It is time to think about time orientation

It is time to think about time orientation

2 Date:

June 24th 2013

Student:

Nienke IJdens; nienkeijdens@gmail.com Student number: s1770004 Tel.: +31 (0)6 50 66 34 447

Field of study:

Faculty of Economics and Business MSc Human Resource Management

3 ABSTRACT

This research examines the influence of CEO age and cognitive complexity on CEOs’ time orientation. Specifically, the present study argues that if leaders are getting older, they will perceive that time is running out, which will result in a past or present time orientation. In contrast, younger CEOs tend to have a future time orientation, because they perceive the future as expansive. A CEOs’ time orientation is also related to his or her cognitive capacity, because time orientation includes the temporal horizon an individual is capable of holding in his or her mind. The present study argues that the relationship between age and past, present and future orientation would diminish with increasing levels of cognitive complexity, such that superior cognitive complexity would decouple a leaders’ age from his or her time

orientation. To test whether cognitive complexity moderates the relationship between age and time orientation, a hierarchical regression analysis has been performed. Using the CEO letters to the shareholders of 109 US-based firms from the Fortune 500 list, the data stress a

4 INTRODUCTION

Time orientation is an important aspect of leadership behavior and the career success of leaders in an organization (Thoms & Greenberger, 1995). Zimbardo and Boyd (1999) state that time orientation is a non-conscious, cognitive temporal “bias” toward past, present or future thinking. Time orientation helps people to give meaning, order, and coherence to

personal and social experiences. As already noted,there are three different time orientations,

namely past, present, and future time orientations (Karande & Merchant, 2012). According to Thoms (2003), every person has a basic time orientation, which means that people tend to favor one orientation over the other. Leaders who have a past orientation are thinking about the past most of the time and like to work in the way that they are used to (Holbrook, 1993; Zimbardo & Boyd, 1999). Leaders with a present time orientation are focused on the achievement of short-term goals and focus on the status quo (Bergadaà, 1990; Murrell & Mingrone, 1994; McClelland, Liang & Barker III, 2010). Finally, future-oriented leaders are driven by the future and are good in creating a vision (Thoms, 2003).

It depends on the company’s situation and industry which time orientation of leaders is most ideal. For example, if there was little change in a particular industry for years, leaders of companies which are operating in such a stable environment, may be more accustomed to dealing with past and present activities. In this case, present or past oriented leaders are optimal. In contrast, when a company has to deal with a dynamic environment and rapid change, a leader may focus more on tasks which require a future time orientation, such as long-range planning (Thoms & Greenberger, 1995).

5 Additionally, research shows that people need a broader time span if they want to move up the ladder in an organization. In other words, leaders need to be capable of having a broad outlook on the future (Jacques, 1982). However, not every leader has a broad outlook or is able to create a vision. For example, U.S. business leaders have been criticized in the past for focusing too much on the short-term in order to maximize their own rewards. However, another explanation for this could be that certain leaders find it very difficult to focus on the future because of their predominant past or present time orientation. As such, knowledge about the antecedents of leaders’ time orientation seems highly relevant.

The importance of the topic notwithstanding, there is hardly any research on

leadership and time orientation (Thoms & Greenberger, 1998; Shamir, 2011). Specifically, there is little understanding of time orientation and its origins among leaders. Despite this lack of knowledge in a business context, research of Carstensen and Lang (1997) shows that age plays a role with regard to the perception of time in the general population. In their studies, Carstensen and Lang (1997) found out that there are clear associations between age and perceived time left in life. In comparison to younger people, older persons feel that their future is limited and realize that they do not have a lot of time left to achieve their goals. Older persons therefore mostly have a present or past orientation, while their younger

counterparts have a stronger future time orientation (Fingerman & Perlmutter, 1995). Drawing on Socioemotional Selectivity Theory (SST; Carstensen, 1999), people tend to focus more on the present when they have the feeling that “time is running out”.

Importantly, however, it is unclear whether the role of age for an individual’s time

orientation will equally apply in all cases.As Thoms (2003) noted, one’s time orientation is

also related to one’s cognitive capacity, for example, because time orientation includes the temporal horizon an individual is capable of holding in his or her mind. Cognitive complexity represents the degree to which an individual differentiates between various competing

solutions when solving cognitive problems and attempts to integrate among these solutions (Tetlock, 1981). In other words, it reflects the integration and differentiation of multiple frames of reference in cognitive processing (Suedfeld, Tetlock & Streufert, 1992; Curşeu & Rus, 2005). Cognitive complexity is associated with a wide range of communication skills, problem solving abilities, and capabilities related to this (Burleson & Caplan, in press). Since a leaders’ time orientation is related to his or her cognitive complexity, it is a possibility that the relationship between age and time orientation might be influenced by the level of

