Quantitative Research Methods: Exploring Data in Search of Meaning

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  1. Background
  2. Objective(s) and Hypotheses
  3. Methods
  4. Findings
  5. Discussion
  6. Conclusion
  7. References

Background

Differences between the productivity of various firms across the world have puzzled economists over time. For instance, studies on the productivity of US firms have shown discrepancies of up to four times per employee between different firms in the same country (Bloom & Van Reenen 2007). However, only 50% of these divergences could be explained by differences in inputs such as productivity, even within homogeneous manufacturing industries. The observed variations in productivity mainly persisted over time (Bloom & Van Reenen 2010). American firms have gained a reputation for being best-managed companies in the world (Bloom and Van Reenen 2007; Keegan 2017). Examples of American model firms include Apple, McDonald’s, IBM and Walmart, among many others. Furthermore, US business schools that educate top-level executives of these companies rate highly in global rankings (Agnew et al. 2016). Nonetheless, Japanese companies were considered the best companies in the 1980s because of adherence to lean manufacturing tenets associated with Toyota (Coetzee et al. 2016; Chiarini et al. 2018).

Consequently, economic research has focused on digging into these productivity discrepancies by investigating different measures of inputs, including skills, materials and capital. Additional studies on the area have revealed that productivity is influenced by other factors such as advances in technology as well as research and development (Su et al. 2018; Andrews et al. 2019). Nevertheless, focusing on these factors does not explain the discrepancies in productivity comprehensively. Therefore, an understanding of how management practices differ from one country to another as well as relationships between the execution of different factors of production from a managerial perspective could help to clarify some of these differences. A number of global surveys have been done to collect data on these factors from different countries (Edwards et al. 2016; Bloom et al. 2019). A notable investigation is the World Management Survey (WMS), which tries to explain variations in management practices across firms and countries (World Management Survey 2015). The WMS survey yielded a lot of data whose mathematical and statistical analysis can reveal relationships, patterns and trends of management practices. The purpose of this paper is to use statistical evaluations to explore the WMS data and make meaning out of it regarding management practices across all the surveyed countries. The variables to be analysed were chosen randomly

Objective(s) and Hypotheses

The overall goal of the study is to compare how management practices vary across different countries and firms with a focus on employment patterns and monitoring of performance. This goal was attained through three main objectives. The first objective was to determine the correlation between different management factors (operations, target, monitoring and people) and overall management scores. The second objective was to determine differences in employment patterns across countries, whereas the third goal was to determine the relationship between different employment patterns and monitoring.

Hypotheses

Null hypotheses were developed based on each objective as follows:

Correlation factors and management scores

H0: There is no association between management factors of operations, target, monitoring and people and the average management scores.

Employment patterns across countries

H0: There is no relationship between employment patterns across different countries.

Employment patterns and monitoring

H0: There is no relationship between employment patterns and monitoring scores.

Methods

The World Management Survey was conducted to investigate and explain differences in management practices across different firms and sectors in 35 countries. A double-blind survey tool was used to collect data from samples that were arbitrarily obtained from firms across different industries and countries. Open-ended questions were used to obtain precise answers concerning the characteristics of managerial procedures in each firm (Appendix A). Additional details regarding the methods are explained by Bloom and Van Reenen (2007). The entire dataset was downloaded from www.worldmamangementsurvey.org. Descriptive statistics were performed to summarize the average management scores across countries. Pearson correlation analysis was done to determine the relationship between different management factors (operations, target, monitoring and people) and overall management scores. A Chi-square test was done to determine the relationship between employment bands and different countries. Finally, a factorial ANOVA was done to determine the influence of different employment bands on monitoring. All statistical analyses were done using IBM’s Statistical Package for Social Sciences (SPSS) version 23 at 0.05 level of significance.

Findings

A total of 11702 observations were made. The lowest score was 1.000, whereas the highest score was 4.889. The mean management score was 2.882 ± 0.006037. Table 1 (Appendix A) summarizes the descriptive statistics, whereas Table 2 (Appendix A) represents the case summaries for each country. The US had the highest mean score of 3.28469 ± 0.019837, while the lowest management score was recorded in Mozambique with 2.01817 ± 0.059249. The findings of the Pearson correlation (Table 3, Appendix A) showed that there was a significant positive association between the average management scores and all management factors: operations (r(11689) = 0.764, p = 0.000), target (r(11701) = 0.903, p = 0.000), monitoring (r(11702) = 0.910, p = 0.000) and people (r(11700) = 0.842, p = 0.000). A chi-square test of independence was done to examine the relationship between employment patterns across different countries. The relation between these variables was significant, X2 (136, N = 11702) = 1826.033, p = 0.000 (Table 4, Appendix A).

