Journal of Chinese Human Resources ManagementDoes training affect individuals' turnover intention? Evidence from ChinaYing Cheng Franz Waldenberger

Article information:To cite this document:Ying Cheng Franz Waldenberger, (2013),"Does training affect individuals' turnover intention? Evidence fromChina", Journal of Chinese Human Resources Management, Vol. 4 Iss 1 pp. 16 - 38Permanent link to this document:http://dx.doi.org/10.1108/JCHRM-10-2012-0024

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Does training affect individuals’turnover intention? Evidence

from ChinaYing Cheng

Management Department, School of Economics and Management,Chongqing University, Chongqing, People’s Republic of China, and

Franz WaldenbergerMunich School of Management, University of Munich, Munich, Germany

Abstract

Purpose – This study aims to investigate how meeting the training expectations of Chineseemployees influences their intention to stay with their company.

Design/methodology/approach – The authors collected data from 292 employees in eight Chineseorganizations. Applying partial least squares path modeling, they tested how fulfilling employees’expectations with regard to different training dimensions influences their level of job satisfaction,organizational commitment and perceived movement capital and how variations in these mediatingfactors in turn influence turnover intentions.

Findings – Chinese employees exhibit varying expectations with regard to the content, the organizationand the outcome of training. The relationship between meeting such expectations and turnover intentionsis mediated by job satisfaction, affective commitment, continuance commitment and perceived movementcapital. Fulfilling employees’ expectations with regard to specific skills and operational factors reducesturnover intentions. Fulfilling expectations with regard to general skills increases turnover intentions.Fulfilling expectations with regard to intra-organizational outcomes has a double-edged effect.Research limitations/implications – It is promising to analyze the relationship between trainingand turnover from an employee perspective. It is important to distinguish different dimensions oftraining and to consider mediated paths in order to depict various conflicting influences. This studycontributes to the understanding of Chinese employees’ attitude towards training, and to the literatureon HRM in China in suggesting that there is an indication of a definitive link between training andturnover, as there is in the West.Practical implications – Organizations in China need to consider employees’ pre-trainingexpectations when designing their training programs. Meeting employee expectations with regardto the design, organization and implementation as well as the outcome of training offers a promisingvenue to retain skilled employees.Originality/value – The authors contribute to the literature by explicitly expounding employees’comprehensive training expectations regarding their turnover intention. Differentiating fivedimensions of training and including four mediating factors, the authors are able to disentangleconflicting influences found in the extant literature.

Keywords Training expectations, Intra-organizational outcomes, Inter-organizational outcomes,Movement capital, Turnover intention, Training, Employees turnover

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/2040-8005.htm

The authors wish to acknowledge their anonymous managers for their generous support duringthe interview process of this study. They are grateful to the Editor, Greg G. Wang. Hisconstructive feedback inspired the writing. Appreciation also goes to the two anonymousreviewers. This study was partially supported by the Fundamental Research Funds from theCentral Universities in China, Project No.CQDXWL-2012-173.

Journal of Chinese Human ResourceManagementVol. 4 No. 1, 2013pp. 16-38q Emerald Group Publishing Limited2040-8005DOI 10.1108/JCHRM-10-2012-0024

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The relationship between training and turnover has received much attention amongresearchers since the seminal work of Becker (1962). Theoretical research has put forwardvarious arguments from human capital (Acemoglu and Pischke, 1999; Chun and Wang,1995), social exchange (Bartlett and Klein, 2001; Eisenberger et al., 2001), and humanresource management (HRM) perspectives (Appelbaum et al., 2000; Huselid, 1995) toinvestigate why and how training may influence turnover. Empirical studies havehighlighted the individual antecedents of turnover, such as gender and age (Lynch, 1991),contextual antecedents, such as labor market conditions (Day, 2005), and organizationalconditions like HRM practices (Guthrie, 2001). Yet, research has not been able to fullydisentangle the manifold and partly conflicting linkages between training and turnoverproposed in the literature (Sieben, 2007). Further, the literature has so far beenpredominantly focused on Western organizations although training and retention ofemployees is presently highly needed by Chinese organizations with difference context.

Purpose and significanceWe develop an analytical framework with a structure that is rich enough tosimultaneously assess various and partly conflicting influences of training on turnoverand to thereby clarify inconclusive results in the existing literature. Specifically, wecontribute to the literature in the following aspects.

First, previous studies were largely based on data either at the organizational level(Bartlett and Klein, 2001; Benson et al., 2004) or the market level (Acemoglu and Pischke,1999). These studies failed to develop a comprehensive understanding on trainingconsequences and their importance for individual turnover behavior (Benson et al.,2004). We develop a framework to advance the understanding of what employees expectfor their training and training outcomes, and analyze the consequences of meeting theseexpectations. In doing so, we examine the relationship between training and turnover atthe individual level following Benson and his colleagues (Benson, 2006; Pattie et al.,2006). We assume that training is a determinant of employee turnover decisions, andexamine key psychological processes that precede trainees’ turnover decisions. Byoutlining the fundamental processes, our research focuses directly on the immediatecauses of turnover decisions and highlights potentially effective interventions.

Second, earlier studies have mainly examined organizational commitment as amediating factor in the causal chain between employees’ training evaluation andturnover intention (TI) (Pattie et al., 2006). Other factors such as job satisfaction ( JS) andperceived chance for a better position elsewhere have not been thoroughly considered,although it has long been suggested that several proximal motivations are likely tocoexist in predicting turnover (March and Simon, 1958). We extend the existingliterature by providing a mediated model to examine how training is associated withmultiple psychological considerations that synergistically impact turnover decisions.

