Keywords

3.1 Research Background and Awareness of Problems

3.1.1 Supply Chain Management and Human Resource Management

In today’s corporate world, companies are achieving competitiveness by maintaining business relationships with many enterprises rather than by engaging in transactions with a single entity. This chain of trade is called the “supply chain” (SC), and can be recognized as a type of network that consists of a wide range of processes including procurement, production, distribution, and customer delivery. In these processes, a chain of distribution and sales channels is being realized both upstream and downstream with the involvement of a range of entities (e.g., materials enterprises, suppliers, assemblers, distributors, and retailers). From the standpoint of SC participants, there is a need to design and manage the string of flows ranging from procurement and production to consumption. The industry term for this type of management is called “supply chain management” (SCM). We believe that if individual players can properly execute SCM, they can raise the value of their business and secure profits, thus improving their financial outcomes and increasing their chances of survival.

Proper execution of SCM often requires handling issues between companies such as building a stable trade relationship between organizations. That said, we must also consider the fact that such stable and efficient interorganizational trade operations can only be achieved where individual companies have the foundation of stable and efficient internal management structures of their own. Accordingly, we believe that internal management of individual companies’ organizational structure must also be recognized as an important factor. Particularly, if a company is considering running an efficient SCM operation from the standpoint of HRM, they must also consider improving the quality of their labor force, instead of just focusing on the quantity aspect. Specifically, a company needs to practice HRM that will encourage employees to engage in behaviors that are desirable for the company. However, when looking at the manufacturing industry in recent years, many manufacturers are carrying out their activities across multiple countries outside their home country. Given this, manufacturers are forced to build HRM practices that are suitable for employees working in their respective countries.

For Japanese companies, China and the ASEAN region have an extremely large presence as bases of production. Japanese companies that have entered these regions are trying out various HRM approaches to run smooth production operations at their respective local sites. As a result, they are forced to make a painstaking decision to choose between two approaches: (i) to implement the same HRM system as that used in Japan or (ii) to implement customized HRM systems to adapt to the characteristics and social systems of local workers. They are forced to face the issues of standardization and adaptability of HRM.

To provide a fixed solution to this kind of situation, we need to conduct an international comparison of various ASEAN Plus Three countries, including Japan. In other words, to have employees actively engage in types of behaviors desirable for their companies, we must conduct by-country analysis to figure out the most effective means of implementing HRM by identifying the differences between each region. Accordingly, in this study, we will focus on three countries by looking at Japan as the home country, and considering Thailand and China as Japanese companies’ main foreign investment countries. By targeting local employees who are working for Japanese companies in these countries, we will attempt to find a solution to help determine the type of HRM practices needed to promote employee behaviors required to run efficient SCM operations, and help identify the difference between these countries.

3.2 Methodology

3.2.1 Survey Overview

We conducted a questionnaire survey by targeting employees who work in the automotive, machinery, and electronic components industries in Japan, China, and Thailand. The survey was conducted in March 2013 by distributing a total of 1800 questionnaires, with 600 questionnaires per each country. We collected a total of 1093 responses (60.7%), with the following breakdown:

  • Japan: 328 responses (response rate 54.7%);

  • Thailand: 352 responses (58.7%);

  • China: 413 responses (68.8%).

We narrowed the number of respondents to those who answered all the question items, and the filtered results were:

  • Japan: 270 responses (response rate: 45.0%);

  • Thailand: 308 responses (51.3%);

  • China: 377 responses (62.8%);

  • Total: 955 responses (53.0%).

The sample attributes were as follows:

  • Male versus female comparison:

    • Japan: female 58 persons, male 212 persons;

    • Thailand: female 128 persons, male 180 persons;

    • China: female 185 persons, male 192 persons.

  • Average age:

    • Japan: 40.38 years old;

    • Thailand: 30.39 years old;

    • China: 31.35 years old.

  • Average age:

    • Japan: 40.38 years old;

    • Thailand: 30.39 years old;

    • China: 31.35 years old.

  • Average service period:

    • Japan: 147.5 months;

    • Thailand: 48.0 months;

    • China: 58.5 months.

