Human capital theory


According to human capital theory, the investments in human capital are crucial for advancing productivity and sustaining competitiveness in both manufacturing and service sectors (Becker, 1993). In the light of this, training is seen by many economists and scholars as a tool to improve performance for both individual organisations and their employees. Though investments in training are deemed to be a collective good on theoretical level, many organisations seem to be faced with a dilemma in practice. On one hand, for surviving and being profitable in more and more competitive market, they want to enhance their performance to achieve competitive advantage by undertaking training. One the other hand, they are afraid that the cost of training could not be recouped. Indeed, there are two situations may make employers lose in training rather than gain from it. Some employers have invested a lot of resources in training, but the expected outcomes have not been achieved. In this case, ineffective training is very harmful to organisations. Another worry falls into voluntary quits. In other words, competitors can easily induce trained employees to migrant from their former employers, which enables them to benefit from the increased performance without paying the considerable training cost. To prevent poaching and retain the trained employees, employers are forced to provide higher remuneration to keep them content. Consequently, the fear of competitors' poaching behaviour and higher pay roll cost dramatically frustrate employers' incentive to train. Therefore, organisations have to deal with this dilemma by balancing the reward and risk of training before the decision is made. Based on this, to examine the training effects on performance and turnover is a necessary ingredient to work out any resolution of this dilemma.

As Glance, et al. (1997) demonstrate that organisations' decision whether or not to invest in training can inevitably affect economic performance, this dilemma has been increasingly highlighted not only by employers but also economists and scholars, especially in those industries that play a crucial role in economies. Electronic manufacturing in China is one of them. A significant growth of electronic manufacturing in China has been witnessed in recent years. Zhao, et al. (2007) in their recent paper indicate that there is an explosive growth in electronic manufacturing. According to the statistics reported by National Bureau of Statistics of China (2006), in a decade from 1995 to 2005, the output of electronic industry has experienced a more than tenfold growth, from RMB 253.6 billion to RMB 2723.64 billion. In the aspect of added value, it has increased from 1 percent of GDP and 5 percent of manufacturing added value to 3.2 percent and 10.2 percent respectively. The growth rate of investment in electronic manufacturing is 8.66 percent in 1996, and it has climbed to 26.26 percent and 89.16 percent in 2000 and 2005. Undoubtedly, under the explosive growth, there must be a huge demand for skilled and competent labour force. Therefore, to achieve the expected performance in productivity and quality, providing training for employees seems to be necessary, which also places the firms in the dilemma mentioned above. Based on this, by collecting relevant data from firms in electronic manufacturing, this research intends to examine the links between training and performance and turnover.

Literature Review

There is extensive literature that exists on the training effects on performance. Although some of them emphasis on the different variables of performance such as productivity, cost, scrap rate and job attendance, the outcome is consistent in general, namely positive relationship between training and performance. Krueger and Rouse (1998) suggest that there is a positive association between work education program and performance. They focused on the effects of a workplace education program in a manufacturing sector and service sector. The manufacturing company is highly cyclical which has more than 500 workers. By comparing data before and after the training, they found it had some positive effects on job attendance, but it seemed not significant. In terms of productivity, due the administrative data was not available, they found training was positively related to productivity by using self-perceived productivity data. Barrett and O'Connell (2001) argue that there is a positive and significant relationship between training and productivity growth. Using a firm-level dataset, they drew their analysis based on a survey conducted in Ireland in 1993 which was funded by European Union. In this survey, 1000 enterprises were selected randomly as samples. To generate data required but not available in that survey, Barrett and O'Connell also conducted a follow-up survey in 654 companies. The theoretical distinction between general and specific training was also applied in the analysis. In terms of the different outcome of these two training types, they found that the positive effects of general training still remained when they controlled the variables such as changes in firm size and work organisations, whereas the ones of specific training could not be observed. Similarly, Bartel (1995) indicates training indeed increases job performance and wage growth by using a unique dataset. This dataset was collected from the administrative personnel records in 1986-1990 of a large company. A total sample of 19,000 observations had been done in these records, and they were distributed in eight different functional departments including finance, engineering, manufacturing, marketing, information systems, R&D, staff services and support services. The results suggest training has a significant and positive effect on job performance, particularly in productivity.

The relationships between training and turnover are complicated. Cotton and Tuttle (1986) claims the sources of turnover can be attributed to many correlates, which can be classified as external factors, structural or work-related factors and personal characteristics. Therefore, any factors or characteristics influenced by training would be reflected on turnover. There is also substantial literature available focused on the links between training and turnover, however, the results seem to be debatable. Drawing on two large-scale micro-economic data sources in Britain, National Child Development Survey (NCDS) and Quarterly Labour Force Survey (QLFS), Dearden, et al. (1997) found the job mobility, in terms of job-to-job moves, was lower for individuals who had received training in previous, especially true in the condition that the training was funded by employers. Whereas in another research, Krueger and Rouse (1998) found that the training participants were equally likely to exit the company as other employees. In addition, they assessed that training participants were less likely to be discharged or laid off, which may be attributed to that employers valued them more highly due to training. From the perspective of human capital theory, Becker (1962) claims general and specific training are the two types of training. In the case of general training, employees' performance is enhanced no matter in where they work. For specific training, the improvement is only useful in a certain firm. Therefore, it can be predicted that specific training may lead to a less job mobility, whereas general training may increase it due to the other employers' poaching behaviour.