Finally, we conduct our empirical study in the Chinese context as opposed to theusual Western context (Pattie et al., 2006; Sieben, 2007). China faces a severe shortage ofqualified employees endangering the sustainability of its economic growth (Rein, 2010).Business organizations are urged to implement training programs, especially whenhigher education and vocational training is lagging behind (MOLSS, 2007). Meanwhile,well-educated younger generation employees seem to be less loyal to their company thantheir older counterparts and tend to have a higher turnover rate (Gu et al., 2010).Understanding how to train and retain employees is therefore especially relevant in

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the Chinese context. There is strong evidence that most current training programs areimported from the West and do not meet the needs and expectations in the Chinesecontext (Wang and Wang, 2006). Our study contributes to the understanding of Chineseemployees’ attitude towards training. The findings also add to the literature on HRM inChina in suggesting whether there is an indication of a definitive link between trainingand turnover, as there is in the West.

Theoretical framework and hypothesesEarlier theoretical literature shows that the effect of training on individuals’ voluntaryturnover can be positive or negative, depending on whether employees receive specific orgeneral skills training, on the distribution of training costs, or on the firms’ expectationsconcerning employee turnover and the policies of competing firms (Sieben, 2007). Morerecent efforts have sought to identify underlying psychological mechanisms (Benson,2006; Swider et al., 2010). However, studies directly theorizing and testing theimplications of such psychological mechanisms are still missing.

Training expectations and expectations fulfillmentStudies have suggested that an individual’s intrinsic interest in training is a relevantpsychological aspect positively affecting employee outcomes such as productivity,commitment and turnover (Au et al., 2008; Rowold, 2007). Following these studies, wedefine training expectations to reflect individuals’ interest in training.

We examine employer sponsored training in a broad sense, including on-the-jobtraining, formal and informal training, mentoring, coaching, or in-house training. Ourreview of the literature allowed us to identify three aspects of training important toemployees: training content, operational factors, and outcomes.

Training content comprises work-related knowledge and skills acquired throughtraining. In line with Becker (1962), we distinguish between specific and general skills.Individuals generally expect to learn job-specific skills crucial for performance,and company-specific norms and regulations to facilitate their work within theorganization (Chen, 2005). They also expect to acquire general skills that can improvetheir employability (Baruch, 2001).

We refer to operational factors as a general term covering the design, organization andimplementation of training, such as training providers, time, length, support and so on.Studies on training effectiveness have found the operation of training has impact ontrainees’ reactions and behavior (Colquitt et al., 2000). In analyzing employees’preferences for training options, Gan and colleagues (2009) reported that employees tendto expect qualified trainers, highly reputed training providers, and sponsoring of trainingexpenses. Literature also reveals that trainees usually expect a reasonable length oftraining time (Mumford et al., 1988). We consider organizational support as anoperational factor of training. Organizational support includes employers’ financialsupport and peer cohesion (Allen et al., 2003). Employees usually prefer a supportiveatmosphere in which reinforcement and feedback can be obtained from supervisors andcoworkers (Banks et al., 2004; Hurtz and Williams, 2009). Post-training tutoring was alsofound to be commonly expected by employees, as it assists employees in applying thetrained skills in their work (Scott, 2005).

We distinguish between intra- and inter-organizational training outcomes. Theliterature has associated intra-organizational training outcomes with better

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performance (Chen, 2005; Colquitt et al., 2000; Kozlowski et al., 2001), wage increase(Benson et al., 2004), and promotion (Benson et al., 2004). A major reason peopleparticipate in training is to improve performance (Chen, 2005; Kozlowski et al., 2001).Individuals may expect higher wages as a result of their higher productivity, and seekpromotion to a position that matches their newly acquired skills (Benson et al., 2004).Training outcomes are classified as inter-organizational if they enhance mobilitybetween employers. Employees often demand degrees and certificates at the end of atraining program as proof of their achievement to prospective employers (Loewensteinand Spletzer, 1999). One motivation to participate in external training programs such asMBAs is the possibility to network with peers and make contacts that can open doors forjob transitions (Seibert and Kraimer, 2001; Smith, 2010).

We group the training-related expectations into five dimensions. The fulfillment ofcertain expectations may promote TIs (e.g. general skills training), while some others(e.g. specific skills training) may reduce them. A list of the dimensions and the respectiveexpectations are provided in Table I.

Given the individual focus of this study, we do not intend to address cost andperformance aspects of training at the organizational level. Instead, we examine howemployees evaluate the training they receive. The psychological consequences oftraining depend on the degree of training expectations being fulfilled (Tannenbaum et al.,1991). To measure to what extent individuals perceive their expectations as beingfulfilled, we adopt the weighted discrepancy approach (Tannenbaum et al., 1991). Wedefine employees’ training expectations fulfillment (FTE) as the product of the perceiveddiscrepancy between what they expected and what they actually experienced, times theimportance attached to what they expected.

ContentSpecific skillsST 1 The trained skills can match my job requirementsST 2 The training is about specific skills, work norms, process, goals, and duties that are

needed in my job positionGeneral skillsGT 1 I can use the trained skills in other companiesGT 2 The trained skills can enhance my employability

Operational factorsOT 1 The instructor(s) have strong educational background or extensive practical

experienceOT 2 Supervisors, mentors and peers support the training I takeOT 3 The training is offered at a proper time and with proper lengthOT 4 Tutoring and coaching is available to assist in applying the trained skills in my workOT 5 Follow-up training and upgrade training are available

OutcomesIntra-organizational outcomesInT 1 The training help improve my performanceInT 2 The training has direct influence on my wage increaseInT 3 The training has direct influence on my promotionInter-organizational outcomesItT 1 On training completion, I can earn a degree. If not, I can include the training experience

in my resume to help demonstrate my job qualificationItT 2 In the training seminars or classes, I can get to know some people in my profession

Table I.Individuals’ training

expectation: afive-component model

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Turnover intentionWe specify turnover as the intention instead of the actual decision to leave thecompany. TI precedes the decision to leave. As the actual decision may be affected byadditional factors (Lee et al., 2008), focusing on intention lends itself to a morestraightforward interpretation. Also, the decision to leave is a binary variable whereasintention can be measured along a scale. This allows us to detect influences by degrees.

