Towards ambidexterity and control variables, we followed the paper of McKenny, Aguinis, Short, & Anglin (2016), which includes 254 firms in four technology industries (SICs: 2834, 7370, 7372 and 7373). Additionally, we retrieved board characteristics information from BoardEx, which consolidates information concerning board of directors and senior management. BoardEx contains profiles of more than 1 million leaders of firms and the data is actualized regularly. After the datasets were merged, our final sample accounted 198 companies with a total number of observations of 1366. The time frame considered in the study was from 2004 until 2016.
Measures
Identified markers from a computer-aided query are certified by human coders to assess their validity. Annual reports are the main textual data source as they contain detailed information of firms. We used the search dictionary used in March (1991) and applied it to Uotila (2012), using words that were directly related with exploratory and exploitative activities. The counts for both orientations were extracted based on the markers recognised in the dictionary. We followed the study of Heyden, Oehmichen, Nichting, & Volberda (2015) and adjusted the counts by the total word length of the textual sources. The scores for relative adjusted exploration were obtained by dividing the adjusted counts of references to both exploration and exploitation (i.e., DVij=exploration countij / [exploration_countij+exploitation countij]). The scores can alternate from 0 to 1, with higher scores (closer to 1) being related with a higher level of exploratory orientation of a firm while lower levels were associated with a focus on exploitation.
Independent variables. The first independent variable used was the annual cash bonus that was received by all the executives in the firm. As a result of the relative exploration adjusted being suited for a company level value (for each company corresponds one value per year), the independent variable was transformed from executive level to company level. Therefore, an addition of the entire annual cash bonuses received by the executives was made. Both stock and options awards were calculated in a similar way. We aggregated all the stock and options awarded to executives per company in US dollars, as represented on ExecuComp. Following Core & Guay, (1999), we logged all compensation measures.
13
company´s board (Chen & Hsu, 2009). The independent directors on the remuneration committee ratio was calculated as the proportion of independent directors on the remuneration committee. Higher scores (closer to 1) exhibit a higher level of independent directors on duty.
Control variables. We introduced control variables that were aimed at control confounding effects (Jansen et.al, 2006). Firm size (log of total employees) was used as a result of firms with different maturity acting differently, in terms of strategy (Tushman & Romanelli, 1985). Tobin´s q ratio is commonly used as a proxy for investment opportunities. However, we followed the study of Fu, Parkash, & Singhal (2017) and established a relationship between the firm´s Tobin’s q and performance. Furthermore, we used return on asset (ROA) as a control variable, which was also related with firm performance. The logarithm of the company´s R&D expenses was adopted to evaluate the level of R&D of each company. Following a company levels analysis, there were certain variables that had to be adjusted. Salary was calculated as the logarithm of the total salary received by all the executives listed in the company (Core & Guay, 1999). Gender and age control variables were also applied, considering the possible relationship between these and the decisions taken towards innovative and risky decisions. Gender control was calculated by the percentage of women present in the role of executive in each company, while age was determined as the average age of all the executives listed in the company. Year dummies were also included in the models to control for unobserved effects.