6 As stated above, there is a lack of literature focusing on the antecedents of leaders’

time orientation.Measuring the concept of time orientation is difficult and appears rarely in

empirical studies (Souder & Bromiley, 2011).In particular, there is no research which focuses

on the effects aging and cognitive complexity might have on the time orientation of leaders. In this research, I will examine age as a predictor of time orientation of a specific, particularly important type of leaders, namely CEOs. In addition, I will investigate if cognitive complexity has a moderating effect on the relationship between age and time orientation. This will be done by examining CEOs’ time orientation, as revealed by their word use in shareholder letters, which are part of the company’s annual report. Letters to the shareholders are issued each year by companies and are meant for shareholders and other interested parties. The letters contain information about the financial performance of a company. Also, in these letters CEOs try to provide reasons for past successes or failures and attempt to give expectations of future results (Staw, McKechnie & Puffer, 1983). Moreover, letters to shareholders often reflect the vision of the CEO: “Because an organizational vision is a cognitive image, it exists in the leader’s mind. A written statement, a business plan, or a speech to followers is a manifestation of the vision, not the vision itself” (Thoms, 2003, p. 61). The conceptual model of this research is shown in Figure 1.

--- Insert Figure 1 here ---

By empirically testing this model, this research will make a contribution to the

development of time-focused leadership theories. As already pointed out, there is a gap inthe

literature concerning the origins of time orientation in a leadership context. Theory building has been difficult because of a lack of empirical evidence, with no directions for further research as a result. By focusing on the effects of aging and cognitive complexity on the time orientations of CEOs of Fortune 500 companies, I will try to come up with interesting

7

HYPOTHESES DEVELOPMENT

Age and time orientation

According to Pennebaker and Stone (2003), changes in age have an influence on individuals’ time orientation. In particular, Socioemotional Selectivity Theory (SST) emphasizes the cognitive mechanism of perceived sense of time in this regard. Specifically, SST argues that age has an effect on the way people perceive time and therefore the way individuals are setting goals (Carstensen, 2006). In comparison with younger people, older people tend to live more in the moment, because the future is seen as limited and not as expansive anymore. This means that the attention of older people shifts to experiences which are occurring in the present or happened in the past. Consequently, older persons mostly have a past or present orientation. In contrast, younger people are more future-oriented as they perceive their future as expansive and open-ended (Fingerman & Perlmutter, 1995; Carstensen, 2006).

A second argument for the shift from future to present or past time orientation with increasing age is that older leaders typically possess well-developed clusters of organizational and strategic problem-solving recipes. Moreover, older leaders have more fully developed schemas to rely on to interpret stimuli, in comparison to younger people, because of the obtainment of common sense, wisdom, and experience. Therefore, older people have a wider knowledge base which results in less information seen as complex or new. This is nicely shown in the cliché “been there, done that” (George & Jones, 2000). The organizational and strategic problem-solving recipes proved to have positive outcomes in the past, which results in a strong belief in the quality of these problem-solving recipes. In other words, the way of working and problem solving in the past, proved to be an effective strategy. Therefore, older leaders tend to stick to this familiar method of working and are likely to develop an increasing commitment to the status quo. Consequently, older leaders will focus more on the short-term and thus on the present or past (McClelland et al., 2010).

8 time orientation. Based on the research of mentioned above, the following hypotheses are proposed:

Hypothesis 1a. There is a positive relationship between a CEOs’ age and his or her past time orientation.

Hypothesis 1b. There is a positive relationship between a CEOs’ age and his or her present time orientation.

Hypothesis 1c. There is a negative relationship between a CEOs’ age and his or her future time orientation.

Cognitive complexity and time orientation

An individuals’ time orientation is also related to his or her cognitive capacity. In particular, one’s time orientation can be seen as a cognitive anchor which indicates an individual’s horizon for reflection and planning (Thoms, 2003). According to Tetlock (1981) cognitive complexity differs between persons and their contexts. Cognitive complexity consists of two components of reasoning, namely “the extent to which someone differentiates between multiple competing solutions and the extent to which someone integrates among solutions” (Tausczik & Pennebaker, 2010, p. 35).

It is expected that the more cognitive capacity a leader has, the better this person will be able to have a broad outlook on the future, or in other words, will have a bigger, more future-oriented time span. Leaders with cognitive complexity have multiple solutions available to them which helps them in dealing with the challenges they face (Thoms, 2003; Jaques, 1982). This may result in a focus on the long-term, because with their capability to integrate among solutions, they find it relatively easy to set future goals and develop a good strategy. Therefore, it is expected that cognitive capacity will improve their goal setting and planning abilities.

Next to increasing a leader’s future time orientation, it is also expected that high levels of cognitive complexity will result in a greater past and present time orientation. As already pointed out in the introduction, cognitive complexity reflects the integration and

9 1985). According to Zimbardo and Boyd (1999), balanced time perspective can be seen as “the mental ability to switch flexibly among time orientations, depending on task features, situational considerations, and personal resources rather than be biased toward a specific time orientation that is not adaptive across situations” (p. 1285).

In conclusion, it is expected that there is a positive relationship between cognitive complexity and future, past and present orientation. Based on the above mentioned findings, the following hypotheses are proposed:

Hypothesis 2a. There is a positive relationship between a CEOs’ cognitive complexity and his or her past time orientation.

Hypothesis 2b. There is a positive relationship between a CEOs’ cognitive complexity and his or her present time orientation.