We conducted the analysis in three phases. First, we conducted an exploratory factor analysis by targeting the entire sample and confirming the factor structure. In doing so, we calculated Cronbach’s α concerning each scale and examined its reliability, and used the scales that possessed such reliability. Next, to determine whether the population of each of the three countries possessed a common factor structure, we conducted a multiple group structural equation modeling to ensure that factors could be used for the analysis in this study. Lastly, we examined the difference between the three countries (Japan, Thailand, and China) by conducting path analysis through multiple group structural equation modeling. The examination of exploratory factor analysis and reliability during the first stage is further explained and discussed in Sect. 3.2.2 (Survey content). Multiple group structural equation modeling (factor analysis) during the second stage and multiple group structural equation modeling (path analysis) during the third stage are explained further in Sect. 3.3 (Analysis).

3.2.2 Survey Content

3.2.2.1 Behavior of Employees

In this study, we decided to measure employees’ trust behavior and innovative/spontaneous behavior by excluding their participation/attachment toward the system.

For the scale concerning trust behavior, we used the scale of “behavior within role” that was used by Williams and Anderson (1991). Question items are composed of four items such as “I adequately complete duties.” To differentiate spontaneous behavior from innovative/spontaneous behavior, we used the scale of “helping behavior” from Podsakoff et al. (1990). Question items are composed of five items such as “Is always ready to lend a helping hand to those around you.” For innovative behavior, we used the scale of “improvement behavior” from Morrison and Phelps (1999). Question items are composed of four items such as “I often make constructive suggestions to improve how things operate within the organization.”

We conducted exploratory factor analysis on items regarding employee behavior out of the above 13 items. To identify the number of factors, in addition to MAP criterion, we also used the Kaiser-Guttman rule that accepts eigenvalues of 1 or more, and we followed the MAP criterion in the case of any discrepancy. Regarding employee behavior, a three-factor structure was shown, with the value of MAP showing a variation of .0394, .0377, .0311, .0427, and so on. Furthermore, the Kaiser-Guttman rule also indicated a three-factor structure. Accordingly, we determined a three-factor structure to be appropriate.

Next, we conducted factor analysis by using the maximum likelihood method and oblique rotation (promax rotation) by fixing the number of factors as three. As a result, we conducted another analysis by excluding item 1 entailing factor load less than .40 (I often try to change how his or her job is executed in order to be more effective.), and we were able to achieve convergence by repeating it six times (Table 3.1). We decided to name factor 1 as “organizational citizenship behavior” (α = .892), since it consists of four organizational citizenship behavior items such as “Willingly helps others who have work related problems.” We decided to name factor 2 as “work behavior” (α = .882), since it consists of four items related to daily work such as “I fulfill responsibilities specified in job descriptions.” We decided to name factor 3 as “improvement behavior,” since it consists of three items related to improvement behavior such as “I often try to correct a faulty procedure or practice.”

Table 3.1 Results of exploratory factor analysis of employee behavior
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3.2.2.2 HRM Practices

Regarding HRM practices, we measured employees’ perception of practices that covered educational training, performance-based systems, work–life balance (WLB), and job security.

For the question items on educational training, we used the scale from Zhang et al. (2008), Sun et al. (2007), Delery and Doty (1996), Ahmad and Schroeder (2003). The question items are composed of five items such as “Individuals in this job receive bonuses based on the profit of the organization.” For the question items on performance-based systems, we used the scale from Zhang et al. (2008), Sun et al. (2007), Deckop et al. (1999).

The question items are composed of six items such as “Individuals in this job receive bonuses based on the profit of the organization.” For question items on WLB, we used the scale from Forsyth and Polzer-Debruyne (2007) and Baptiste (2007). The question items are composed of four items such as “My current work place provides help to improve or assist my work life balance.” For the question items on job security, we used the scale from Gaertner and Nollen (1989), Lee et al. (2010), Sun et al. (2007) and Yamamoto (2009). The question items are composed of three items such as “Job security is almost guaranteed to employees in this organization.”

We conducted an exploratory factor analysis on the HRM practices items composed from the above 18 items. When looking at the change in MAP value regarding HRM practices, it indicated a four-factor structure by showing the following changes: .0377, .0316, .0239, .0233, .0311, and so on. Furthermore, the Kaiser-Guttman rule also indicated a four-factor structure. Based on these findings, we found a four-factor structure to be appropriate.