Model
ANALYSIS AND RESULTS
Descriptive statistics and quartile univariate analysis
Table 1. Descriptive statistics and correlations Obs. Mean SD (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (1) RE 1,887 0.52 0.21 (2) CB (log) 1,887 3.31 3.36 0.01 (3) SA (log) 1,584 6.12 3.76 0.03 0.01 (4) OA (log) 1,585 5.76 3.69 0.04 0.10 -0.12 (5) SAL (log) 1,887 7.66 0.47 0.15 0.15 0.41 0.23 (6) AGE 1,881 50.78 4.88 0.08 0.03 0.09 0.06 0.28 (7) GEN 1,887 0.92 0.12 -0.03 0.00 -0.01 -0.05 -0.10 -0.03 (8) FP 1,826 2.88 1.94 0.01 -0.01 -0.08 0.04 -0.08 -0.07 0.12 (9) CAP (log) 1,823 13.18 1.85 0.14 0.17 0.50 0.15 0.74 0.22 -0.08 -0.10 (10) ROA 1,815 5.79 16.16 -0.03 0.06 0.00 0.01 0.05 0.04 0.01 0.23 0.14 (11) SIZE (log) 1,808 7.74 1.69 0.02 0.16 0.45 0.07 0.69 0.25 -0.04 -0.10 0.85 0.12 (12) R&D (log) 1,591 11.03 1.94 0.23 0.13 0.52 0.12 0.74 0.23 -0.10 -0.03 0.88 0.04 0.82 (13) BS 1,690 8.35 2.08 0.06 0.13 0.32 0.21 0.58 0.24 -0.09 -0.08 0.65 0.01 0.58 0.63 (14) % ID 1,690 0.78 0.14 0.06 -0.11 0.24 0.03 0.16 0.05 0.05 -0.03 0.13 -0.08 0.10 0.06 0.16 (15) % TS 1,404 6.61 10.08 -0.10 0.04 -0.32 -0.05 -0.31 -0.14 0.06 0.07 -0.18 0.07 -0.13 -0.04 -0.21 -0.23 (16) % IDR 1,683 0.97 0.13 0.04 -0.04 0.10 -0.02 0.04 0.03 -0.03 0.00 0.08 -0.02 0.06 0.07 0.06 0.39 -0.15
Table 2. Quartile univariate analysis
Panel A. Compensation variables Panel B. Firm-level variables
Cut-off Expl. Change Cut-off Expl. Change
Cash bonus Total capital
1Q 0 0.525 1Q 138,315 0.506
2Q 25 0.519 -1.1% 2Q 467,010 0.485 -4.1%
3Q 755 0.505 -2.7% 3Q 1,464,241 0.520 7.2%
4Q 22,900 0.540 6.9% 4Q 112,340,814 0.577 11.1%
Stock awards Firm size (log)
1Q 6 0.534 1Q 7 0.544
2Q 2,263 0.512 -4.1% 2Q 8 0.462 -15.0%
3Q 7,120 0.500 -2.3% 3Q 9 0.513 11.0%
4Q 303,781 0.573 14.5% 4Q 13 0.568 10.9%
Option awards R&D expenses
1Q 0 0.522 1Q 14,642 0.500
2Q 1,542 0.509 -2.6% 2Q 46,140 0.466 -6.9%
3Q 4,779 0.519 2.1% 3Q 171,691 0.503 8.0%
4Q 237,109 0.568 9.4% 4Q 11,279,449 0.604 20.1%
Salary Board size
1Q 1,565 0.500 1Q 7 0.526
2Q 2,100 0.496 -0.8% 2Q 8 0.490 -6.7%
3Q 2,896 0.521 5.1% 3Q 10 0.556 13.4%
4Q 9,966 0.578 10.8% 4Q 16 0.545 -2.0%
17 The role of incentives
Table 3. OLS results for relative exploration Relative exploration
(1) (2) (3)
b (s.e.) b (s.e.) b (s.e.)
Intercept -0.425 (0.164)** -0.327 (0.135)* -0.439 (0.180)* Cash bonus (log) 0.002 (0.002)
Stock awards (log) -0.004 (0.002)* Option awards (log) 0.000 (0.002) Salary (log) 0.038 (0.018)* 0.041 (0.020)* 0.040 (0.020)† Age 0.002 (0.001) 0.002 (0.001) 0.003 (0.001)† Gender 0.017 (0.046) 0.018 (0.051) 0.021 (0.051) Firm performance -0.004 (0.003) -0.007 (0.003)* -0.007 (0.004)* Total capital 0.036 (0.008)*** 0.035 (0.009)*** 0.033 (0.009)*** ROA 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)
Firm size (log emp.) -0.117 (0.007)*** -0.116 (0.007)*** -0.115 (0.007)*** R&D expenses (log) 0.082 (0.007)*** 0.085 (0.007)*** 0.082 (0.007)*** Board size -0.007 (0.003)* -0.006 (0.004) -0.006 (0.004)
Year dummies Yes Yes Yes
F-statistic 20.630 *** 20.080 *** 19.570 ***
R2 0.25 0.26 0.26
Adjusted R2 0.24 0.25 0.24
N observations 1,366 1,147 1,148
N companies 198 174 174
This table reports the results for three ordinary least squares (OLS) regressions using ‘relative exploration’ as dependent variable. Regression (1) uses ‘cash bonus’ as independent variable, regression (2) uses ‘stock awards’ as independent variable, and regression (3) uses ‘option awards’ as independent variable. ‘(log)’ means that the variable has been transformed into a logarithm. ‘(log emp.)’ means that the variable is the logarithmic function of the number of employees. ‘Year dummies’ are dummy variables for the years between (and including) 2004 and 2016.
† = p < 0.10; * = p < 0.05; ** = p < 0.01; *** = p < 0.001.
The impact of independent directors
19
Table 4. OLS results for relative exploration using the interaction term between compensation and percentage of independent directors on board
Relative exploration
(1) (2) (3)
b (s.e.) b (s.e.) b (s.e.)