Mediating variablesThe literature suggests a number of mediating variables connecting training andturnover (March and Simon, 1958). Our hypotheses are derived from two related bodiesof literature. The first are studies investigating the influence of training onorganizational commitment (Banks et al., 2004), JS (Lee and Bruvold, 2003), and themarketability of skills (Trevor, 2001). The second comprises research examiningorganizational commitment (Maertz et al., 2007), JS (Wright and Bonett, 2007)and movement capital (Trevor, 2001) as antecedents of turnover. Moreover, Tsui andcolleagues (1997) further identified two types of exchanges between employees andemployers. One resembles a pure economic exchange (continuance commitment (CC)),while the other represents a social exchange relationship (affective commitment (AC)).Combining these results we focus on four mediating variables: JS, AC, CC and perceivedmovement capital.

HypothesesEarlier studies have shown that fulfilled expectations contribute to JS (Mitchell et al.,2001). We consider the result can be extended to all five dimensions of training. In otherwords, if employees feel their expectations for training are met, they will be morecontent with their job. This is to hypothesize that:

H1. For each component of training expectations, a higher degree of fulfillmentwill lead to a higher level of JS.

JS has been recognized as a primary factor influencing employees’ TI (Griffeth et al.,2000). In order to test the effect of JS in the Chinese context, we hypothesize that:

H2. A higher level of JS is likely to reduce an employee’s intention to leave.

The social exchange perspective suggests that caring and positive regard foremployees’ training and development interests can enhance AC (Tsui et al., 1997).Employees may feel emotionally attached to their company when they receive care andattention (Rhoades et al., 2001), they will establish a sense of loyalty towards theorganization (Cheng and Stockdale, 2003). Hence we hypothesize:

H3. For each component of training expectations, a higher degree of fulfillmentwill lead to a higher level of AC.

H4. A higher level of AC is likely to reduce an employee’s intention to leave.

CC suggests that employees’ loyalty to their company is based on concerns of losingsomething valuable if moving to another employer (Vandenberghe et al., 2007, 2011).By definition, specific skills represent an asset whose value will be lost when moving toanother employer (Becker, 1962). This motivates:

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H5. A higher degree of fulfilled expectations with regard to specific skills will leadto a higher level of CC.

Studies have shown that CC reduces turnover (Cheng and Stockdale, 2003). We includethe corresponding hypothesis in order to capture the effect within our model and to testwhether it holds in the Chinese context. We hypothesize that:

H6. A higher level of CC is likely to reduce an employee’s intention to leave theorganization.

Trevor (2001) has defined movement capital as attributes enhancing an employee’smobility. Human capital theory predicts that movement capital depends primarily onthe nature of learned skills. Namely, specific skills are not transferable to otherorganizations while general skills are portable (Becker, 1962). This suggests:

H7. A higher degree of fulfilled expectations to specific skills will impairemployees’ perception of their movement capital.

H8. A higher degree of fulfilled expectations to general skills will enhanceemployees’ perception of their own movement capital.

The perception of movement capital also depends on intra-organizational outcomessuch as higher wages and promotion, for these may serve as indicators of betterperformance for other employers (Allen and Griffeth, 2001). Thus, we have:

H9. A higher degree of fulfilled expectations to intra-organizational outcomes willenhance employees’ perception of their movement capital.

Studies in social capital show that professional contacts play an important role in jobchanges (Seibert and Kraimer, 2001). Evidence also supports that employees with moreexternal networks have shorter duration of group membership and join more newgroups (McPherson et al., 1992). Hence, we hypothesize that:

H10. A higher degree of fulfilled expectations to inter-organizational outcomes willenhance employees’ perception of their movement capital.

Empirical studies suggest that the ease of movement may induce employees to quit(Trevor, 2001) or at least search alternatives outside (Swider et al., 2010). Again, to testwhether it holds in the Chinese context, we hypothesize that:

H11. A higher perceived movement capital will increase an employee’s intention toleave.

Figure 1 summarizes the structure of the hypothesized linkages between the FTE withregard to the five training dimensions, the four mediating variables and TI.

MethodSamples and procedureWe tested the hypotheses in two steps. In the first step, we adopted a conveniencesampling approach. The first author contacted HR managers through her network.Eight managers were invited for interviews (Table II). We conducted interviews inChinese through webcam, each for 45 minutes. During the interviews, we presented themanagers with a list of training expectations derived from the literature, and asked

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if their employees had these expectations. Based on their input, we developed aquestionnaire and e-mailed it to them for their further comments. We made somemodifications afterwards. For instance, we re-arranged the sequence of the questionsaccording to their significance as suggested by the managers.

A pilot survey was subsequently conducted. A manager from a state-ownedconstruction firm assisted us to distribute our questionnaire to 30 employees in fivedepartments by convenience sampling. We asked the participants after each question forcomments on the clarity of the wording. The feedback resulted in further modifications.For example, the two original items on the expectations of “upon training completion, I canearn a degree” and “if degrees are not available, I want the training to be included in myresume to demonstrate my job qualification” were combined into one item, becauserespondents felt that the questions were too specific or had too much overlap. The finalversion of the questionnaire was first prepared in Chinese. For disseminating the results inEnglish, we invited an English native-speaker with a PhD in business studies to check theoverall conceptual equivalence of Chinese and English translation throughout the process.

