Keywords

1 Introduction

At present, science and technology is rapidly developing and new knowledge explosion continued. Some enterprises carry out reform or development in China, and most technical employees have to take more time to work as well as learning new technology and skills. Accompanied by high working pressure, health problems or safety accidents have increased in recent years. According to the latest medical statistical data [1], there were more cases of psychological problems and mental disorders than ever in China. The safety department survey results [2] showed that 60–90 % of accidents were caused by human errors. This paper examines job-engagement and its influencing factors to explore the means of evaluating from a psychological perspective, so that it provides related technical support for safety management.

2 Core Concepts

2.1 Job Engagement

Job engagement (JE) is defined as a positive, fulfilling, work-related state of mind. There are three dimensions to JE: (1) vigor (VI), invested high levels of energy in one’s job and challenged difficulties with tenacity; (2) dedication (DE), a sense of significance, showed enthusiastic and proud at working; and (3) absorption (AB), characterized by being happily engrossed in one’s job and not willing to get off the job. In 2002, job engagement was tested with the Utrecht Work Engagement Scale [3].

2.2 Job Burnout

Job burnout was a metaphor that was used to describe a state of mental weariness in workplace. It can be traced back to a survey of Maslach Burnout Inventory (MBI). Later, the original developed the new version that was called MBI-General Survey (MBI-GS). There were three dimensions in MBI-GS: exhaustion, cynicism, and efficacy [4].

In this paper, based or referenced on job burnout theories of Maslach and other scholars, the author edited Technical Staff Job Burnout Scale (TSJBS) using the data or information on psychological status at work, which were collected from interviewing and investigating some Chinese technical staff in Beijing [5]. In TSJBS, there are three dimensions: (1) mental fatigue (MF), a sense of psychological fatigue at work, feeling of difficulties in concentrating, memorizing, and thinking; (2) alienation sense (AS), reflected indifference to customers or a negative attitude towards job; and (3) inefficacy sense (IS), made a lower evaluation on one’s occupational accomplishment, working performance was worse than that one would expect.

2.3 Job Characteristics

Job characteristics is defined as professional factors related to one’s job. There were five dimensions in the Job Characteristics Scale (JCS): (1) treatment satisfied (TS), satisfied with payment, position, and incentive scheme; (2) job payload (JP), being demanded on the quality or quantity of jobs within a period; (3) working roles (WR), being expected to do two or more jobs in a period; (4) resource supply (RS), getting energy both physically and psychologically; and (5) resource matching (RM), working in good human–machine environment system, such as rational technical reserves for operating machine, good workplace to human fitness [5].

3 Interview and Results

20 subjects from a research institute in Beijing were interviewed by the author, who talked about their occupation experience. Some constituting elements of job engagement were found in the in-depth interview, which include: (1) liked their job and did jobs in high spirits; (2) enjoyed working and challenged difficulties; and (3) put their energy into job as much as possible. There were some issues found, such as pay benefits, job plans and roles assignment, significantly impacted job engagement.

4 Questionnaires and Results

4.1 Subjects

230 technical staff from a research institute in Beijing presented with self-reporting questionnaires on job engagement, job burnout, and job characteristics. There were 20 individuals interviewed among the subjects. In this survey, 199 test papers were received and the 190 papers were completed, of which the validly response rate was 82.6 %.

4.2 Instruments and Material

The following instruments and material were used:

  • Utrecht Work Engagement Scale of Chinese Version (UWES-C)

    Utrecht work engagement scale (UWES) was made by Schaufeli originally, which was translated and revised by Chinese scholars [6]. There are 15 items which were grouped into three subscales and the items were scored on a seven-point scale (0–6). The subscales include VI, DE, and AB.

  • TSJBS and JCS

    Both scales were revised by the author [5].

  • Professional efficiency scale(PES)

    PES includes 9 items (e.g. “I can effectively complete the job and make an achievement”), which was edited by the author. All the items were scored on a four-point scale (0–3).

4.3 Subjects

Based on psychological principle of measurement and assessment, first, questionnaires were sent to subjects and taken back by the author. Second, data from this investigating was reorganized and some invalid papers or abnormal numbers were deleted. Third, the data or information was studied by using SPSS FOR WINDOWS software for psychological statistics or calculation.

4.4 Investigation Results

4.4.1 UWES-C

  • Structure validity

    Using confirmatory factor analyses to the UWES-C, the three-factor model showed an acceptable fit to the data with χ2 = 144.05, df = 82; important model fitting parameters (GFI, CFI, etc.) reach or approach 0.9; RMSEA < 0.08. The results showed that this structure validity of UWES-C can meet psychometric requirements as the following Table 13.1.