Intercept -0.420 (0.167)* -0.321 (0.139)* -0.423 (0.183)* Cash bonus (log)*
% Ind. directors 0.000 (omitted) Cash bonus (log) 0.002 (0.002) Stock awards (log)*
% Ind. directors 0.000 (omitted) Stock awards (log) -0.004 (0.002)* Option awards (log)*
% Ind. directors 0.000 (omitted)
Option awards (log) 0.000 (0.002) % Ind. directors -0.008 (0.048) -0.010 (0.053) -0.023 (0.052) Salary (log) 0.039 (0.018)* 0.041 (0.020)* 0.040 (0.020)* Age 0.002 (0.001) 0.002 (0.001) 0.003 (0.001)† Gender 0.017 (0.046) 0.017 (0.051) 0.019 (0.051) Firm performance -0.004 (0.003) -0.007 (0.004)* -0.007 (0.004)* Total capital 0.036 (0.008)*** 0.035 (0.009)*** 0.033 (0.009)*** ROA 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)
Firm size (log emp.) -0.117 (0.007)*** -0.116 (0.007)*** -0.116 (0.007)*** R&D expenses (log) 0.082 (0.007)*** 0.085 (0.007)*** 0.082 (0.007)*** Board size -0.007 (0.003)* -0.006 (0.004) -0.006 (0.004)
Year dummies Yes Yes Yes
F-statistic 19.720 *** 19.110 *** 18.640 ***
R2 0.25 0.26 0.26
Adjusted R2 0.24 0.25 0.24
N observations 1,366 1,147 1,148
N companies 198 174 174
This table reports the results for three ordinary least squares (OLS) regressions using ‘relative exploration’ as dependent variable. Regression (1) uses the interaction term between ‘cash bonus’ and ‘percentage of independent directors’ as independent variable, regression (2) uses the interaction term between ‘stock awards’ and ‘percentage of independent directors’ as independent variable, and regression (3) uses the interaction term between ‘option awards’ and ‘percentage of independent directors’ as independent variable. ‘(log)’ means that the variable has been transformed into a logarithm. ‘(log emp.)’ means that the variable is the logarithmic function of the number of employees. ‘Year dummies’ are dummy variables for the years between (and including) 2004 and 2016. ‘(omitted)’ means that the variable was omitted due to collinearity issues.
21
Table 5. OLS results for relative exploration using the interaction term between compensation and percentage of independent directors on the remuneration committee
Relative exploration
(1) (2) (3)
b (s.e.) b (s.e.) b (s.e.)
Intercept -0.382 (0.181)* -0.341 (0.153)* -0.313 (0.267) Cash bonus (log)*
% Ind. dir. rem. 0.040 (0.018)* Cash bonus (log) -0.037 (0.018)* Stock awards (log)*
% Ind. dir. rem. 0.016 (0.015) Stock awards (log) -0.019 (0.015) Option awards (log)*
% Ind. dir. rem. 0.024 (0.025)
Option awards (log) -0.023 (0.025) % Ind. dir. rem. -0.061 (0.081) 0.005 (0.078) -0.113 (0.186) Salary (log) 0.042 (0.018)* 0.042 (0.020)* 0.039 (0.020)* Age 0.002 (0.001) 0.002 (0.001) 0.003 (0.001)† Gender 0.010 (0.046) 0.014 (0.051) 0.024 (0.051) Firm performance -0.004 (0.003) -0.007 (0.003)* -0.007 (0.004)* Total capital 0.037 (0.008)*** 0.036 (0.009)*** 0.032 (0.009)*** ROA 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)
Firm size (log emp.) -0.118 (0.007)*** -0.117 (0.007)*** -0.115 (0.007)*** R&D expenses (log) 0.081 (0.007)*** 0.085 (0.007)*** 0.083 (0.007)*** Board size -0.007 (0.003)* -0.006 (0.004) -0.006 (0.004)
Year dummies Yes Yes Yes
F-statistic 19.250 *** 18.340 *** 17.860 ***
R2 0.26 0.26 0.26
Adjusted R2 0.24 0.25 0.24
N observations 1,366 1,147 1,148
N companies 198 174 174
This table reports the results for three ordinary least squares (OLS) regressions using ‘relative exploration’ as dependent variable. Regression (1) uses the interaction term between ‘cash bonus’ and ‘percentage of independent directors’ as independent variable, regression (2) uses the interaction term between ‘stock awards’ and ‘percentage of independent directors’ as independent variable, and regression (3) uses the interaction term between ‘option awards’ and ‘percentage of independent directors’ as independent variable. ‘(log)’ means that the variable has been transformed into a logarithm. ‘(log emp.)’ means that the variable is the logarithmic function of the number of employees. ‘Year dummies’ are dummy variables for the years between (and including) 2004 and 2016.