Various countries adopted significantly different employment patterns. There were statistically significant differences between employment patterns and monitoring scores as determined by factorial ANOVA (F(1,4) = 465.944, p = 0.000)). Post-hoc tests (Tukey HSD) showed that the differences existed between all the five employment bands (Table 6, Appendix A). Table 8 summarizes the ranking of different countries based on their performance scores from the highest to the lowest as well as the proportions of different employment bands. The predominant employment band for most countries was B (101 to 250). This trend applied to the top 5 countries in terms of average management scores except for Japan, whose predominant band was C (251 to 500). Only Singapore and China had the band E dominating their employment.

Table 8: Performance scores ranking and employment band for different countries. The predominant employment band is highlighted.

Employment Bands
Rank Country Mean A) 50 to 100 B) 101 to 250 C) 251 to 500 D) 501 to 1000 E) 1000+
1 United States 3.28469 8.10% 31.20% 17.90% 14.90% 27.90%
2 Germany 3.17822 5.10% 29.80% 20.90% 20.50% 23.70%
3 Sweden 3.16639 7.80% 38.40% 29.10% 14.00% 10.90%
4 Japan 3.16465 0.80% 35.40% 37.80% 17.30% 8.70%
5 Canada 3.14259 12.70% 39.70% 16.50% 13.40% 17.70%
6 France 2.99921 10.40% 37.60% 20.20% 14.90% 16.90%
7 Australia 2.9984 13.70% 32.80% 17.30% 14.00% 22.20%
8 Great Britain 2.9926 8.30% 39.80% 25.40% 11.90% 14.50%
9 Singapore 2.95375 25.40% 23.90% 12.30% 7.40% 31.00%
10 Italy 2.94669 13.30% 46.20% 21.10% 7.80% 11.70%
11 Mexico 2.88563 8.40% 33.50% 22.40% 17.00% 18.70%
12 Poland 2.87763 5.50% 46.60% 23.10% 16.40% 8.40%
13 New Zealand 2.85133 35.30% 31.30% 21.30% 8.70% 3.30%
14 Northern Ireland 2.808 30.30% 33.60% 10.90% 9.20% 16.00%
15 Portugal 2.76937 21.80% 45.60% 18.10% 7.80% 6.70%
16 Republic of Ireland 2.76639 40.40% 23.60% 18.60% 4.30% 13.00%
17 Spain 2.74844 24.30% 47.20% 18.70% 6.50% 3.30%
18 Turkey 2.70566 24.40% 43.70% 18.70% 8.70% 4.50%
19 Chile 2.70361 18.00% 39.00% 22.00% 10.00% 11.00%
20 Greece 2.69299 26.40% 41.80% 17.80% 8.40% 5.50%
21 Argentina 2.68206 8.70% 42.20% 27.50% 11.80% 9.90%
22 Brazil 2.66126 8.60% 36.40% 25.20% 15.40% 14.50%
23 China 2.64538 13.50% 11.70% 25.20% 24.20% 25.40%
24 Vietnam 2.60771 26.50% 32.50% 18.50% 11.90% 10.60%
25 Colombia 2.57778 30.60% 36.50% 15.30% 8.80% 8.80%
26 Kenya 2.54258 24.20% 37.40% 22.00% 5.50% 11.00%
27 India 2.52701 26.60% 24.60% 18.40% 15.90% 14.50%
28 Nigeria 2.48645 63.10% 20.70% 6.30% 2.70% 7.20%
29 Nicaragua 2.42725 24.10% 26.50% 14.50% 22.90% 12.00%
30 Myanmar 2.37104 27.90% 25.20% 20.40% 13.60% 12.90%
31 Zambia 2.31923 43.10% 26.20% 21.50% 3.10% 6.20%
32 Tanzania 2.25192 34.20% 28.80% 17.80% 11.60% 7.50%
33 Ghana 2.22754 34.70% 36.70% 20.40% 3.10% 5.10%
34 Ethiopia 2.22392 20.60% 38.90% 22.90% 9.90% 7.60%
35 Mozambique 2.01817 46.70% 28.00% 11.20% 4.70% 9.30%

Discussion

The main goal of the study was to compare how management practices vary across different countries and firms with a focus on employment patterns and monitoring of performance. The outcomes of the descriptive statistics showed that the US had the highest average management scores followed by Germany, Sweden, Japan and Canada. These observations align with observations that US firms have some of the best management practices globally (Bloom et al. 2019; Sroufe and Gopalakrishna-Remani 2019). The country with the lowest average management scores was Mozambique.