Number of managers Company ownership City Industry Interview

1 State-owned Beijing Construction U

3 Foreign-owned Chengdu IT service U

4 State-owned Chongqing Banking U

1 State-owned Chongqing Manufacturing1 Private Chongqing Manufacturing1 Foreign-owned Shanghai Manufacturing1 Private Shenzhen Construction

Table II.Managers who helpedto distribute thequestionnaire

Figure 1.Hypothesized model

Expectations and Expectations Fulfillment

JobSatisfaction

AffectiveCommitment

PerceivedMovement Capital

ContinuanceCommitment

Expectations forSpecific Skills Training

Fulfilled

Expectations forGeneral Skills Training

Fulfilled

Expectations forOperational Factors

Fulfilled

Expectations for Intra-organizational

Outcomes Fulfilled

Expectations for Inter-organizational

Outcomes Fulfilled

TurnoverIntention

Discrepancy

Significance

Discrepancy

Significance

Discrepancy

Significance

Discrepancy

Significance

Discrepancy

Significance H1

H2

H3H4

H5

H6

H7

H8

H9

H10

H11

Mediated Model

Note: Variables in the dark circles are the hypothesized mediators

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Next we collected data using the questionnaire to test the hypotheses. During the lastquarter in 2010, we contacted 12 managers including the previous eight for datacollection (Table II).

The survey language was in Chinese. To maximize the responses and minimize thesurvey administration, we deployed the survey in both paper-pencil and online formats,allowing them to choose a convenient form to respond. Four firms chose the onlineversion and forwarded the web link to their employees; the rest took the hardcopyversion and distributed the questionnaires to their employees. We also sent eachmanager a memo explaining the purpose and confidentiality of the survey. A randomdrawing prize was set up to encourage participation. Four managers helped further toforward the questionnaires as well as a letter that explained the survey purpose andassured the anonymity and confidentiality of the responses to their business partners. Inthis way, the survey reached a larger population. A follow-up letter was sent two weeksafter the survey. Of the 2,700 distributed surveys, a total of 576 responses were returned,representing a response rate of 21.3 per cent. Our analysis showed that there was nosignificant difference between respondents and non-respondents with regard toindustrial type, job positions and company locations.

Measure: formative and reflective constructsWe differentiated formative and reflective constructs based on the methodologicalliterature (Diamantopoulos and Winklhofer, 2001; Jarvis et al., 2003). When a construct isformative, it captures a causal relationship from the indicators to the construct, whereasa reflective construct reflects a causal relationship from the construct to the indicators.

For example, perceived discrepancy in specific skills training (PDST) is a formativeconstruct measuring the attributes of one’s expectations on specific skills training. It askedthe respondents to report whether they held respective expectations (Table III). Theexpectations serve as indicators of the construct and are not interchangeable. Together,they measure the conceptual domain of specific skills training. The rest of the trainingexpectations-related constructs were measured in a similar manner. Significance ofspecific skills training expectations (SST) was measured by asking respondents to rate theimportance of the specific expectations. We adopted six items to measure JS (Table III).While no major antecedents of JS may be detected by the items, they express individuals’content with the present jobs. Furthermore, as these items are interchangeable inmeasuring the same construct, they are considered reflective.

We summarize the constructs and their measurement in Table III. All measures werein Likert five-point scales from 1 (strongly negative) to 5 (strongly positive) unlessotherwise noted.

Corporate training was measured by whether a respondent had completed orwere undergoing any of the “specific training programs”, “mentoring”, “learning bydoing” and “systematic observation of peers’ work”. Finally, we collected data onrespondents and associated organizational demographics as control variables, includingeducation, age, work experience, gender, and job title, as well as organization size,location, and ownership type.

Data analysisUpon examining the returned responses, we disqualified 284 responses becausethey either had significant incomplete portions or reported that training was

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self-sponsored. This resulted in a sample size of 292, equivalent to a 50.7 per cent ofeffective response rate.

We adopted a partial least squares (PLS) regression analysis (Wold, 1982) andSmartPLS 2.0 M3 (Ringle et al., 2005) to test the hypotheses. Our choice of analyticalmethod was based on the following considerations. First, a sample size of 292 is

Construct Measurement Reference

FormativeSpecifictraining

Discrepancy (PDST) ST1-2(TableI)

Becker (1962)Significance (SST)

Generaltraining

Discrepancy (PDGT) GT1-2(TableI)

Becker (1962), Baruch (2001),Benson (2003)Significance (SGT)

Operationalfactors

Discrepancy (PDOT)Significance (SOT)

OT 1-5(TableI)

Gan et al. (2009), Mumford et al.(1988), Banks et al. (2004, 2003),Scott (2005)

Intraoutcomes

Discrepancy (PDInT)Significance (SInT)

InT 1-3(TableI)

Chen (2005), Colquitt et al. (2000),Kozlowski et al. (2001)

Interoutcomes

Discrepancy (PDItT)Significance (SItT)

ItT 1-2(TableI)

Loewenstein and Spletzer (1999),Seibert and Kraimer (2001)

ReflectiveJobsatisfaction

1. I am proud of talking about my job2. I feel real enjoyment in my work3. I feel fairly satisfied with my present

job4. I am enthusiastic about my work5. I consider my job rather unpleasant

(reverse coded)6. I am worried about my future in the

present organization (reverse coded)

Brayfield and Rothe (1951),Weiss et al. (1967)

Affectivecommitment

1. I feel a strong sense of belonging tomy organization

Rhoades et al. (2001)

2. I really feel that problems faced bymy organization are also my problems

Meyer and Allen (1997)

Continuancecommitment

1. It is really difficult to leave mycompany, although I want to

Bentein et al. (2005), Buchanan(1974), Cook and Wall (1980)

2. It will not be costly to leave mycompany

Perceivedmovementcapital

1. I am able to acquire a number of jobsin other companies

2. I received some unsolicited job offersfrom other companies when I am onthe present job

3. It is easy for external employers to tellthat I am a qualified employee

Day (2005), Eby et al. (2003),Lee et al. (2008)