Robustness tests
A “robustness check” is where the researcher analyzes how “core” regression coefficient estimates act when the regression is modified in some way (White and Lu, 2014). This can be done by adding or clearing out regressors. Structural evidence can be explained if the coefficients are plausible and robust. First, we analyzed the results on table 6 for robustness. Here, we calculated the robust standard errors in STATA (regress x y, robust). Robust standard errors fix for a non-distribution of variances. We check that the results didn´t alter significantly from the results performed above. Consequently, heteroscedasticity (heterogeneity of variance) does not bias the results obtained.
Figure 2
23
Table 6. Robust standard errors OLS results for relative exploration Relative exploration
(1) (2) (3)
b (r.s.e.) b (r.s.e.) b (r.s.e.) Intercept -0.425 (0.168)* -0.327 (0.141)* -0.439 (0.189)* Cash bonus (log) 0.002 (0.002)
Stock awards (log) -0.004 (0.002)†
Option awards (log) 0.000 (0.002)
Salary (log) 0.038 (0.020)† 0.041 (0.021)† 0.040 (0.022)† Age 0.002 (0.001) 0.002 (0.001) 0.003 (0.001)† Gender 0.017 (0.043) 0.018 (0.048) 0.021 (0.048) Firm performance -0.004 (0.003) -0.007 (0.003)* -0.007 (0.003)* Total capital 0.036 (0.008)*** 0.035 (0.008)*** 0.033 (0.008)*** ROA 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)
Firm size (log emp.) -0.117 (0.007)*** -0.116 (0.007)*** -0.115 (0.007)*** R&D expenses (log) 0.082 (0.007)*** 0.085 (0.008)*** 0.082 (0.008)*** Board size -0.007 (0.003)* -0.006 (0.004) -0.006 (0.004)
Year dummies Yes Yes Yes
F-statistic 22.610 *** 24.050 *** 22.740 *** R2 0.25 0.26 0.26 Root MSE 0.19 0.19 0.19 N observations 1,366 1,147 1,148 N companies 198 174 174
This table reports the results for three robust standard errors ordinary least squares (OLS) regressions using ‘relative exploration’ as dependent variable. Regression (1) uses ‘cash bonus’ as independent variable, regression (2) uses ‘stock awards’ as independent variable, and regression (3) uses ‘option awards’ as independent variable. ‘(log)’ means that the variable has been transformed into a logarithm. ‘(log emp.)’ means that the variable is the logarithmic function of the number of employees. ‘Year dummies’ are dummy variables for the years between (and including) 2004 and 2016.
† = p < 0.10; * = p < 0.05; ** = p < 0.01; *** = p < 0.001.
25
Table 7. OLS results for relative exploration using compensation quartile regression Relative exploration
(1) (2) (3)
b (s.e.) b (s.e.) b (s.e.)
Intercept -0.422 (0.165)* -0.383 (0.177)* -0.416 (0.184)* (Base: 1st quartile) 2nd quartile 0.009 (0.032) -0.041 (0.016)* 0.001 (0.019) 3rd quartile 0.005 (0.013) -0.049 (0.017)** 0.005 (0.017) 4th quartile 0.021 (0.015) -0.013 (0.020) 0.009 (0.018) Salary (log) 0.039 (0.018)* 0.036 (0.020)† 0.038 (0.020)† Age 0.002 (0.001) 0.002 (0.001)† 0.003 (0.001)† Gender 0.016 (0.046) 0.019 (0.051) 0.022 (0.051) Firm performance -0.004 (0.003) -0.008 (0.004)* -0.007 (0.004)* Total capital 0.037 (0.008)** 0.034 (0.009)*** 0.032 (0.009)*** ROA 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)
Firm size (log emp.) -0.117 (0.007)*** -0.117 (0.007)*** -0.115 (0.007)*** R&D expenses (log) 0.081 (0.007)*** 0.083 (0.007)*** 0.082 (0.007)*** Board size -0.007 (0.003)* -0.006 (0.004) -0.006 (0.004)
Year dummies Yes Yes Yes
F-statistic 18.880 *** 18.690 *** 17.780 ***
R2 0.25 0.27 0.26
Adjusted R2 0.24 0.25 0.24
N observations 1,366 1,147 1,148
N companies 198 174 174
Table 8. OLS results for relative exploration excluding null values Relative exploration
(1) (2) (3)
b (s.e.) b (s.e.) b (s.e.)
