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Long-Term Focus and Enterprise Strategy

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27 August 2024

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28 August 2024

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Abstract
This research investigates the relationship between the long-term orientation of top managers and their strategic decision-making processes, emphasising the comprehensiveness, speed, and inventiveness of decisions. The study employed structural equation modelling to look into the suggested connections using a sample of CEOs. The findings demonstrated a high and positive correlation between long-term orientation and decision comprehensiveness and speed, but not for decision creativity. The study contributes to the growing body of research on how human variations, such as temporal orientation, affect business outcomes and strategic decision-making. Moreover, the fact that a number of the results differed from those of other studies carried out in other contexts highlights how important it is to consider the industry and cultural environment when assessing these connections.
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Subject: Business, Economics and Management  -   Business and Management

Introduction

Lately, the temporal orientation of senior executives has drawn a lot of attention from academics and researchers that study strategy. This expanding interest stems from a growing understanding of the critical role that temporal orientation plays in influencing strategic decision-making processes. The concept of temporal orientation refers to a person's inclination to concentrate on the past, present, or future. Psychological research has shown that this orientation differs amongst people and is correlated with a range of life outcomes, such as risk-taking behaviours and academic achievement. This shows up as discrepancies in the way leaders prioritise past vs future factors in a managerial setting. Studies indicate that the temporal orientation of managers strongly influences their strategic decision-making. For example, a recent Chinese study discovered that top managers' strategic decision-making was positively influenced by a long-term orientation in a number of areas.
The current study aims to address this by investigating the relationship between long-term orientation and strategic decision-making processes in a novel cultural business context. Understanding this relationship is crucial given its potential impact on firm performance and outcomes. Moreover, evidence suggests that long-term orientation varies across cultures, emphasizing the need for cross-cultural research in this area.
This study contributes to the literature in several ways. While previous research in the private sector has examined long-term orientation's relationship to firm performance, few have investigated the underlying temporal value system and its relationship with strategic decision-making processes. Moreover, there is no such research in the in the publoic sector. By exploring this relationship among public sector top managers, this study expands the existing research literature. Furthermore, top managers in the public sector increasingly face strategic decisions in a new world order characterised by great uncertainty with call for privatisation. In such environments, managers may rely more heavily on internal value systems to guide their choices. Temporal orientation, particularly long-term orientation, may be one such value system. Understanding how temporal orientation relates to strategic decision-making processes can increase awareness of the factors influencing these processes and their potential implications.

Methods

This study employed structural equation modelling (SEM) to investigate the relationship between long-term orientation and decision-making comprehensiveness, speed, and creativity. SEM allows for the modelling of both latent and measured variables and their relationships. CEOs were contacted via email, phone, or LinkedIn to participate in the study. They received background information and a consent form through a link, ensuring anonymity and confidentiality to promote honest responses. The informed consent form did not mention long-term orientation to avoid biasing responses. Post-survey, participants were debriefed about the study's hypotheses and provided with researcher contact information.
Required sample size is based on a statistical power of .80, significance level of .05, and effect size of .30. The study achieved a 21% response rate, yielding 86 completed questionnaires. This response rate is considered acceptable for CEO-focused research, where smaller rates are common. Given the clearly defined and relatively small population (N = 401), this sample size was deemed adequate. Moreover, structural equation modelling can provide sufficient power with at least 80 participants.
Each company was represented by one respondent. While some studies use two respondents per company, high interrater reliability suggests that multiple respondents may not significantly improve data quality. The participating companies ranged from 19 to 21,000 employees, with an average of 624.
The study used several measures:
1. Decision comprehensiveness: Five items from Lin et al. (2019), measured on a 7-point Likert scale. Cronbach's α was 0.85.
2. Decision creativity: Three items from Menon et al. (1999) and Lin et al. (2019), measured on a 7-point Likert scale. Cronbach's α was 0.80.
3. Decision speed: Three items from Baum & Wally (2003) and Lin et al. (2019), measured on a 7-point Likert scale. Cronbach's α was 0.69.
4. Long-term orientation: Three items from Lin et al. (2019), measured on a 7-point Likert scale. Cronbach's α was 0.73.
Due to anonymity assurances, direct comparison of responding and non-responding CEOs was not possible. Instead, the sample was split based on response timing. An independent samples t-test showed no significant differences between early and late respondents, suggesting limited systematic bias. To address potential common method variance in the cross-sectional, self-reported data, several steps were taken. These included careful wording of scale items, guaranteeing anonymity and confidentiality and conducting a confirmatory factor analysis and Harman's one-factor test. The results indicated that common method bias was not a significant concern in this study.