Turnoverintention

1. I am frequently thinking of quittingthis job

Cummann et al. (1979)

2. I plan to look outside my presentcompany for a new job within the nextyear

3. I do not intend to quit my present jobfor the next year (reverse coded)

Table III.Constructs andmeasurement

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relatively small and often cannot meet the strict criteria for covariance-based structuralequation model (CBSEM) that usually requires larger sample size and multivariatenormal distribution (Haenlein and Kaplan, 2004). Second, PLS is particularly suitable fordata analysis during the early stage of theory development where the theoretical modeland its measures are not well formed (Tsang, 2002). Third, the core constructs in thestudy are formative hierarchical constructs (Edwards, 2001) as the five components ofFTE are co-determined by discrepancies and values of the respective expectations.Literature suggests that it is difficult to identify and measure formative constructs andhierarchical models in CBSEM, instead PLS-based SEM approach is moreimplementable (Wetzels et al., 2009).

ResultsFollowing the guidelines and criteria by Chin (2010), we report the results in formativemodels, reflective models, and structural models, respectively.

Formative modelsThe literature has recommended that formative measurement model be assessed for itscontent validity, indicator specification, indicator collinearity, and external validity(Diamantopoulos and Winklhofer, 2001). The content validity on FTE was assured notonly from the literature, but also through the pretest among the HR managers andemployees. Thus, we focus on the remaining aspects of the model in the subsequentpresentation.

With PLS method, the indicator specification can be assessed by their the weightsand significance, together with multicollinearity among the indicators (Diamantopoulosand Siguaw, 2006). The results in Table IV showed that except for one indicator, theSInT3, the variance inflation factor (VIF) values of all the other measures were lowerthan the threshold of 3.3, suggesting that multicollinearity did not pose a threat in thedata. The weights for most variables were significant. We maintained variables withinsignificant weights in the model to avoid altering the conceptual domain as suggestedby Javis and colleagues (2003).

Reflective modelsConsistent with previous studies, we carried out a principal component analysis (PCA) toassess the underlying factor structure for variables in the reflective construct (Gotz et al.,2010). We used SPSS 19.0 and found that the original pool of 16 items for reflectiveconstructs was subjected to an exploratory factor analysis (EFA) with varimax rotation.As a result, five factors were extracted accounting for 74.42 per cent of the overallvariance. Examining the items loading, we found that three variables, JS5, JS6, and TI3,had loadings less than 0.6 on all factors. They were removed from further analysis.

In PLS, we examined the item reliability by factor loadings (Haenlein and Kaplan,2004). As shown in Table V, all loadings of the reflective variables exceeded therecommended threshold of 0.7 (Tenenhaus et al., 2005).

We applied Fornell and Larcker’s (1981) composite reliability (CR) for theconstruct reliability and average variance extracted (AVE) for the convergent validityin this study. Table VI summarizes the CR and AVE, as well as the Cronbach’s a for thereflective constructs. According to the recommended threshold (Gotz et al., 2010), all themeasurement models appeared satisfactory.

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Construct Indicators Mean SD Weights VIF

PDST PDST 1 3.59 1.132 0.596 * * * 1.767PDST 2 3.19 1.188 0.501 * * 1.767

SST SST 1 4.07 1.127 0.715 * * * 1.691SST 2 3.47 1.017 0.378 * * * 1.691

PDGT PDEG 1 3.27 1.129 0.646 * * * 1.518PDEG 2 3.42 1.070 0.475 * * * 1.518

SGT SGT 1 3.56 1.137 0.534 * * * 1.361SGT 2 3.86 1.224 0.614 * * * 1.361

PDOT PDOT 1 3.35 1.101 0.585 * * * 2.002PDOT 2 3.46 1.082 0.081 * * * * 1.634PDOT 3 3.46 1.053 0.225 * * 1.634PDOT 4 2.96 1.153 0.187 * 1.858PDOT 5 2.87 1.134 0.158 * 1.534

SOT SOT 1 3.92 1.182 0.401 * * * 3.119SOT 2 3.89 1.165 0.450 * * * 2.563SOT 3 3.76 1.157 0.151 * * * * 2.409SOT 4 3.59 1.225 20.039 2.668SOT 5 3.67 1.101 0.178 * * 1.740

PDInT PDInT 1 3.37 1.065 0.402 * * * 1.735PDInT 2 4.25 1.567 0.021 1.555PDInT 3 3.74 1.861 20.241 * 1.641

SInT SInT 1 3.69 1.082 0.367 * * 2.283SInT 2 3.44 1.198 0.243 * * * * 3.217SInT 3 3.45 1.224 20.054 3.410

PDItT PDItT 1 3.03 1.307 0.569 * * * 1.158PDItT 2 3.20 1.170 0.639 * * * 1.158

SItT SItT 1 3.36 1.209 0.454 * * * 1.366SItT 2 3.56 1.125 0.686 * * * 1.366

Notes: Significant at: *p , 0.05, * *p , 0.01, * * *p , 0.001 and * * * *p , 0.10; SD – standarddeviation; bootstrapping results; n ¼ 292; sample ¼ 1,000

Table IV.Results for formativeconstructs

Indicators JS AC CC PMC TI

JS 1 0.885 0.513 20.324 0.538 0.141JS 2 0.901 0.514 20.326 0.514 0.132JS 3 0.815 0.519 20.277 0.351 0.170JS 4 0.747 0.439 0.383 20.620 20.206AC 1 0.400 0.839 20.326 0.523 0.108AC 2 0.638 0.886 20.399 0.552 0.122CC1 0.426 0.369 2 0.816 0.351 0.420CC2 20.280 20.339 0.853 20.510 20.436PMC 1 0.643 0.575 20.475 0.940 0.293PMC 2 0.621 0.590 20.439 0.927 0.188PMC 3 0.507 0.523 20.498 0.839 0.354TI 1 0.176 0.048 20.455 0.223 0.886TI 2 0.182 0.191 20.445 0.318 0.869

Note: n ¼ 292

Table V.Cross-loadings: reflectiveindicators

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For discriminant validity, we used square root of the AVE (Fornell and Larcker, 1981).The suggested value was to exceed the variables’ intercorrelations with the othervariables in the model. This criterion was also met as reported in Table VII.