Intercept -0.278 (0.173) -0.246 (0.159) -0.381 (0.165)† Cash bonus (log) 0.004 (0.005)
Stock awards (log) 0.004 (0.006) Option awards (log) 0.002 (0.006) Salary (log) 0.030 (0.026) -0.023 (0.024) 0.063 (0.025)* Age 0.001 (0.002) 0.005 (0.002)** 0.003 (0.002)* Gender 0.056 (0.064) -0.029 (0.056) 0.033 (0.057) Firm performance 0.006 (0.004) -0.007 (0.004) -0.006 (0.004) Total capital 0.052 (0.012)*** 0.047 (0.010)*** 0.025 (0.010)* ROA -0.001 (0.000) -0.001 (0.000) -0.001 (0.000) Firm size (log emp.) -0.106 (0.011)*** -0.126 (0.008)*** -0.110 (0.008)*** R&D expenses (log) 0.066 (0.010)*** 0.094 (0.009)*** 0.077 (0.008)*** Board size -0.012 (0.005)** -0.002 (0.004) -0.008 (0.004)†
Year dummies Yes Yes Yes
F-statistic 9.220 *** 17.830 *** 15.850 ***
R2 0.23 0.30 0.26
Adjusted R2 0.21 0.28 0.25
N observations 693 873 865
N companies 181 153 165
This table reports the results for three ordinary least squares (OLS) regressions using ‘relative exploration’ as dependent variable. The sample used is transformed to exclude null values of compensation. Regression (1) uses ‘cash bonus’ as independent variable, regression (2) uses ‘stock awards’ as independent variable, and regression (3) uses ‘option awards’ as independent variable. ‘(log)’ means that the variable has been transformed into a logarithm. ‘(log emp.)’ means that the variable is the logarithmic function of the number of employees. ‘Year dummies’ are dummy variables for the years between (and including) 2004 and 2016.
† = p < 0.10; * = p < 0.05; ** = p < 0.01; *** = p < 0.001.
27
remuneration are not significant. The only added value of this analysis is that if one doesn´t consider the observations that have a stock award of zero, the impact of stock award changing it becomes insignificant.
Table 9. OLS results for relative exploration using percentage of total shares owned Relative exploration
(1) (2) (3)
b (s.e.) b (s.e.) b (s.e.)
Intercept -0.262 (0.190) -0.254 (0.189) -0.270 (0.195) % total shares owned -0.001 (0.001) -0.001 (0.001) -0.001 (0.001) Cash bonus (log) 0.003 (0.002) Stock awards (log) -0.005 (0.002)* Option awards (log) 0.000 (0.002) Salary (log) 0.018 (0.022) 0.018 (0.021) 0.021 (0.022) Age 0.002 (0.001) 0.001 (0.001) 0.002 (0.001) Gender 0.012 (0.053) 0.007 (0.053) 0.011 (0.054) Firm performance -0.006 (0.004) -0.006 (0.004) -0.006 (0.004) Total capital 0.032 (0.009)*** 0.036 (0.009)*** 0.033 (0.009)*** ROA 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)
Firm size (log emp.) -0.120 (0.008)*** -0.121 (0.008)*** -0.120 (0.008)*** R&D expenses (log) 0.085 (0.008)*** 0.088 (0.008)*** 0.084 (0.008)*** Board size -0.003 (0.004) -0.003 (0.004) -0.002 (0.004)
Year dummies Yes Yes Yes
F-statistic 17.720 *** 18.100 *** 17.560 ***
R2 0.27 0.27 0.26
Adjusted R2 0.25 0.26 0.25
N observations 1,049 1,048 1,049
N companies 169 169 169
This table reports the results for three ordinary least squares (OLS) regressions using ‘relative exploration’ as dependent variable. The independent variable in all three regressions is now the percentage of total shares owned. Regression (1) uses ‘cash bonus’ as control variable, regression (2) uses ‘stock awards’ as control variable, and regression (3) uses ‘option awards’ as control variable. ‘(log)’ means that the variable has been transformed into a logarithm. ‘(log emp.)’ means that the variable is the logarithmic function of the number of employees. ‘Year dummies’ are dummy variables for the years between (and including) 2004 and 2016.
DISCUSSION AND CONCLUSION
Research on the antecedents that impact the innovative orientation of a firm is expanding. Throughout the years, scholars have analyzed the factors that can affect the innovation outcomes of the company. Although prior research on agency theory suggests that the alignment between shareholders and executives towards a common orientation can be accomplished by a suited incentive plan, the empirical examinations in this thesis collected contrasting results. Prior empirical research analysed in depth the effect of board background characteristics (Heyden et al., 2015), senior team attributes and leadership (Jansen, Vera, & Crossan, 2009), and the impact of environmental and organization antecedents (Jansen et. Al., 2005) on the innovative orientation of the firm. However, there are limited empirical researches that interpret the impact of the compensation plan on exploration/exploitation.