Hypothesis 2c. There is a positive relationship between a CEOs’ cognitive complexity and his or her future time orientation.

Cognitive complexity as moderator

In the previous paragraphs the main effects of age and cognitive complexity on time orientation have been pointed out, based on literature of various scholars. However, this research examines if cognitive complexity will also have a moderating effect on the relationship between age and time orientation. It might be that if a CEO has high cognitive complexity, the influence age has on his or her time orientation will change.

As already mentioned in the previous paragraphs, it is expected that individuals with high cognitive complexity are able to more flexibly approach different situations with

different time orientations, due to the capability of integrating multiple time orientations. This was referred as ‘balanced time perspective’ (Boniwell & Zimbardo, 2004; Rappaport, Enrich & Willson, 1985). It can be argued, accordingly, that superior cognitive complexity may have such a strong influence that it decouples a leaders’ age from his or her time orientation. Hence, leaders’ time orientation may be less “biased” by their age, which results in a more flexible approach towards different situations with different time orientations, independent of leaders’ age. This means that cognitive complexity will diminish the positive relationship between age and past and present time orientation. In contrast, due to high cognitive complexity, the negative relationship between age and future orientation should be

10 perceived sense of time (Carstensen et al., 1999).

In other words, the expectation is that the relationship between age and past, present and future orientation will decline due to the level of cognitive complexity a CEO has. Elaborating on the expected main effects by building on the findings of Boniwell and

Zimbardo (2004) and Rappaport, Enrich and Willson (1985), I therefore assume that cognitive complexity will act as a moderator between age and time orientation. I expect the

relationships between age and past, present, and future time orientation to decline with increasing cognitive complexity. With the previous hypotheses kept in mind, the following hypotheses will be tested:

Hypothesis 3a. Cognitive complexity will act as a moderator in the relationship between CEOs’ age and past time orientation. This positive relationship will be less pronounced among CEOs with higher than among CEOs with lower cognitive

complexity.

Hypothesis 3b. Cognitive complexity will act as a moderator in the relationship between CEOs’ age and present time orientation. This positive relationship will be less pronounced among CEOs with higher than among CEOs with lower cognitive

complexity.

Hypothesis 3c. Cognitive complexity will act as a moderator in the relationship between CEOs’ age and future time orientation. This negative relationship will be less pronounced among CEOs with higher than among CEOs with lower cognitive

complexity.

METHOD

Data collection and sample description

Data have been collected by analyzing letters to the shareholders written by CEOs of Fortune 500 companies in the year 2011 in the United States, which can be found in the respective companies’ annual reports. Listed companies are obliged to publish these annual reports, which made the process of obtaining data relatively easy (submitted at the U.S. Securities and Exchange Commission (SEC); http://www.sec.gov/edgar/searchedgar/companysearch.html). Initially, I tried to collect 150 letters to the shareholders (i.e., the top 150 companies in the Fortune 500 list; http://money.cnn.com/magazines/fortune/fortune500/2011/full_list/).

11 reports. Due to excessive missing data, 41 companies have been excluded. Therefore, the existing dataset consisted of 109 letters, of which 102 were derived from male CEOs and 7 letters from female CEOs. The respective CEOs ranged in age from 39 to 81 years (M = 57.59, SD = 6.28).

The letters to the shareholders were analyzed using a well-validated computerized text analysis program called Linguistic Inquiry and Word Count (LIWC; http://www.liwc.net). Tausczik and Pennebaker (2010) describe LIWC as a “transparent text analysis program that counts words in psychologically meaningful categories” (Tausczik & Pennebaker, 2010, p. 24). In order to use the letters to the shareholders of the different companies, the shareholder letters have been converted into MS-word files. Moreover, the MS-word files have been carefully adjusted to the LIWC guidelines, as suggested by Pennebaker and Stone (2003). Using shareholder letters can be seen as an effective research setting because the way CEOs use words reflects their way of thinking (Tausczik & Pennebaker, 2010). Moreover, the reason why CEO letters to shareholders are used as linguistic analysis method is because these letters have never been used in former studies in examining time orientations of CEOs (McClelland et al., 2010).

Measures

LIWC classifies the text of the letters into numerous categories. The constructs used in this research, namely time orientation and cognitive complexity, can be deducted from some of these categories.

Time orientation. The categories that are mentioned in the LIWC category list and used in this research are past tense verbs (e.g., had, earned, moved), present tense verbs (e.g., hear, does, is) and future tense verbs (e.g., will, shall) in order to measure the first construct in this study, namely past, present, and future time orientation. In particular, verb tenses are linguistic elements that are helpful in identifying one’s cognitive focus, which can reflect priorities and intentions (Tausczik & Pennebaker, 2010). Moreover, analyzing the tense of common verbs shows the attention to a particular time orientation, according to research of Gunsch et al. (2000).