The results showed that there was a strong positive association between management factors of operations, setting targets, monitoring and people (employee skills and talents) and the average management scores. Therefore, the null hypothesis was refuted. People factors that contributed to effective management include instilling a talent mindset, building a high-performance culture, making room for talent, nurturing talent, creating a distinctive EVP and retaining talent. Attracting and retaining talent is one of the most challenging things in human resource management (Isac 2016; Tysiac 2016; Pandita and Ray 2018). Identifying talent and nurturing it enhances employee engagement and leads to job satisfaction. As a result, employees are committed to their work and are less likely to leave. Therefore, managers should aim at synchronising talent management initiatives with employee engagement to realize the best outcomes (Pandita and Ray 2018). Conversely, monitoring practices in management entail documenting processes, tracking performance, reviewing performance, having performance dialogues and handling every repercussion appropriately (consequence management). Heavy investment in workforce wellbeing is one of the numerous practices done by well-managed companies with high productivity (Grossmeier et al. 2016; Nisar et al. 2019).

When setting targets or goals in an organization, the most important factors to consider include the type of targets, the relationship between goals (‘interconnection of goals’), time limits for attaining each goal (‘time horizon’), the flexibility of targets based on prevailing circumstances (‘goals are stretching’) and clarity of goals and measurement.

In the context of the survey, operations include ‘introduction to lean (modern) manufacturing’ and ‘rationale for lean (modern) manufacturing’. A manager should also ensure that realistic goals are set. Nonetheless, all these efforts may become fruitless if specific timelines for the attainment of targets are not established (Aranda et al. 2017; Jung 2018).

These outcomes confirm the assertions put forth by Bender et al. (2016) that observed cross-firm discrepancies in productivity are attributed to the application of advanced management practices, including goal setting, monitoring and the use of inducements. However, these practices often correspond to the skills of the employees. Consequently, firms with excellent management capabilities tend to recruit workers with high qualifications and use attractive compensation schemes to retain these employees (Bloom et al. 2017).

There was a statistically significant relationship between employment patterns as marked by employment bands across countries, which nullified the null hypothesis. These results showed that employment patterns had a significant influence on monitoring scores. The key monitoring practices in an enterprise include documentation of processes, tracking performance, reviewing performance, having performance dialogues and consequence management. The numbers of employees in a firm determine how effectively these practices can be done. Generally, it is easier to manage a few employees than many workers. However, this does not mean that the performance of many workers cannot be monitored. Electronic monitoring systems make it easier for managers to keep track of the performance of their employees (Fusi and Feeney 2018; Jiang 2019). However, leaders should ensure that the monitoring techniques do not infringe on the privacy and confidentiality of employees (Verburg et al. 2018).

The key challenge in most studies looking into management practices has been determining the most appropriate way of measuring and labelling management practices. Bloom and Van Reenen (2007) determined an efficient way of classifying and describing various management practices, which facilitated the effective comparison of management and performance practices across countries. A major strength of the study is the reliability and validity of the findings. Bloom and Van Reneen (2007) validated the findings internally by repeating the survey and interviewing different managers in different firms as well as involving different interviewers at separate times in the same company. The two datasets had a strong correlation. In contrast, external validity was determined by tallying the survey outcomes with stock market values and information on the companies’ accounts. This process also yielded similar outcomes.

Excellent management practices are linked to increased profitability and productivity in addition to survival rates of firms. Good management also influences sales growth rates and Tobin’s Q. Therefore, any firm that wishes to achieve maximum productivity and profitability needs to consider its management practices, paying attention to the different factors of management such as operations, monitoring of performance, nurturing talents, setting realistic targets and adoption of contemporary methods of manufacturing such as lean’s.

Conclusion

Management practices plays a vital role in the productivity and profitability of a company. To attain optimum results, it is necessary to consider various aspects of management, particularly monitoring, operations, setting targets and employee skills and talents. The findings of this study showed that these factors had a significant positive correlation with the average management scores. A manager can lead effectively if the number of subjects is manageable. This observation was confirmed by the predominance of employee bands of 500 and below in the highest-ranking countries in management scores. Pertinent questions that could be explored by further analyses include: ‘What is the relationship between the adoption of lean’s management in topmost and poorly performing countries?’ ‘Is there a significant relationship between employment and retention of talent?’ The type of data needed for such endeavours include ‘introduction to lean manufacturing’, ‘rationale for lean manufacturing’, ‘average of all management questions’ for selected top and bottom performing countries. Other data required for the second question are ‘retaining talent’ and ‘employment at the firm (size bands).’

References

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