    Table 13.1 Results of confirmatory factor analyses to UWES-C (N = 190)
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  • Internal consistencies

    Cronbach’s (α) is displayed in Table 13.2. According to the relevant standard [7], it was good at the internally consistent.

    Table 13.2 UWES-C internal consistencies (N = 190)
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4.4.2 The Scores (M and SD) and the Correlations

Subjects (N = 190) were assessed by UWES-C, TSJBS, JCS, and PES. The mean (M), standard deviation (SD) of the study variables, and correlations showed in Table 13.3.

Table 13.3 The scores (M, SD) and the correlations
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4.4.3 Regression Analysis on the Influence Factors

JE was a dependent variable; the subscales of JCS and TSJBS were predictors (independent); TS, RM, JP, MF, and AS were predictors. The results showed in Table 13.4.

Table 13.4 Coefficients on the subscales of JCS & TSJBS predicting JE
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5 Discussions

5.1 The Meaning of Correlations

TS, JP, and RM could be important factors impacting JE because there were moderately positive correlations (r > 0.3, p < 0.01) between UWES-C subscales and JCS subscales (TS, JP, and RM) as the data displayed (Table 13.3), and their statistical validity is good [8]. The UWES-C positively correlated with mental fatigue (r = 0.21, p < 0.01), showing that the mental fatigue showed a person was indeed working hard. On the one hand, if there is no mental fatigue, it means that a person did not engage with the job. But, some employees, who were psychological and physiologically healthy, might have felt intense mental fatigue, meaning that they spent much energy or time in job (over-Job Engagement). If so, leaders of safety department should pay attention to the employees who might conduct unsafe activities or make troubles because they were too tired to work well.

The TSJBS and its subscale IS unrelated significantly with the score of UWES-C, which means both job burnout and job engagement are different in concept from each other, and inefficacy sense is not a significant influence factor to the job engagement.

5.2 Job Engagement Predicting Model in Reality Meaningfulness

Job engagement predicting model (JEPM) was built using standardized regression coefficients, which also called the path coefficient (Beta), as shown in the Fig. 13.1 (figures are numerical value of Beta). As Table 13.4 showed, in the model (JCS), some dimensions of job characteristics scale (TS, JP, and RM) attributed to job engagement in the same time, reflecting a level of organizational management; In the model (TSJBS), mental fatigue and alienation sense could impact (or mediate) Job Engagement.

Fig. 13.1
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JEPM

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In the interview, there were two extreme cases of job engagement: insufficient and excessive. The former showed passive, distraction in day-dreams and so on; the later displayed one’s unbalanced situation between work and life because of investing too much time into jobs, in which the individual did not enjoy eating and sleeping for many days so that the brain felt painful or disorder. Both of the cases could weaken safety management in the workplace, and even dangerous accident could occur in their working programs.

Job engagement is a psychological status to staff at work, which includes physical, cognitive, emotional perspectives etc. Job engagement plays an important role not only to staff’s well-being, but also to organizational success and achievement [9].

Valuing human reliability and avoiding human factor accidents is an essential task for organizations to survive and develop. Thus, organizational managers should optimize Man-Machine-Environment Systems (MMES) at workplaces, and pay attention to staff’s psychological and physiological needs. The job engagement predicting model can support organizational managing strategies, such as exploring good ways to resource matching, reasonable arranging job payload, reducing alienation sense with EAP [10], improving treatment satisfied by organizational culture, guarding against unsafe operation when technical staff might be in a case of mental fatigue.

To conclude, recognizing the significance of technical staff’s Job engagement and influencing factors would be helpful with safety management or human resources program. R&D organizations or enterprises will be benefitted from safe and healthy environment so that they can more effectively do science and technology research and produce commodities for human society.

5.3 Job Engagement on Expending and PES Examined

Professional efficiency was an original subscale of MBI-GS Scale. However, some scholars consider it as a part of job engagement [3]. To examine the hypothesis, in this study, the professional efficiency scale (PES) was edited and operated by the author. PES was verified through using exploring factor analysis, and its internal consistencies were good at Cronbach’s (α > 0.74). The result displays that PES positively and highly correlated with UWES-C (r = 0.72, p < 0.01, Bilateral inspection), and reveals that professional efficiency not only has an impact on the job engagement but also could be an expending dimension of the job engagement scale.

6 Conclusions

With interviews and questionnaires, the data and information was collected and calculated by using SSPS software. This study’s conclusions are as following:

  • Job engagement became available to describe psychological status of the technical staff at work;

  • UWES-C is reliable and effective, in terms of reasonably and objectively measuring technical staff’s job engagement. A norm criteria of evaluating job engagement needs to be set up in future.

  • UWES-C and job engagement prediction model can be helpful on research to human factor of safety programs as well as accident prevention.