12 Tentative words, exclusive words and negations are useful in making distinctions, since people use these words when they distinguish between what belongs to a certain category and what not (Tausczik & Pennebaker, 2010). According to Graesser, McNamara, Louwerse and Cai (2004) conjunctions link various thoughts together and are necessary for creating coherent narratives. A formula that stems from a study of Abe (2011) has been used in order to

measure cognitive complexity. This formula adds up the amount of exclusive words,

conjunctions, negations and expressions of tentativeness within the letters to the shareholders. These different types of words are categorized by ‘excl’, ‘negate’, ‘tentat’ and ‘conj’ in the

LIWC category list.Because the different categories are transformed in standardized scores

the computed standardized variable is shown in the following formula: Cognitive Complexity = zexcl + znegate + ztentat + zconj. This cognitive complexity construct provides a

Cronbach’s alpha of 0.73 in the present study.

CEO age. In the 10-K-Forms, which are part of companies’ annual reports, the age of the CEOs is stated (http://www.sec.gov/edgar/searchedgar/companysearch.html). The 10-K-Forms of the calendar year 2011 have been used.

Control variables. Because differences between the companies are likely to exist, this research controlled for several variables. In this way, the possibility that unmeasured

influences might bias the results has been reduced. The first control variable consisted of the industry sector of the different companies. The industry control variable consists of five different sectors, namely heavy industry (28 companies), light industry (19 companies), service (15 companies), finance / insurance (26 companies) and retail/wholesale (21 companies). The industry sectors of the companies were gathered from the company information in the Fortune 500 list

(http://money.cnn.com/magazines/fortune/fortune500/2011/full_list/).

Another control variable used is company size, which is measured by revenue in 2011. In 2011, generated revenues of the 109 companies showed a wide spread, namely between $486.85 and $423 Million. This financial information was obtained from the 10-K-Forms of the companies, which are part of companies’ annual reports, as pointed out above

(http://www.sec.gov/edgar/searchedgar/companysearch.html).

The last control variable is CEO gender. According to Tausczik and Pennebaker, (2010), there are sex differences in terms of language use. Therefore, I use this control

variable to be sure gender does not bias the results. Gender information was obtained from the additional information section in the 10-K-Forms, which are part of companies’ annual

13 Data-analysis

The hypotheses were tested by using moderated linear regression analysis and contained standardized variables (Aiken & West, 1991). The first step contains a regression of the control variables on CEOs’ past, present and future time orientations, respectively. In step two, age and cognitive complexity were inserted in order to test the main effects of age on past, present and future time orientation (Hypothesis 1a, 1b and 1c) and of cognitive

complexity on past, present and future time orientation (Hypothesis 2a, 2b and 2c). The third step contains the age X cognitive complexity interaction variable in order to test the

moderating effect of cognitive complexity on the relation between age and past, present and future time orientations (Hypothesis 3a, 3b and 3c).

RESULTS

Descriptive Statistics. Table 1 reflects the means, standard deviations, and bivariate correlations among the predictor variables, the control variables, and the dependent measures. The main effects of cognitive complexity on time orientation are significant for all three time orientations. Cognitive complexity is positively related to past time orientation (r = .35; p < 0.01), present time orientation (r = .58; p < 0.01) and future time orientation (r = .24; p < 0.05). No significant correlations have been found between age and past, present and future time orientation. Although not all control variables are significantly related to each other, most of them do correlate with one of the dependent measures. Therefore, the control variables have been included in the analyses.

Hypotheses testing. Results of the moderated hierarchical regressions are shown in Tables 2, 3 and 4. After taking the effects of the different control variables on the dependent variables into account, one can derive from Step 2 in Table 2 that, contrary to Hypothesis 1a, past time orientation is not affected by age (B = .05; p = n.s.). However, Table 2 shows that cognitive complexity is an important predictor of past time orientation (B = .33; p < .01), which resulted in a confirmation of Hypothesis 2a. Although it was expected that cognitive complexity would act as a moderator between age and past time orientation, adding the interaction term in Step 3 added no significant variance to the explained steps (B = .09; p = n.s.), which resulted in the rejection of Hypothesis 3a.

14 rejection of Hypothesis 1b. The table does show a significant main effect of cognitive

complexity on present time orientation (Beta = .57; p < .01), however, which confirms Hypothesis 2b. Adding the interaction term in Step 3 did not add significant value to the different steps (Beta = -.09; p = n.s.), which resulted in the rejection of Hypothesis 3b.

In order to test the effects on the dependent variable ‘future time orientation’, the effects of the control variables have first been taken into account. Step 2 in Table 4 shows that the main effect of age on future time orientation was not significant, which contradicts the expectations (Beta = -.11; p = n.s.). This results in a rejection of hypothesis 1c. However, Table 4 shows that there is a significant main effect of cognitive complexity on future time orientation (Beta = .25; p < .05), which results in the confirmation of Hypothesis 2c. Inserting the interaction term in Step 3 yielded a marginally significant coefficient (Beta = .20; p = 0.06), offering some support for Hypothesis 3c. Based on Aiken and West (1991) the interaction is graphically explored in Figure 2. In support of Hypothesis 3c, Figure 2

confirmed that having a high cognitive complexity will result in the maintenance of a future time orientation, i.e., cognitive complexity diminishes the negative effect age has on future time orientation. In contrast, when cognitive complexity is low, age is strongly negatively related with future time orientation.