Results

To examine the hypotheses, the study employed structural equation modelling (SEM), a sophisticated statistical technique that has gained prominence in social sciences and behavioral research. This method was chosen for its ability to assess multiple variable relationships concurrently, offering greater flexibility compared to more traditional approaches like regression analysis. SEM's capacity to handle complex, multivariate models made it particularly suitable for this study, which aimed to investigate intricate relationships between various constructs.
Following best practices in SEM, the research team first developed and validated an appropriate measurement model before proceeding to the structural model. This two-step approach is crucial in ensuring the reliability and validity of the constructs being measured. The measurement model focuses on the relationships between observed variables and their underlying latent constructs, while the structural model examines the hypothesized relationships between these latent constructs.
All statistical analyses were conducted using a significance threshold of p ≤ .05, a commonly accepted level in social science research. This threshold helps to minimize the likelihood of Type I errors, where false positives might be accepted. The initial step involved a confirmatory factor analysis (CFA) to evaluate the latent factors within the measurement model. CFA is a powerful tool that allows researchers to test how well measured variables represent the number of constructs, providing a rigorous assessment of construct validity. Furthermore, SEM's ability to handle latent variables – theoretical constructs that cannot be directly observed – makes it particularly valuable in psychological and social research where many key concepts are not directly measurable. This feature of SEM allows researchers to model abstract concepts and test theoretical models in a way that more closely aligns with the complex nature of human behavior and social phenomena.
The results, presented in Table 1, demonstrate that each item showed significant loading onto its intended factor. This finding is crucial as it supports the construct validity of the measures used in the study. Factor loadings indicate the strength of the relationship between each observed variable and its underlying latent construct. Significant loadings suggest that the observed variables are indeed good indicators of the constructs they are intended to measure.
The measured scales demonstrated internal consistency with Cronbach's alphas ranging from acceptable to very good. The measurement model showed a good fit to the data, as indicated by the goodness of fit indices: χ2(71) = 98.473, p = .017, CFI = .94, RMSEA = .067. To further assess measure reliability, composite reliability and average variance extracted (AVE) were calculated. All measures exhibited composite reliability above .6 (minimum .71), suggesting acceptable internal reliability. AVE, considered a more stringent measure of internal reliability than composite reliability, exceeded the recommended .5 threshold for all but one measure (decision-making speed, AVE = .46). However, composite reliability can still accurately indicate convergent validity even when AVE falls below 0.5.
After confirming the measurement model's acceptable fit, hypothesis testing was conducted using the structural model. This model also demonstrated good fit to the data: χ2(216) = 286.707, p = .001, CFI = .91, RMSEA = .062. The model incorporated covariances between several variable pairs (DCRE2 and DCRE3, DCOM5 and DCRE1, LTON3 and DSPD3, LTON1 and LTON3, and DCOM1 and DSPD2). Results revealed that long-term orientation was significantly related to decision-making comprehensiveness (β = .252, p = .048) and decision-making speed (β = .344, p = .025), but not to decision-making creativity (β = .247, p = .100). These findings support hypotheses 1 and 2, which proposed positive correlations between long-term orientation and decision-making comprehensiveness and speed, respectively. However, the results do not support hypothesis 3, which suggested a positive correlation between long-term orientation and decision-making creativity. Table 2 presents a summary of these hypotheses and their outcomes.
The analysis of the hypotheses revealed varying relationships between long-term orientation and different aspects of decision-making. While long-term orientation did not show a significant correlation with decision-making creativity (β = .247, p = .100), it demonstrated significant positive relationships with decision-making comprehensiveness (β = .252, p = .048) and decision-making speed (β = .344, p = .025). These findings provide support for hypotheses 1 and 2, which proposed positive correlations between long-term orientation and decision-making comprehensiveness and speed, respectively. However, the results fail to support hypothesis 3, which suggested a positive correlation between long-term orientation and decision-making creativity. In summary, the model indicates that managers with a long-term orientation tend to exhibit greater comprehensiveness and speed in their decision-making processes, but this orientation does not appear to significantly influence their decision-making creativity.