Hierarchical constructsWe have five hierarchical constructs composed of the five dimensions of the FTE.According to classification criteria for hierarchical constructs, these hierarchicalconstructs are Type IV constructs: they are formative and composed of other first-orderformative constructs, discrepancy and significance ( Jarvis et al., 2003).

We examined the Type IV constructs through two steps (Yi and Davis, 2003): first,we excluded the hierarchical constructs from the analysis, but included the first-orderconstructs and linked them to the respective consequences variables. The factor scoresof the first-order constructs were obtained from the SmartPLS software. Next, weassigned the factor scores as the variables representing the hierarchical constructs.The weights of the variables were considered representatives of the correlationbetween the variables and hierarchical constructs. They were further multiplied by thecorresponding first-order factor scores and then summed to derive the composite ofsecond-order scores. Similar to the first-order constructs, the hierarchical models werealso tested for their validity and reliability. Table VIII reports the results, showing allthe first-order formative constructs were valid for the respective hierarchicalconstructs. The five hierarchical constructs were then related to the rest of thevariables and analyzed within the structural model.

Descriptive statisticsWe reported the means, standard deviations, reliability coefficients, and zero-ordercorrelations of the variables in Table IX. The five dimensions of FTEs are correlatedsignificantly with TI. Four mediating variables were all correlated significantly withthe TI.

JS AC CC PMC TI

JS 0.795AC 0.612 0.863CC 0.418 0.423 0.892PMC 0.657 0.623 0.520 0.901TI 0.204 0.134 0.513 0.306 0.877

Notes: n ¼ 292; diagonal elements (italic) are the square root of AVE by latent constructs

Table VII.Reflective measurementmodels: latent variable

correlations

Construct AVE CR Cronbach’s a

JS 0.632 0.887 0.832AC 0.744 0.853 0.658CC 0.796 0.699 0.696PMC 0.811 0.930 0.886TI 0.770 0.870 0.761

Note: n ¼ 292

Table VI.Reflective construct

measurement results

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Common method varianceTo test potential common method bias, we took the following two steps.First, a Harman’s one-factor test (Podsakoff and Organ, 1986) was conducted on theten variables including the five dimensions of FTE, JS, AC, CC, PMC, and TI. Resultsshowed that the most covariance explained by one factor is 11.45 per cent, indicatingthat common method biases are unlikely to contaminate the data (Williams et al., 2003).Second, we included in the PLS model a common method factor with indicatorsincluding all the principal constructs’ indicators and calculated each indicator’svariances explained by the principal construct and by the method factor (Liang et al.,2007). The average substantively explained variance of the indicators was 0.561, whilethe average method based variance is 0.004 (Table X). Additionally, most method factorloadings were not significant. The small magnitude and insignificance of methodvariance suggested that the method was unlikely to be a serious concern.

Hypotheses testingIn examining the mediated model, we compared the full mediated model against acompeting model that predicted a partial mediation. The partially mediated model hasthe same paths as the hypothesized fully mediated one and additionally includes fivedirect paths from the distal antecedent factors to TI. As the models are nested, they canbe compared statistically using PLS results (Rai et al., 2006). The R 2 for both models,R 2 ¼ 28.3 per cent, R 2 ¼ 28.6 per cent, were acceptable[1]. Following Chin (2010),we calculated F-statistics of the change in R 2 values in order to identify a better fit.This resulted in an F (5, 282) ¼ 0.24, indicating that the additional variance explained byintroducing the direct path from the FTE components to TI did not significantly add tothe variance explained by the four dependent variables. The full mediation model is thusconsidered a better fit.

We then analyzed the path coefficient. The PLS results (Figure 2) showed that theFTE explained 58.8 per cent of the variance in individuals’ JS, 42.9 per cent in AC,

Weights

Fulfillment of specific skills training expectationsIndicator 1: PDST 0.435 * * *

Indicator 2: SST 0.641 * * *

Fulfillment of general skills training expectationsIndicator 1: PDGT 0.397 * * *

Indicator 2: SGT 0.694 * * *

Fulfillment of operational issues expectationsIndicator 1: PDOT 0.502 * * *

Indicator 2: SOT 0.596 * * *

Fulfillment of intra-organizational outcomes expectationsIndicator 1: PDInT 0.549 * * *

Indicator 2: SInT 0.601 * * *

Fulfillment of inter-organizational outcomes expectationsIndicator 1: PDItT 0.312 * *

Indicator 2: SItT 0.786 * * *

Notes: Significant at: *p , 0.05, * *p , 0.01, * * *p , 0.001 and * * * *p , 0.10; bootstrapping results;n ¼ 292; sample ¼ 1,000

Table VIII.Assessment ofsecond-order formativeconstructs

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Mea

nS

D1

23

45

67

89

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501

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10.

101

4.F

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3.61

21.

371

0.10

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32

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95.

FE

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455

1.06

60.

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0.04

52

0.11

80.

864

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557

1.20

60.

094

0.03

62

0.10

80.

869

0.81

57.

FE

InT

3.50

81.

144

0.11

80.

0562

0.13

20.

776

0.80

70.

779

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1.15

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133

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0.77

80.

784

0.77

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3.97

31.

110

0.12

80.

0192

0.13

90.

730

**

0.69

80.

729

**

*0.

668

*0.

681

10.

AC

3.16

21.