Although vast research has been conducted to examine the relationships between training and performance and turnover, little is focused on electronic manufacturing, particularly true for China. Furthermore, current electronic manufacturing in China is confronted with many distinctive problems such as migrant work force, industry upgrading and international norms compliance. These require firms to invest a lot in training, but there is little information for reference. Therefore, I intend to identify training effects on performance and turnover to provide some information and recommendations for electronic manufacturing in China.

Research Objectives

  1. To examine effectiveness of training programmes on performance.
  2. To identify the turnover correlates which are significantly affected by training.
  3. To assess the training effects on turnover, if applicable, specify the effects of various training (e.g. general training & specific training).
  4. To present some recommendations for training policies in electronic manufacturing.


According to the research objectives, case study will be used due to its essential of this research, namely explanatory research. To enable a better generalised finding, the research will be conducted in two electronic manufacturers, which have about 250 and 400 employees respectively. In addition, to obtain required data relating to different objectives, multiple collection techniques will be employed, which are presented as following.

Firstly, to examine the training effects on performance, the administrative data from the two companies will be used. One of the advantages is that the bias and errors can be avoided. Employees may have difficulties in precisely recalling training types and amount of which they have received in previous time. Moreover, employees may have different understanding towards training. In other words, some training programmes may be ignored by some workers, since they regard them as work rather than training. In addition, the reliability and validity of administrative data relating to performance is stronger than self-report data. By comparing different duration data in monthly or weekly production report from workshops, the changes in performance indicators such as productivity, scrap rate, downtime, quality pass rate and absenteeism can be observed to assess the training effectiveness. In the case of self-report data, it lacks of objectivity and accuracy. In terms of potential harm caused by sensitive data exposure, I will deal with them carefully and keep the two companies anonymous strictly for ethical concerns.

Secondly, given that Cotton and Tuttle (1986) have classified turnover correlates, questionnaires will be distributed among employees to explore which turnover correlates are significantly affected by training. The simple population should cover those trained operators, technicians, quality inspectors and line managers, who work in the frontline of production. Besides collecting demographic information, each question will be designed for the purposes of examining the link between training and one certain correlates. For the concerns of reliability and validity, a pilot will be conducted before the final distribution. To collect feedback and enable modifications, 20 questionnaires will be distributed in my former company which is a multinational electronic manufacturing. In this way, to what extent training affects the correlates can be observed, and further, it enables explanation of training effects on turnover. To assist the explanation of training effects on turnover, trainees' attitudes towards job mobility will be also studied by conducting interviews. Besides the staffs in the frontline, the human resource professionals will be also involved for a more comprehensive understanding. In the light of this, the quantitative and qualitative data can be combined to generate the findings of training effects on turnover. In addition, to verify the findings, the administrative data in terms of trainees' turnover will also be analysed and compared with data obtained from questionnaires and interviews. Again, to eliminate employees' worries and ensure the reliability, all sensitive data will be kept confidential.

At last, to achieve the effective investigate, the data obtained from company dataset, questionnaires will be analysed by using SPSS, whereas the in-depth data acquired from interviews will be transferred to NVivo. By uncovering training effects on performance and turnover, appropriate recommendations can be developed for training strategies in electronic manufacturing.


I will fund myself for all the incidental cost in this research. The accesses to electronic manufacturers are in the process of negotiation. Although the formal approval has not been obtained, the companies have shown highly interested in this topic. The access of pilot questionnaires has been informally guaranteed by my former employer. In addition, other resources such as computer software SPSS and NVivo, library and access to internet are also available. At last, my former fellows who are professionals in production, maintenance and human resource management, would also provide me some valuable suggestions and information.


  • Barrett, A. and O'Connell, P. J., 2001. Does training generally work? The returns to in-company training. Industrial & Labor Relations Review, 54(3), 647-662.
  • Bartel, A. P., 1995. Training, Wage growth, and job performance: evidence from a company database. Journal of Labor Economics, 13(3), 401-425.
  • Becker, G. S., 1962. Investment in human capital: a theoretical analysis. Journal of Political Economy, 70(5), 9-49.
  • Becker, G. S., 1993. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. 3rd ed. London: University of Chicago Press.
  • Cotton, J. L. and Tuttle, J. M., 1986. Employee turnover: a meta-analysis and review with implications for research. Academy of Management Review, 11(1), 55-70.
  • Dearden, L., Machin, S., Reed, H. and Wilkinson, D., 1997. Labour Turnover and Work-related Training. London: The Institude for Fiscal Studies.
  • Glance, N. S., Hogg, T., Huberman, B. A., 1997. Training and turnover in the evolution of organisations. Organisation Science, 8(1), 84-96.
  • Krueger, A. and Rouse, C., 1998. The effect of workplace education on earnings, turnover, and job performance. Journal of Labor Economics, 16(1), 61-94.
  • National Bureau of Statistics of China, 2006. China Statistical Yearbook of High-tech Industries. Beijing: China Statistics Publishing House.
  • Zhao, Z., Huang, X., Ye, D. and Gentle, P., 2007. China's industrial policy in relation to electronics manufacturing. China & World Economy, 15(3), 33-51.

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