DISCUSSION

The goal of this research was to get a more complete picture of the origins of leaders’ time

orientation. In particular, the purpose was to examine the influence of a CEO’s age and

cognitive complexity on his or her time orientation. The results show that age does not have a significant main effect on past, present or future time orientation, which does not reinforce previous research. However, cognitive complexity does have a significant positive main effect on past, present and future time orientation.

15 contrary to my suggestions, having a high level of cognitive complexity does not moderate the relationship between age and past and present orientation.

The unexpected findings can be explained by the range in CEOs’ age within the dataset of this study. In the Hypotheses Development section, hypotheses were drawn from Socioemotional Selectivity Theory (SST; Carstensen, 2006). The theory emphasizes the cognitive mechanism of perceived sense of time. Specifically, SST argues that age has an effect on the way people perceive time and therefore the way individuals are setting goals. However, the findings of these scholars are based on the whole lifespan of individuals. Their samples consist of people between the ages of 18 and 95 years old. In the present study, the respective CEOs ranged in age from 39 to 81 years, which can be seen as a narrow spread in comparison to the research of Carstensen et al. (1999). The reason why age did not have a significant effect on time orientation might be because of this lack of particularly young leaders.

Although there were unexpected findings in this research, the present study makes a contribution to the development of time-focused leadership theories in such a way that the research made clear that cognitive complexity is an important predictor of a leaders’ past, present and future time orientation. Moreover, the study reveals that cognitive complexity has such a strong influence, in that it acts as a moderator of the negative relationship between age and future time orientation. Since leaders with low cognitive complexity are less capable of imaging the future (Thoms, 2003), they are “victims of their age” when getting older. Leaders with high cognitive complexity in contrast, appear to conserve a stronger future time

orientation even as they get older, which may result in distinct benefits. Having a broad outlook on the future is needed, for example, in order to be an effective leader, according to Bennis and Nanus (1985) and Tichy and Devanna (1990). Nanus (1992) and Mead (1971) go a step beyond in stating that, in order to prepare for future challenges, leaders with planning

and predicting abilities have always been of great importance to human societies.The

findings regarding the strong influence of cognitive complexity on the relationship between age and time orientation will at least fill some of the gap that exists in the literature

concerning the origins of time orientation in a leadership context.

Limitations

16 case they have been instructed to write the letters on behalf of their superior or by other powerful actors within the company (Staw, McKechnie & Puffer, 1983). A Wall Street Journal (1982) article states that, although the shareholder letter only bears one or two signatures, it generally is a committee project. Although this has to be taken into account when reading this research, one should bear in mind that the CEO always has to put his or her signature on the bottom of the letter. Therefore, there is a high probability that the letters reflect the words, and thus the time orientation, a CEO would use. Also, I note that various studies have previously used content-analyzed shareholder letters in order to make inferences about CEO’s psychological states (e.g., McClelland et al., 2010).

A second limitation of this study was its correlation nature. Moreover, all gained data in this study were cross-sectional. Future research may use an experimental design to explore causal relations between age, cognitive complexity and time orientations.

Third, generalizability of this study is limited. It can be argued, for example, if

drawing on the word use of Fortune top 150 CEOs is generalizable to all leaders in the world. In addition, this study has been conducted in a dominantly individualistic culture (i.e., the USA; Hofstede, 1991). It could be that differences in time orientations exist between leaders from individualistic cultures and collective societies, because the latter is more focused on historical traditions, which might result in more people with a past time orientation (Levine and Wolff, 1985).

Finally, research of Staw (1980) shows that organizations with low performance focus more on past events in contrast to future events. This happens to be the case since efforts to justify performance may lead to a retrospective focusing as opposed to a prospective focusing. Taken this into account, it could be that the CEO also applies a ‘general’ look, i.e. the view of the company. This could have an effect on the usage of their basic time orientation. However, Thoms (2003) shows that people do not feel comfortable when using a different time

orientation than their own basic time orientation. It is therefore expected that, although low performing companies tend to focus more on past events, the CEO still uses a future time orientation if that is his or her basic time orientation.

Future Research Directions

In this paragraph I will identify various directions for future research. First, future research may focus on personal characteristic variables which can have an effect on time orientation, for example a leaders’ personality. Thoms (2003) argues that the more optimistic an

17

confidence in the feasibility of future goals.It might be expected that a high level of optimism

will result in a future time orientation.An interesting avenue for future research may therefore

be the exploration of personality as a predictor of future time orientation.

A second topic of future research may be the level of commitment of leaders to their organizations. When a leader is more committed, they are more concerned with the

achievement of their organizations’ goals (Moreland & Levine, 1985). Because this high level of commitment, leaders may be more motivated to assure the sustainability of the

organizations. Those leaders might be more engaged in setting goals for the long term, which may result in a time orientation towards the future. The level of commitment could therefore serve as a moderator between cognitive complexity and time orientation, which can be explored in future research.