189

0.02

02

0.03

42

0.04

50.

582

*0.

507

*0.

647

**

*0.

485

0.52

30.

612

11.

CC

1.17

31.

1912

0.09

22

0.05

90.

1512

0.56

6*

*2

0.56

92

0.57

22

0.49

82

0.48

02

0.40

80.

598

12.

PM

C3.

589

1.16

60.

1732

0.04

90.

738

0.73

8*

**

0.66

10.

708

0.66

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647

0.65

70.

623

20.

520

13.

TI

2.67

31.

160.

1742

0.04

72

0.14

00.

306

**

*0.

321

*0.

288

**

0.25

7*

0.25

0*

0.20

4*

*0.

335

**

*2

0.51

3*

**

0.30

7*

Note

s:

Sig

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tat

:* p

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£#

800”

and

3¼

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and

2–

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Table IX.Descriptive statistics

and correlations

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Construct Indicator Substantive factor loading (R1) R12 Method factor loading (R2) R22

FTEs DisS 0.435 * * * 0.189 0.079 * * * 0.006SigS 0.641 * * * 0.411 0.083 0.007

FTEg DisG 0.397 * * * 0.158 0.073 0.005SigG 0.694 * * * 0.482 0.080 0.006

FTEop DisOp 0.502 * * * 0.252 0.078 0.006SigOp 0.596 * * * 0.355 0.080 0.006

FTEintra DisIntra 0.549 * * * 0.301 0.070 0.005SigIntra 0.601 * * * 0.361 0.072 * * 0.005

FTEinter DisInter 0.312 * * * 0.097 0.067 0.005SigInter 0.786 * * * 0.618 0.077 0.006

JS JS2 0.885 * * * 0.783 0.061 * 0.004JS3 0.901 * * * 0.812 0.060 0.004JS4 0.815 * * * 0.664 0.043 0.002JS6 0.747 * * * 0.558 -0.071 0.005

AC AC1 0.839 * * * 0.704 0.054 0.003AC2 0.886 * * * 0.785 0.061 0.004

CC CC1 -0.816 * * * 0.666 0.049 0.002CC2 0.853 * * * 0.728 -0.055 * 0.003

PMC PMC1 0.94 * * * 0.884 0.077 0.006PMC2 0.927 * * * 0.859 0.075 0.006PMC3 0.839 * * * 0.704 0.066 * * * 0.004

TI TI1 0.886 * * * 0.785 0.029 * * 0.001TI2 0.869 * * * 0.755 0.035 0.001

Average 0.656 0.561 0.054 0.004

Notes: Significant at: *p , 0.05, * *p , 0.01, * * *p , 0.001 and * * * *p , 0.10; bootstrapping results;n ¼ 292; sample ¼ 1,000

Table X.Common methodvariance analysis

Figure 2.Model evaluation result:the full mediated model

0.155

0.159

0.129

0.566*

JobSatisfactionR2 = 0.588

AffectiveCommitmentR2 = 0.429

Perceived MovementCapital

R2 = 0.567

ContinuanceCommitmentR2 = 0.321

Expectations for SpecificSkills Training Fulfilled

Expectations for GeneralSkills Training Fulfilled

Expectations for OperationalFactors Fulfilled

Expectations for Intra-organizational Outcomes

Fulfilled

Expectations for Inter-organizational Outcomes

Fulfilled

TurnoverIntention

R2 = 0.283

–0.140*

–0.175**

–0.508***

–0.150***

0.245†

0.145†

0.272**

0.606***

0.104

0.197†

Firm Size

FirmOwnership

Gender

–0.047

–0.174

–0.140

Notes: Significant at: *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.10;bootstrapping results; n = 292; sample = 1,000; confirmed relationships arerepresented by bold arrows, and the hypothesized relationships that were notsupported are represented by dot arrows

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32.1 per cent in CC, and 56.7 per cent in perceived movement capital. Overall, theseantecedents accounted for 28.3 per cent of the variance in TI.

Relationship represented by a path coefficient . 0.1 cannot be neglected in the PLSresults (Bossow-Thies and Albers, 2010). Three aspects of FTE were significantly andpositively related to JS, FTE for specific skills training (b ¼ 0.245, p , 0.1), FTE foroperational factors (b ¼ 0.272, p , 0.01), and FTE for inter-organizational outcomes(b ¼ 0.155), supporting H1. And the significant and negative relationship between JSand TI (b ¼ 20.140, p , 0.05) confirmed H2. Further, H3 predicted that eachdimension of FTE enhance trainees’ AC, respectively. The results showed that, exceptfor general training, the FTE for specific skills training (b ¼ 0.159), operational factors(b ¼ 0.606, p , 0.01), intra-organizational outcomes (b ¼ 0.104) andinter-organizational outcomes (b ¼ 0.129) significantly and positively affect AC,hence H3 was largely supported. The result on the relationship between TI and AC wasalso significant and negative (b ¼ 20.175, p , 0.01), supporting H4. We also found thepath predicted by H5 on the relationship between specific training and CC positive andsignificant (b ¼ 0.566, p , 0.05) and there was strong evidence showing a significantand negative relationship between CC and TI (b ¼ 20.508, p , 0.001), thus H5 and H6were supported.

In terms of FTE on specific skills training, it had a significant and positive effect onPMC, refuting H7. Consistent with the hypotheses, we found a significant and positiveeffect of FTE for general skills training on PMC (b ¼ 0.145, p , 0.05), and a significantand positive relationship between FTE on intra-organizational outcomes and PMC(b ¼ 0.197, p , 0.05). We did not find evidence that FTE on inter-organizationaloutcomes enhanced PMC. These results support H8 and H9, but not H10. Finally, H11was also supported with a positive and significant sign for the paths from PMC to TI(b ¼ 0.150, p , 0.001).