Next, we conducted factor analysis by using the maximum likelihood method and oblique rotation (promax rotation) by fixing the number of factors as four. As a result, we were able to achieve convergence by repeating it five times (Table 3.2). Factor 1 consists of five educational training items such as “Employees will normally go through training programs every few years.” and obe item on performance evaluation. We named it “educational training” in consideration of the content of the item (α = .940). We named factor 2 as “performance-based system” (α = .882), since it consists of five items on performance-based systems such as “Close tie or matching of pay to individual/group performance.” We named factor 3 as “WLB”, since it consists of four items on WLB such as “My current work place provides help to improve or assist my work life balance.” We named factor 4 as “job security,” since it consists of four items on job security such as “If the company was facing economic problems, employees would be the last to get downsized” After extracting these factors, we calculated the descriptive statistics of each factor and correlation coefficients between variables (Tables 3.3 and 3.4).

Table 3.2 Results of exploratory factor analysis of HRM practices
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Table 3.3 Descriptive statistics
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Table 3.4 Correlation coefficient between variables
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3.3 Analysis

3.3.1 Examination of Explanatory Model

The purpose of this study is to verify the validity of a model for each country. By making such comparisons, we attempt to conduct a by-country comparison to determine what kind of impact HRM practices have on employee behavior. In doing so, we decided to verify the relationship between the variables by using path analysis based on structural equation modeling (SEM). Since we did not find any extreme values regarding the mean, standard deviation, skewness, and kurtosis of the observed variables, we decided to use all variables in our analysis.

3.3.2 Examination of Factor Structure (Multiple Group Structural Equation Modeling)

The scale shown was the result of an exploratory factor analysis conducted on the responses of three populations (Japan, Thailand, and China). Given this, we need to check whether the factor structure will apply to each country in the same way. In doing so, we check whether the factor structure applies to all three countries based on the result of the exploratory factor analysis by conducting multiple group structural equation modeling that targets the three populations.

We analyzed each model by assuming model 1 as the fixed position model (without any equivalency restriction), model 2 as a model with equal factor load volume, and model 3 as a model where the path is equal between factor load volume and factor. For the adaptability criterion, we examined the value of Akaikeʼs information criterion (AIC) and Browne–Cudeck criterion (BCC), in addition to goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). Both AIC and BCC were used as valid criteria in comparing multiple models, as they determine whether the adaptability of a model that indicates a smaller value is functioning at a high level.

The findings showed that, as indicated in Table 3.5, model 1 (fixed position model) achieved the best adaptability to the data for both employee behavior and HRM practices. Though it did not perform as well as model 1, we also found satisfactory adaptability for model 2 that placed equivalency restriction on factor load, thus leading to our determination that matters such as homogeneity of data are being ensured in Japan, Thailand, and China.

Table 3.5 Results of factor structure (Multiple group structural equation modeling)
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3.3.3 Examination of Explanatory Model

In continuing with the purpose of this study, we set up three models and conducted a test through multiple group structural equation modeling (path analysis) to find out whether there is a difference in explanatory model depending on nationality. In addition to GFI, AGFI, CFI, and RMSEA, we also used AIC and BCC for the adaptability criteria. We used the following three models for the analysis.

Model 1 is a model that assumes the entire path coefficient to be different depending on the country, without adding restrictions on the entire covariance and path coefficients. Model 2 is a model that assumes the path coefficient between observed variables to be different depending on the country, by deeming observed variables having equal quality by adding an equivalency restriction on covariance between observed variables. Model 3 is a model that assumes the path coefficient between latent variables to have the same quality for each country, by adding an equivalency restriction on path coefficient between observed variables for each country. Amos 18.0 was used for statistical analysis in this study. Our analysis results showed that only GFI, AGFI, and CFI of model 1 displayed a satisfactory value of more than .9, and RMSEA displayed a satisfactory value of less than .05. Furthermore, Model 1 also showed the smallest numerical value for AIC and BCC. Based on these results, we determined that a model with no added equivalency restrictions is the best type of model (Table 3.6).

Table 3.6 Goodness-of-fit index of each model
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3.4 Examination of Model by Country

3.4.1 Examination of the Model for Each Country

First, we will examine the model in Japan. In terms of educational training policy and WLB policy, we were unable to confirm that they had any impact on the three behaviors of employees. We recognized that the performance-based policy had a positive impact on improvement behavior, but we were unable to confirm that the policy had any impact on work behavior and organizational citizenship behavior. For job security practice, we confirmed that it had an impact on all behaviors that covered work behavior, organizational citizenship behavior, and improvement behavior.