Third, I propose that the level of power a leader has, may moderate the relationship between cognitive complexity and time orientation. According to Gruenfeld, Keltner and Anderson (2003), low- power individuals reason in more complex ways in comparison to high-power individuals, because low-power individuals are more concerned about the consequences of their own actions. High-power individuals are already in control of

resources, and do already possess power, which makes them less focused on their own actions and those of others. Lerner and Tetlock (1999) and (Tetlock, 1992) argue that this increased attention of low-power individuals to their own actions, tends to lead to a high level of cognitive complexity. Therefore, power might play a moderating role when focusing on the relationship between cognitive complexity and time orientation. However, it must be noted that leaders may already possess average to high levels of power due to their high position in organizations. Yet, I think it might be an interesting starting point for future research to focus on the different power levels of leaders when focusing on the relationship between cognitive complexity and time orientation.

18 case that leaders with a promotion focus are more thinking about future opportunities in comparison to leaders with a prevention focus, which could result in being more future time oriented. According to Lewin (1948), when people feel that goals are accessible, they are likely to embrace a future time orientation. On a different note, research shows that an individual’s regulatory focus changes across the lifespan (Lockwood, Chasteen & Wong, 2005; Ebner, Freund & Baltes, 2006). Older people are likely to have a relatively stronger prevention focus in comparison to younger people, who are likely to have a relatively stronger promotion focus. Although the present study does not find a significant main effect of age on time orientation, it might be interesting to examine if regulatory focus has a moderating effect on the relationship between age and time orientation.

A fifth starting point for further research may be the exploring of the influence of need for cognition between the relationship of cognitive complexity on time orientation. Need for

cognition is defined as “people’s tendency to engage in and enjoy effortful cognitive activity”

(Cacioppo, Petty, Feinstein & Jarvis, 1996, p. 198). These scholars argue that intrinsic cognitive needs result in more intense cognitive efforts. In other words, differences in the need for cognition demonstrate variance in the nature of one’s cognition and problem solving strategies. Therefore, a high need for cognition might serve as a moderating variable between the relationship of cognitive complexity on time orientation.

A sixth avenue for further research might be the exploration of leaders’ background in

relation to their time orientation. Although people have a basic, predominant time orientation which they prefer over the other, research of Bergadaà (1990), Thoms (2003) and Zimbardo and Boyd (1999) conceive time orientation as situational determined. The scholars state that overreliance of one particular time orientation is multiply determined by many learned factors, with educational, cultural, social class, religious, and family composition among the most prominent. For example, research shows that people from lower classes are more present oriented (LeShan, 1952). Research on the antecedents of time orientation could be extended by focusing on these social variables.

Finally, in this research came to the fore that cognitive complexity takes away the negative effect of age on future time orientation. Although there is some little research on

training of cognitive complexity (Suedfeld, Tetlock & Streufert, 1992), I recognize that for

19 Additionally, it would be beneficial for organizations to train leaders’ visioning abilities, in order to improve leaders’ outlook on the future. Although it is recognized by Thoms (2003) that training can result in better visioning “skills” of leaders, future research may extend this line of thought.

Practical Implications

This research showed that although age does not have a significant effect on leaders’ time orientation, cognitive complexity does. This study may provide HR departments and the management of organizations with the message that it could be very beneficial for companies to focus more on exploring the cognitive abilities of future CEOs, since it is an important predictor of future time orientation. Research of Jaques (1982) showed that if a person has a higher level of management in an organization, this individual also needs a broader time span of discretion. For example, production workers who are operating at the bottom level of a company’s hierarchy, only have to think about the tasks they have to complete in the current workday. In contrast, the supervisor of these production workers should have a broader time span, since he or she is concerned with thinking ahead to prepare work for the following weeks. The director that stands above him is responsible for the strategy and plans that will be made next year. At last, the CEO is considered with the direction of the company during the next 10 to 20 years (Jaques, 1982). While facing future challenges, companies need leaders with a high level of cognitive complexity who can foster a company’s longevity due to their orientation towards the future. Again, this shows the importance of a good recruitment and selection strategy for HR departments. The findings of this research might help the

recruitment and selection departments of organizations to better underpin their decisions in the hiring process of new leaders. As a result, alignment with the particular situation of an organization will be reached easier.

ACKNOWLEDGEMENTS

The author wants to thank her supervisor F. Walter for his helpful comments, constructive

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REFERENCES

Abe, J. A. (2011). Changes in Alan Greenspan’s language use across the economic cycle: A text-analysis of his testimonies and speeches. Journal of Language and Social Psychology, 30, 212-223.

Aiken, L.S. & West, S.G. (1991). Multiple Regressions: testing and interpreting interactions.

Newbury Park, CA: Sage.

Bass, B. M. (1985). Leadership and Performance beyond Expectations. New York: Free Press.

Bennis, W. (1989). On Becoming a Leader. Reading, Mass.: Addison-Wesley.

Bennis, W. & Nanus, B. (1985). Leaders: The strategies for taking charge. New York: Harper & Row.

Bergadaà, M. M. (1990). The Role of Time in the Action of the Consumer. Journal of Consumer Research, 17, 289–302.