29
there is a recent shift towards qualitative aspects. Metrics such as strategic initiatives and project completion are now milestones being used to awards these types of bonuses. This shift goes in the line with the alignment of interests between both shareholders and executives, as well as acting as a motivation for the manager. This could be one of the reasons why the increase in annual cash bonus is not directly related with an increase on the company’s exploitative orientation.
now be vulnerable to firm-specific risks. On the other hand, option awards have different characteristics. Due to their convex payoff methods, the manager’s wealth cannot be damaged if the current projects fail. Ryan and Wiggins (2001) propose that options are better for reducing the long-term risk aversion. Indeed, stock awards are a LTIP that can be used as a way of encouraging managers to pursue exploratory innovations, but we have to be meticulous and explain the different types of stock existent. These can be important in explaining why the results pointed out to a more exploitative orientation of the firm when stocks were used. Stock-based incentives can include a portfolio of awards as (un)restricted stock grants, phantom stocks, and performance shares.
31
company and help in the performance so the shares would increase in value in the future. Respectively, we have to be careful when analyzing stock awards. The different types of stock bonuses may have different impacts on relative exploration. The foundation of this study is the relationship between length of incentives and innovation. Well, the length goal in stock awards varies differently across the diverse types of award.
discretionary plans do not have strictly financial goals, and the final payout depends on judgments made by the remuneration committee. The remuneration committee can introduce qualitative and non-financial goals that will affect the annual cash bonus of the manager. This positive impact on exploration can be provoked by an objective and unbiased presence of independent directors on the remuneration committee that looks for creating metrics and goals that reduce managerial risk aversion and boost exploration. If managers focus only on performance metrics, they can exhibit something that is called surrogation. Choi, Hecht, & Tayler (2012) explain surrogation as the case where the managers are merely focused on performance measures which is hindering their capacity of taking actions that are aligned with the organization strategy. Managers can be evaluated through multiple compensation metrics, which will lure them to follow a different path.
33
qualitative metrics, which award strategic decisions, project completion and innovative adjustment, the values of exploration are expected to increase.
Limitations and future directions
There are diverse limitations that, by consequence, provide relevant insights for further research. Firstly, although agency theory is highly influential, in certain scenarios is incapable to provide satisfactory explanations on many issues because these can be related with a miscellaneous of other factors. Furthermore, the use of other theories that explain the drivers of innovation are frequently used. The resource-based theory is often utilized by researchers to understand and explain firm´s innovative outcomes. RBV claims that firm’s resources are the source of competitive advantage, giving an emphasis to resources, capabilities and competences. The industrial organizational theory (IO) is an alternative theory that emphasize the importance of industry forces in which a firm operates, focusing on the industry, buyers, suppliers and competitors. All in all, agency theory by itself may not be enough to fully understand the real drivers of different types of innovation.
Furthermore, and now focusing on the regression model, the OLS may have some flaws and limitations that can hinder the analysis. OLS may lead to insufficient predictions when dealing with independent variables that have the chance of being correlated with each other. Due to similarity on my independent variables (all are compensation measures) the predictions might be affected accordingly. For example, Makri, Lane, & Gomez-Mejia (2006) utilized fixed-effects intercept model in their incentives study due to the ability of explicitly modeling unaccounted and unobserved heterogeneity, also solving the omitted variables that are caused by correlation of variables (Baltagi, 2005).
the types of stock and option awards would be enriching for this study. Different types of stocks and options may impact differently our dependent variable, as mentioned in the discussion. Future research could do an in-depth analysis on the effects that different types of stock and option awards have on the innovative orientation of the firm. Secondly, it would be fruitful to integrate diversity moderating variables in our study. The characteristics of the CEO and past experience could be an interesting factor that would definitely affect the innovation orientation of the firm (Ahn, Mortara, & Minshall, 2013; Lin, Lin, Song, & Li, 2011).
35
REFERENCES
Ahn, J. M., Mortara, L., & Minshall, T. (2013). Linkages between Openness and CEO Characteristics in Innovative SMEs. SSRN Electronic Journal.
Baltagi, B. H. (2005). Econometric analysis of panel data. Vasa.