Next, we will examine the model in Thailand. Our findings showed that educational training policy had an impact on work behavior. Our findings showed that performance-based policy had an impact on improvement behavior. Our findings further showed that WLB policy had an impact on organizational citizenship behavior. Job security policy had an impact on organizational citizenship behavior and improvement behavior.

Next, we will examine the model in China. Educational training policy was found to have an impact on organizational citizenship behavior and improvement behavior. Our findings showed that WLB policy did not have a significant impact on any behaviors of employees. Result-based policy and job security practices promoted all behaviors that covered work behavior, organizational citizenship behavior, and improvement behavior.

Lastly, with respect to the path that indicates a significant trend in the populations of two of the countries among Japan, Thailand, and China, we conducted a significance test of path coefficient to confirm whether a significant difference is found on the coefficient. We targeted the following items:

  • Job security → work behavior

    • Japan = China

  • performance-based system → improvement behavior

  • Japan = China, Japan = Thailand, Thailand = China

  • job security → OCB

  • Japan = China, Japan = Thailand, Thailand = China

  • job security → improvement behavior

  • Japan = China, Japan = Thailand, Thailand = China

The test statistics on the differences between the parameters of each population is shown in Table 3.7. These values represent values that converted the difference between two path coefficients into a standard normal distribution, and it signifies that there is a significant difference (at a 5% significance level) between two path coefficients if the test statistic is above 1.96.

Table 3.7 Result of significant difference test in the path coefficient
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The result of our significance test confirmed that a significant difference was shown at 5% standard for path of Thailand = job security between China → OCB. This shows that a relationship between job security → OCB is significant for both countries, and it also simultaneously shows that the difference in path coefficient between both countries is significant. More specifically, it shows that the non-standardizing coefficient in Thailand is at .160, and in China, it is at .336. In other words, we can suggest that the impact of job security has on OCB may potentially differ between the two countries. The other path coefficients did not show any significant values. Accordingly, we did not find any differences that can be detected from the sample used in this study, and therefore were unable to determine the existence of differences regarding the path coefficient that showed common significant values for each country (Figs. 3.1, 3.2, and 3.3).

Fig. 3.1
figure 1

Relationship between HRM practices and employee behavior (Japan)

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Fig. 3.2
figure 2

Relationship between HRM practices and employee behavior (Thailand)

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Fig. 3.3
figure 3

Relationship between HRM practices and employee behavior (China)

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3.4.2 Common Points Between the Models for Each Country

The findings showed that the impact that HRM practices have on employee behavior comes in two forms: impacts that are common among all three countries and impacts that are unique to specific countries. We will first examine the common impacts. First, we note that performance-based policy has an impact on improvement behavior of each country, when looking at it from the standpoint of the effect of the policy. Based on this finding, we can suggest that to promote improvement behavior in today’s manufacturing industry, it is important to make sure such behavior is being evaluated in a proper way.

Second, we can point out that job security practices have an impact on organizational citizenship behavior and improvement behavior in each country. This shows that improvement in job security practices help employees feel secure in their employment status, thus vitalizing outside-of-role behavior that goes beyond their work behavior of fulfilling one’s responsibility regarding regular duties.

When looking at the common point between these two based on behavior, it is important that the stance on job security is being expressed clearly at each country to promote the outside-of-role behavior referred to as organizational citizenship behavior. Furthermore, to promote improvement behavior—considered as the source of the strength of Japanese companies’ production sites—as also being recognized as the same type of outside-of-role behavior, we believe that incorporating performance-based HRM on top of job security can potentially serve as an effective way to promote such behavior.

3.4.3 Differences Between the Models for Each Country

Next, we will discuss the differences between the models for each country. First, we can point out the impact of educational training policy. We did not find educational training policy to have any impact on employee behavior in Japan. However, we did confirm that educational training policy had an impact on work behavior in Thailand. Furthermore, in China, we did not find educational training policy to have an impact on work behavior, but we did confirm that the policy had an impact on organizational citizenship behavior and improvement behavior. Based on these findings, from the standpoint of promoting employee behavior, we can suggest that in Japan, it may potentially be more effective to strengthen other kinds of policies rather than strengthening the educational training policy. Furthermore, since the type of employee behavior being promoted by the educational training policy between Thailand and China was different, companies must recognize the effect of this policy and develop ways to strengthen it.