Burleson, B. R., & Caplan, S. E. (in press). Cognitive complexity. In J. C. Daly, & M. M. Martin (Eds)., Communication and personality: Trait perspectives. Cresskill, NJ: Hampton Press.

Burns, J. M. (1978). Leadership. New York: HarperCollins.

Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: the life and times of individuals varying in need for cognition. Psychological Bulletin, 119(2), 197–253.

Carstensen, L. L., Isaacowitz, D. M., & Charles, S. T. (1999). Taking time seriously: A theory of socioemotional selectivity. American Psychologist, 54, 165–181.

Carstensen, L. L., & Lang, F. (1997). Measurement of time orientation in diverse population. Unpublished data, Stanford University, Stanford, CA.

Carstensen, L.L. (2006). The influence of a sense of time on human development. Science, 312, 1913-1915.

21 Danner, D. D., Snowdon, D. A., & Friesen, W. V. (2001). Positive emotions in early life and

longevity: Findings from the nun study. Journal of Personality and Social Psychology, 80, 804–813.

Ebner, N.C., Freund, A.M., & Baltes, P.B. (2006). Developmental changes in personal goal orientation from young to late adulthood: From striving for gains to maintenance and prevention of losses. Psychology and Aging, 21, 664-678.

Fingerman, K., & Perlmutter, M. (1995). Future time perspective and life events across

adulthood. Journal of General Psychology, 122, 95-111.

George, J.M., & Jones, G.R. (2000). The Role of Time in Theory and Theory Building. Journal of Management, 26(4), 657-684.

Gunsch, M. A., Brownlow, S., Haynes, S. E., & Mabe, Z. (2000). Differential linguistic content of various forms of political advertising. Journal of Broadcasting & Electronic Media, 44, 27-42.

Graesser, A. C., McNamara, D. S., Louwerse, M. M., & Cai, Z. (2004). Coh-metrix: analysis of text on cohesion and language. Behavior Research Methods, Instruments, & Computers, 36, 193-202.

Gruenfeld, H., Keltner, D. & Anderson, C. (2003). Power, Approach and Inhibition. Psychologial Review, 110(2), 265–284.

Heneman, H.G., Judge, T.A, & Heneman, R.L. (2000). Staffing Organizations. Middleton: Irwin/McGraw-Hill.

Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52, 1280–1300.

Hofstede, G. (1991). Cultures and Organizations: Software of the Mind. London: McGraw Hill.

Holbrook, M. B. (1993). Nostalgia and Consumption Preferences: Some Emerging Patterns of Consumer Tastes. Journal of Consumer Research, 20, 245–256.

Jaques, E. (1982). The Form of Time. New York: Crane, Russak, & Co.

Karande, K. & Merchant, A. (2012). The impact of time and planning orientation on an individual’s recreational shopper identity and shopping behavior. Journal of Marketing Theory and Practice, 1, 59-72.

22 Levine, R.V., & Wolff, E. (1985). Social time: the heartbeat of culture. Psychology Today

28–35.

Lerner, J. S., & Tetlock, P. E. (1999). Accounting for the effects of accountability. Psychological Bulletin, 125, 255–275.

LeShan, L. (1952). Time orientation and social class. Journal of Abnormal and Social Psychology, 47, 589-592.

Lewin, K. (1948). Resolving Social Conflicts. New York: Harper & Row.

Lockwood, P., Chasteen, A.L., & Wong, C. (2005). Age and regulatory focus determine preferences for health-related role models. Psychology and Aging, 23, 376-389.

McClelland, P.L, Liang, X. & Barker III, V.L. (2010). CEO Commitment to the Status Quo: Replication and Extension using Content Analysis. Journal of Management, 36(5): 1251-1277.

Mead, M. (1971). Toward more vivid utopias. In G. Kateb (Ed.), Utopia. New York: Atherton.

Mintzberg, H. (1994). The rise and fall of strategic planning. New York: Free Press.

Moreland, R. & J. Levine. (1985). Socialization in Small Groups: Temporal Changes in Individual-Group Relations. In L. Berkowitz (ed.) Advances in Experimental Social Psychology. New York: Academic Press.

Muis, K. R., Bendixen, L. D., & Haerle, F. C. (2006). Domain-generality and

domain-specificity in personal epistemology research: Philosophical and empirical reflections in the development of a theoretical framework. Educational Psychology Review, 18, 3–54.

Murrell, A. J. & Mingrone, M. (1994). Correlates of Temporal Perspective. Perceptual and Motor Skills, 78, 1331–1334.

Nanus, B. (1992). Visionary Leadership: Creating a Compelling Sense of Direction for Your Organization. San Francisco: Jossey-Bass.

Pennebaker, J. W., & Stone, L. D. (2003). Words of wisdom: Language use over the life span. Journal of Personality and Social Psychology, 85, 291-301.

23 Schriber, J.B. & Gutek, B.A. (1987). Some Time Dimensions of Work: Measurement of an

Underlying Aspect of Organization Culture. Journal of Applied Psychology, 72, 642-650.