DiscussionOur study investigated how Chinese employees’ level of JS, AC, CC and perceivedmovement capital (MC) changed as a result of variations in FTE and how changes inJS, AC, CC and MC in turn influenced TI.

We distinguished expectations of employees with regard to five training dimensions.To our knowledge, this is the first study explicitly expounding employees’comprehensive expectations for training. The results confirmed that employees heldexpectations not only with regard to training content, but also the outcome, and the waytraining was designed, organized and implemented. Employees also weighed theimportance of the various training dimensions differently.

Based on the concept of FTEs, we examined a mediated model to assess the effect ofFTE on TI. We found that specific skills training had a significant impact on howemployees felt about their jobs and employers. Yet we did not find evidence that specifictraining indirectly reduced TI by lowering MC (H7). A possible reason could be that we didnot clearly explain specific training as firm specific; the respondents might thus haveinterpreted it as position or industry specific and therefore transferable across employers.

We found a positive effect of general skills-related FTE on PMC. This supports theargument that such training can promote turnover by strengthening employees’ confidencein their marketability. Yet we did not find evidence that general skills training wasassociated with JS or AC. A possible interpretation is that there may be some variations in

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respondents’ expectations, so the variance maximization estimation of the path coefficientanalysis was unable to reach a significant level. A more practical interpretation is that ourrespondents tend to consider general skills training a general duty of their employers ratherthan an additional benefit. If this was the case, social exchange rules might not apply to thiscontext, and employees would not feel more committed to their organizations.

Our paths analysis results highlighted that operational factors are important. Thecorresponding FTE is strongly positively related to JS and AC, thus indirectly reducing TI.This adds a new result to the literature. It is possible that the finding is associated with theChinese context (Rousseau and Fried, 2001; Whetten, 2009). Because supportive colleaguesas well as work and learning environment are important to Chinese employees and Chineseculture values guanxi and content (Hui et al., 2004), Chinese employees tend to stay in amore intimate environment and also to avoid the uncertainty in a new organization.

We did not find evidence that receiving academic degrees or establishingprofessional contact would enhance trainees’ confidence in their movementcapabilities. A possible reason may be that the theoretically postulated effect may notdepend so much on how training is evaluated, but rather on what was actually obtained.Or the theoretically postulated effect may not depend so much on the perception or thediscrepancy, but rather on what was actually obtained through training, independent ofwhether this was expected and how important it was.

The results showed that FTE related to intra-organizational outcomes are not onlypositively linked to AC, but also to MC. This implies that performance improvement,wage increase or promotions are two-edged swords. So far existing Western literaturehas only acknowledged that these HRM functions are effective in retaining trainees(Appelbaum et al., 2000; Benson et al., 2004). Few have considered the possibility that thesame HRM practice may also facilitate job mobility (Allen and Griffeth, 2001). Ourfindings warrant further research.

Practical implicationsOur findings show that organizations need to take employees’ training expectations intoaccount. Such expectations relate not only to training content, but also to how training isdesigned, organized and implemented and what results the individual obtains from it.

HR managers usually want to retain trained employees. For the Chinese employeessurveyed we are able to propose two potentially effective interventions for companies toaddress their training investment dilemma. One is to reward employees’ trainingachievement with higher wages and promotion. These practices are not new in the West,but organizations in China have not been successful in linking training to other HRMpractices (Hutchings et al., 2009). Our results confirm Chinese employees’ strong interest inthese practices. Fulfilling them will increase AC. However, as our results revealed, suchrewards represent double-edged swords, because they also increase marketability of skills.Managers need to find out how to effectively counterbalance the mobility enhancing effect.

Ensuring that training is well designed, organized and implemented is the secondintervention that can outweigh the mobility effect. Our results suggest that operationalfactors, including learning support and post-training tutoring can effectively lower TI.

Limitations and future researchA number of limitations associated with this study may offer insight for future research.First, we took individual TI as the dependent variable. There may be a social desirability

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bias (Podsakoff and Organ, 1986). Turnover is generally a sensitive topic, particularly inthe employee-supervisor relationship. Our data was collected through senior managers.Although this was done anonymously, respondents might still have underreported theirreal intention to please their supervisors. The effect might be especially relevant giventhe pronounced power-distance trait in Chinese culture (Hofstede et al., 2010). Moreover,TI does not necessarily translate into actual behavior (Swider et al., 2010). The results ofour study should therefore be interpreted cautiously with regard to the prediction ofactual turnover behavior.

Second, our data were collected in eight companies; each has its own “context” inownership type, industries, and geographic location with different labor marketsituations. It is likely that this study, while capturing the overall picture, may miss somecontext-specific influences (Bryman and Bell, 2003). For example, the FTE and TIrelationship may be different according to the associated degree of job security,management styles or organizational attributes. Future research may further explorethe influence of such context factors.

Finally, our results highlighted that the design, organization and implementation oftraining are influential in determining employees’ post-training attitude towardsemployers. It seems that the influence of this aspect of training is greater than the effectof skills acquired and post-training outcomes. Given the design of the study, we wereunable to further identify whether the results were caused by the specific Chinesecontext or by other associated factors. Future research may consider a cross-countrydesign or testing our framework in a non-Chinese context.

Note

1. Due to space restriction, the estimation results of the partially mediated model are notreported, but available upon request.

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About the authorsYing Cheng is an Assistant Professor in the Management Department, School of Economics andManagement, Chongqing University, Chongqing, China. Her research focuses on thedevelopment of management capability, career development, leadership development, andbusiness ethics in China. Ying Cheng is the corresponding author and can be contacted at: ying.cheng.cqu@gmail.com

Franz Waldenberger is a Professor at the Munich School of Management,Ludwig-Maximilians-University Munich (LMU), Munich, Germany. His current researchfocuses on Japanese economy, human resource management and corporate social responsibility.

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