Bebchuk, L. A., & Fried, J. M. (2003). Summary for Policymakers. In Intergovernmental Panel on Climate Change (Ed.), Climate Change 2013 - The Physical Science Basis (pp. 1–30). Cambridge: Cambridge University Press.
Benner, M. J., & Tushman, M. L. (2003). Exploitation, Exploration, and Process
Management: The Productivity Dilemma Revisited. Academy of Management Review, 28(2), 238–256.
Bhagat, S., & Welch, I. (1995). Corporate research & development investments international comparisons. Journal of Accounting and Economics, 19(2–3), 443–470. Brennan, M. J. (1995). Corporate Finance over the Past 25 Years. Financial Management,
24(2), 9.
Castro, C. B., De La Concha, M. D., Gravel, J. V., & Periñan, M. M. V. (2009). Does the Team Leverage the Board’s Decisions? Corporate Governance: An International Review, 17(6), 744–761.
Choi, J. (Willie), Hecht, G. W., & Tayler, W. B. (2012). Lost in Translation: The Effects of Incentive Compensation on Strategy Surrogation. The Accounting Review, 87(4), 1135– 1163.
Core, J., & Guay, W. (1999). The use of equity grants to manage optimal equity incentive levels. Journal of Accounting and Economics, 28(2), 151–184.
Cuervo, A. (2002). Corporate Governance Mechanisms: a plea for less code of good governance and more market control. Corporate Governance, 10(2), 84–93.
Danneels, E. (2002). The dynamics of product innovation and firm competences. Strategic Management Journal, 23(12), 1095–1121.
David, P., Hitt, M. A., & Gimeno, J. (2001). THE INFLUENCE OF ACTIVISM BY INSTITUTIONAL INVESTORS ON R&D. Academy of Management Journal, 44(1), 144–157.
Dechow, P. M., & Sloan, R. G. (1991). Executive incentives and the horizon problem. Journal of Accounting and Economics, 14(1), 51–89.
DENIS, D. J., DENIS, D. K., & SARIN, A. (1997). Agency Problems, Equity Ownership, and Corporate Diversification. The Journal of Finance, 52(1), 135–160.
Duriau, V. J., Reger, R. K., & Pfarrer, M. D. (2007). A Content Analysis of the Content Analysis Literature in Organization Studies: Research Themes, Data Sources, and Methodological Refinements. Organizational Research Methods, 10(1), 5–34. Eisenhardt, K. M. (1989). Agency Theory: An Assessment and Review. The Academy of
Management Review, 14(1), 57.
Fama, E. (1980). Agency Problem and the Theory of the Firm. Journal of Political Economics.
Fama, E. F., & Jensen, M. C. (1983). Agency Problems and Residual Claims. The Journal of Law and Economics, 26(2), 327–349.
Feltham, G. A., & Wu, M. G. H. (2001). Incentive efficiency of stock versus options. Review of Accounting Studies.
37
International Research Journal of Appllied Finance.
Garcia, R. (2002). A critical look at technological innovation typology and innovativeness terminology: a literature review. Journal of Product Innovation Management, 19(2), 110–132.
He, Z.-L., & Wong, P.-K. (2004). Exploration vs. Exploitation: An Empirical Test of the Ambidexterity Hypothesis. Organization Science, 15(4), 481–494.
Heyden, M. L. M., Oehmichen, J., Nichting, S., & Volberda, H. W. (2015). Board
Background Heterogeneity and Exploration-Exploitation: The Role of the Institutionally Adopted Board Model. Global Strategy Journal, 5(2), 154–176.
Holland, J. H. (1995). Hidden Order: How Adapation Builds Complexity. Basic Books. Holmqvist Mikael. (2004). Experiential Learning Processes of Exploitation and Exploration
within and between Organizations: An Empirical Study of Product Development on JSTOR. Organization Science.
Jansen, J. J. P., Vera, D., & Crossan, M. (2009). Strategic leadership for exploration and exploitation: The moderating role of environmental dynamism. The Leadership Quarterly, 20(1), 5–18.
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.
Jenwittayaroje, N., & Jiraporn, P. (2017). Do Independent Directors Improve Firm Value? Evidence from the Great Recession. International Review of Finance.
Jiraporn, P., & Nimmanunta, K. (2017). Estimating the effect of board independence on managerial ownership using a quasi-natural experiment. Applied Economics Letters, 1– 7.
Kuran, T. (1988). The tenacious past: Theories of personal and collective conservatism. Journal of Economic Behavior & Organization, 10(2), 143–171.
Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14(S2), 95–112.
Lin, C., Lin, P., Song, F. M., & Li, C. (2011). Managerial incentives, CEO characteristics and corporate innovation in China’s private sector. Journal of Comparative Economics, 39(2), 176–190.