Shamir, B. (2011). Leadership takes time: Some implications of (not) taking time seriously in leadership research. The Leadership Quarterly, 22, 307-315.

Smith, A. D. (1996). Memory. In J. Birren, & W. Schaie (Eds.), Handbook of the psychology of aging. San Diego, CA: Academic Press.

Souder, D. & Bromiley, P. (2011). Explaining Temporal Orientation: Evidence from the durability of fims’ capital investments. Strategic Management Journal, 33, 550-569.

Staw, B.M., McKechnie, P.I. & Puffer, S.M. (1983). The Justification of Organizational Performance. Administrative Science Quarterly, 28, 582-600.

Strathman, A., Gleicher, F., Boninger, D. S. & Edwards, C. S. (1994). The Consideration of Future Consequences; Weighing Immediate and Distant Outcomes of Behavior. Journal of Personality and Abnormal Psychology, 66, 742–752.

Suedfeld, P., Tetlock, P. E., & Streufert, S. (1992). Conceptual/integrative complexity. In C.P. Smith (Ed.), Motivation and personality: Handbook of thematic content analysis. Cambridge:

Cambridge University press.

Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24-54.

Tetlock, P. E. (1981). Pre- to post-election shifts in presidential rhetoric: Impression

management or cognitive adjustment. Journal of Personality and Social Psychology, 41, 207-212.

Tetlock, P. E. (1992). The impact of accountability on judgment and choice: Toward a social contingency model. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 25, pp. 331–376). New York: Academic Press.

Thoms, P. (2003). Driven by time: time orientation and leadership. Preager.

Thoms, P. & Greenberger, D.B. (1995). The relationship between leadership and time orientation. Journal of Management Inquiry, 4, 272-292.

24 Tichy, N.M., & Devanna, M.A. (1990). The transformational leader. New York: Wiley.

Wallace, M. & Rabin, A. I. (1969). Temporal Experience. Psychological Bulletin, 57, 213-36.

Wall Street Journal (1982). Double talk grips business reports as firms try to sugarcoat bad news. March 31: 29.

Wheatley, M.J. (1992). Leadership and the New Science. San Francisco: Berrett-Koehler

Yukl, G. (2002). Leadership in Organizations, 5th ed. Upper Saddle River, N.J.: Prentice Hall.

25

TABLE 1

Descriptive Statistics and Pearson Zero-Order Correlations Among the Study Variables

26

TABLE 1 - continued

Descriptive Statistics and Pearson Zero-Order Correlations Among the Study Variables

27

TABLE 2

Moderated Hierarchical Regression Analysis of Past Time Orientation

Predictor Step 1 Step 2 Step 3

B SE B SE B SE Controls Heavy Industry -.07 .16 -.04 .15 -.03 .16 Light Industry .06 .18 .04 .18 .05 .18 Service .10 .20 .04 .19 .05 .19 Finance / Insurance .10 .16 .02 .16 .02 .16 Gender .02 .25 -.00 .23 .00 .24 Company size -.26** .06 -.26** .05 -.26** .05 Main effects Age .05 .05 .02 .06 Cognitive Complexity .33** .02 .32** .02 Two-way interaction Age * Cognitive Complexity .09 .02 R2 .11 .21 .22 ∆R2 .11 .10** .01

NOTE: n = 109. Standardized regression weights are shown. ** p < 0.01

28

TABLE 3

Moderated Hierarchical Regression Analysis of Present Time Orientation

Predictor Step 1 Step 2 Step 3

B SE B SE B SE Controls Heavy Industry -.14 .44 -.07 .37 -.08 .38 Light Industry -.06 .51 -.09 .42 -.09 .42 Service .08 .55 -.01 .46 -.01 .46 Finance / Insurance .19 .45 .07 .38 .07 .38 Gender .11 .68 .06 .57 .05 .57 Company size .02 .17 .02 .13 .02 .13 Main effects Age -.11 .13 -.08 .14 Cognitive Complexity .57** .05 .57** .05 Two-way interaction Age * Cognitive Complexity -.07 .04 R2 .09 .39 .39 ∆R2 .09 .30** .00

NOTE: n = 109. Standardized regression weights are shown. ** p < 0.01

29

TABLE 4

Moderated Hierarchical Regression Analysis of Future Time Orientation

Predictor Step 1 Step 2 Step 3

B SE B SE B SE Controls Heavy Industry -.06 .12 -.03 .12 -.01 .12 Light Industry -.01 .14 -.02 .13 -.01 .13 Service -.01 .15 -.05 .14 -.03 .14 Finance / Insurance -.06 .12 -.12 .12 -.12 .12 Gender .03 .18 .01 .18 .02 .18 Company size .01 .04 .01 .04 .01 .04 Main effects Age -.11 .04 -.10 .04 Cognitive Complexity .25* .02 .23* .02 Two-way interaction Age * Cognitive Complexity .20^ .01 R2 .01 .06 .10 ∆R2 .01 .06

^

^

NOTE: n = 109. Standardized regression weights are shown. ** p < 0.01

30 FIGURE 1 Conceptual model CEO Cognitive Complexity

CEO Age CEO Time

31 FIGURE 2