Second, we can point out the impact of performance-based systems. In Japan and Thailand, the awareness of performance-based policy only had an impact on improvement behavior, while in China, performance-based policy had an impact on all aspects of employee behavior. Based on these findings, when it comes to China, we can expect to gain a wide range of effects by strengthening the performance-based policy.

Third, we can consider the impact of WLB policy. WLB had an impact on employee behavior (organizational citizenship behavior) only in Thailand. We did not find WLB to have an impact in Japan or China. Based on this finding, we can conclude that it is important to strengthen WLB policy for Thailand.

Fourth, we can consider job security practices. Thailand was the only country that was not affected by this policy. Our findings also revealed that there was a difference in the strength of impact between Thailand and China regarding the impact job security had on organizational citizenship behavior. The findings showed that job security practices had a stronger impact on organizational citizenship behavior in China than in Thailand. As mentioned in Sect. 5.4.2, we can suggest that job security has a wide range of effects toward the promotion of employee behaviors in each country. We can also suggest that such effects can potentially carry greater weight in China compared to Thailand. At the same time, we can also suggest the possibility of such effects not having as broad a range in Thailand compared to China and Japan.

When we combine these common points and differences, we can conclude that in order to promote the active engagement of desirable behavior among employees in the manufacturing industry in Japan, Thailand, and China, companies first need to strengthen the policies that function effectively throughout all three countries, and then select and strengthen the policies that are suited for each specific country.

3.5 Contribution and Issue

3.5.1 Contribution of This Study

The first contribution of this study is that it analyzes the common points and differences regarding the impact of HRM practices on employee behavior in the manufacturing industry in Japan, China, and Thailand. Very few previous studies have engaged in this kind of international comparison. Even where such comparisons were made, most studies did not engage in detailed analysis that compared models by confirming the commonality of factor structure and using path analysis with multiple group structural equation modeling. In consideration of the circumstances surrounding these preceding studies, we believe this study played the role of expanding the explanation area of HRM practices by conducting analysis that entailed higher levels of interpretability.

The second contribution is that by using this kind of analysis method, we clarified the common effect of HRM practices. Specifically, we revealed that limitations of WLB policy and the importance of job security practices are factors common to each country. This study was significant in the sense that it pointed out the common points between Japan—known as an economically advanced nation—and Thailand and China, which are known for having a relatively high level of economic development among developing nations.

By revealing these common points, this study made it possible for companies to draw up specific policy in figuring out how to advance the standardization of HRM that can go beyond national borders.

The third contribution is the concrete clarification of the difference in the effect of HRM practices. Specifically, it revealed that: the effect of educational training policy was not found in Japan, the effect of performance-based policy was shown widely throughout China, the effect of WLB policy was found in Thailand exclusively, the effect of job security practices showed the widest range in China and was limited in Thailand when compared to other countries, and the fact that such policy had less impact in Thailand than in China. By clarifying these differences, this study made it possible for companies to engage in evidence-based discussions to determine the specific type of policy that should be used to advance the local adaptability of HRM.

3.5.2 Study Task

In this study, we focused on the direct relationship between HRM practices and employee behavior to explain employee behavior. However, we can reasonably expect attitude variables such as work satisfaction and organizational commitment to act as mediation factors. In the future, we need to extend our analysis and take direct as well as indirect effects into consideration, including mediation factors.

Furthermore, we attempted to analyze only four individual policies among HRM policies in this study, omitting, for example, policies on work design and career development. In the future, we need to extend our study to address the entire HRM system by extending the individual policy category and conducting international comparisons.

Furthermore, we will also identify the sampling issues that were discussed in this study. Here, we conducted a survey by targeting employees working for Japanese companies in the manufacturing industry. In that sense, we could control the sample characteristics to a certain extent. However, we were not able to tightly control items such as industry type and regions within each country. In the future, we would like to see a study that can generalize the suggestions made in this study through sampling that can sufficiently control the sampling characteristic.