Makri, M., Lane, P. J., & Gomez-Mejia, L. R. (2006). CEO incentives, innovation, and performance in technology-intensive firms: a reconciliation of outcome and behavior-based incentive schemes. Strategic Management Journal, 27(11), 1057–1080.
March, J. G. (1991). Exploration and Exploitation in Organizational Learning. Organization Science, 2(1), 71–87.
McDermott, C. (2002). Managing radical innovation: an overview of emergent strategy issues. Journal of Product Innovation Management, 19(6), 424–438.
McGuire, J. B., James, B. E., & Papadopoulos, A. (2016). Do your Findings Depend on your Data(base)? A Comparative Analysis and Replication Study Using the Three Most Widely Used Databases in International Business Research. Journal of International Management, 22(2), 186–206.
McKenny, A. F., Aguinis, H., Short, J. C., & Anglin, A. H. (2016). What Doesn’t Get Measured Does Exist. Journal of Management, 014920631665759.
Rice, M. P., O’Connor, G. C., Peters, L. S., & Morone, J. G. (1998). Managing Discontinuous Innovation. Research-Technology Management, 41(3), 52–58.
39
Schumpeter, J. (1934). The theory of economic development. Joseph Alois Schumpeter. Sheikh, S. (2012). Do CEO compensation incentives affect firm innovation? Review of
Accounting and Finance, 11(1), 4–39.
Tushman, M. L., & Romanelli, E. (1985). Organizational evolution: A metamorphosis model of convergence and reorientation. Research in Organizational Behavior.
Uotila, J. (2012). Balancing Exploratory and Exploitative Adaption: Organizational and Environmental Dynamics. Statewide Agricultural Land Use Baseline 2015.
Veryzer, R. W. (1998). Discontinuous Innovation and the New Product Development Process. Journal of Product Innovation Management, 15(4), 304–321.
Wahal, S., & McConnell, J. J. (2000). Do institutional investors exacerbate managerial myopia? Journal of Corporate Finance, 6(3), 307–329.
Williamson, O. E. (1983). Credible commitments: Using hostages to support exchange. The American Economic Review.
APPENDICES
Table A1: Variance Inflection Results
VIF tests
Variable VIF 1/VIF VIF 1/VIF VIF 1/VIF
lbom 1,05 0,952445 lstaw 1,4 0,715059 lopaw 1,13 0,881449 lcapt 7,94 0,125934 8,13 0,123076 8,03 0,124481 lrdexpe 6,23 0,160431 6,33 0,158056 6,3 0,158762 size 5,1 0,196075 4,97 0,201219 5 0,199887 lsal 2,78 0,360334 2,73 0,365768 2,89 0,34605 td 1,91 0,523477 1,96 0,511417 2,01 0,497783 nroa 1,18 0,845125 1,18 0,847848 1,17 0,852033 tq1 1,15 0,8725 1,16 0,862866 1,16 0,859184 age 1,08 0,924038 1,08 0,923791 1,08 0,927507 gen1 1,04 0,961084 1,05 0,953627 1,05 0,955839 Mean VIF 2,95 3 2,98
Table A2: Variance Inflection Results for Annual Cash Bonus
Output Cash Bonus
Variable VIF 1/VIF
41
Table A3: Variance Inflection Results for Stock Awards
Output Stock Awards
Variable VIF 1/VIF
lcapt 8,13 0,123076 lrdexpe 6,33 0,158056 size 4,97 0,201219 lsal 2,73 0,365768 td 1,96 0,511417 lstaw 1,4 0,715059 nroa 1,18 0,847848 tq1 1,16 0,862866 age 1,08 0,923791 gen1 1,05 0,953627 Mean VIF 3
Table A4: Variance Inflection Results for Option Awards
Output Stock Awards
Variable VIF 1/VIF
Table B1: REXP2 and Cash Bonus (With Outliers)
Table B2: REXP2 and Stock Awards (With Outliers)
0 .2 .4 .6 .8 1 0 5000 10000 15000 20000 25000 BOM
Fitted values REXP2
0 .2 .4 .6 .8 1 0 500000 1000000 1500000 2000000 STAW
43 Table B3: REXP2 and Stock Awards (With Outliers)
Table B4: REXP2 and Stock Awards (Without Outliers)
0 .2 .4 .6 .8 1 0 50000 100000 150000 200000 250000 OPAW
Fitted values REXP2
0 .2 .4 .6 .8 1 0 50000 100000 150000 200000 250000 OPAW