Discussion

The current study aimed to explore the connection between long-term orientation and strategic decision-making processes among top managers. The findings revealed significant relationships between long-term orientation and both decision-making comprehensiveness and speed, but not with decision-making creativity. The reason for this discrepancy is not immediately clear. The alignment between managers' temporal orientation and the external business environment, including cultural factors, can strengthen the relationship between long-term orientation and strategic decision-making processes.
This research contributes to the literature in several ways. It adds to the growing body of work examining managers' individual differences and their impact on firm performance. By directly surveying CEOs, it offers insights into the black box mechanisms connecting manager characteristics to firm outcomes. While strategic decision-making processes are not equivalent to firm outcomes, they provide valuable insight into how top managers make decisions that can significantly impact their companies. The use of structural equation modelling allowed for simultaneous testing of relationships between long-term orientation and decision comprehensiveness, speed, and creativity. This approach offers advantages over more traditional methods like regression analysis, as it can model both latent and measured variables.
From a theoretical standpoint, this study contributes to research highlighting temporal orientation as a crucial factor in strategic decision-making processes and outcomes in the pubic sector. It helps bridge the gap between micro-level factors and macro-level organisational phenomena. Top managers' temporal orientation may serve as a key micro-level variable influencing macro-level structures through strategic decision-making processes, potentially offering insights into the antecedents of these processes. The cultural context plays a significant role in the relationship between long-term orientation and strategic decision-making processes. National cultural context may exert a stronger influence on the external business environment than organisational cultures or industry contexts. This implies that national cultures should be considered a key part of the external business environment when studying firm behaviours and performance.
Regarding practical implications, the results suggest that long-term orientation may be related to certain strategic decision-making processes (comprehensiveness and speed) among public sector top managers. This could be a consideration in CEO recruitment for companies interested in these decision-making aspects. Long-term and short-term orientations might also relate to organisational ambidexterity, with long-term oriented managers potentially having a more explorative mindset for new markets, while short-term oriented managers might focus more on exploiting current capabilities.
However, it's crucial to note that while long-term orientation showed significant relationships with decision comprehensiveness and speed, it did not with decision creativity. This suggests that long-term orientation is just one of many factors to consider in CEO recruitment. Previous research has examined other factors such as top managers' narcissism and self-evaluation as predictors of firm performance, indicating that multiple variables should be considered when determining the fit between a CEO and a public enterprise.

Limitations and Directions for Future Research

The study's focus on top managers from specific countries restricts the applicability of its findings to significantly different cultural contexts. Cultures and languages vary in their temporal focus during communication, potentially affecting behaviours related to savings and health. This underscores the importance of cautiously applying results from one cultural setting to another. Additionally, the relatively small sample size of the current study may have limited its ability to detect subtle effects between long-term orientation and strategic decision-making processes among CEOs. Consequently, future research with larger samples would be beneficial to more accurately assess this relationship. Moreover, the cross-sectional nature of the study precludes drawing causal inferences between long-term orientation and top managers' strategic decision-making processes. This leaves open the possibility that other unexplored variables might contribute to the observed results. While structural equation modelling may have aided in establishing a causal model for the data, it's possible that some causal relationships were not captured by the model.
Future research directions should prioritise validating the current findings across diverse cultural contexts to determine if similar relationships exist in other cultures. Additionally, conducting experimental studies could help establish clearer causal links between long-term orientation and decision comprehensiveness, speed, and creativity. One approach could involve experimentally manipulating long-term orientation, such as by priming an experimental group of managers with a long-term mindset. The public sector management field would generally benefit from more experimental research. Another avenue for future research could explore potential gender differences in the relationship between long-term orientation and decision-making processes. The current study's results have limited generalisability beyond male top managers, representing a significant limitation because the role of mid-level managers in the public sector is very important.

Conclusions

This research aimed to explore the connection between long-term orientation and various strategic decision-making processes among top managers in the public sector, focusing on decision comprehensiveness, speed, and creativity. The findings revealed a significant positive correlation between long-term orientation and both decision-making comprehensiveness and speed. However, no significant relationship was found between long-term orientation and decision-making creativity. These results indicate that while long-term orientation is associated with certain key decision-making processes, it is likely just one of many factors influencing the strategic decision-making of public sector top managers. This underscores the necessity of incorporating a cultural business perspective to gain a comprehensive understanding of the factors shaping top managers' strategic decision-making processes. The study's outcomes suggest that a more holistic approach, considering multiple variables including cultural context, is crucial for fully grasping the complexities of strategic decision-making in public sector management.

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Table 1. Scale items with factor loadings.
Table 1. Scale items with factor loadings.
Factors and items Loadings SE
Long-term orientation (α = .72)
LTON1 .61 .18
LTON2 1.2 .19
LTON3 .52 .18
Decision comprehensiveness (α = .85)
DCOM1 .68 .16
DCOM2 .75 .16
DCOM3 .74 .14
DCOM4 .86 .14
DCOM5 .67 .16
Decision creativity (α = .80)
DCRE1 .73 .20
DCRE2 .61 .22
DCRE3 .57 .21
Decision speed (α = .69)
DSPD1 .70 .17
DSPD2 .73 .21
DSPD3 .55 .14
Note. LTON=Long-term orientation, DCOM=Decision comprehensiveness, DCRE=Decision creativity, DSPD=Decision speed. All p-values ***<.001
Table 2. Test of hypotheses with standardised path estimates.
Table 2. Test of hypotheses with standardised path estimates.
Hypothesis Relationship Standardised estimate p-value Support
H1 Direct effect LTON->Decision comprehensiveness .251 .047* Yes
H2 Direct effect LTON->Decision speed .344 .025* Yes
H3 Direct effect LTON->Decision creativity .247 .101 No
*<.05
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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