Training and Diffusion of Change: The Impact of Training and Proactivity on Change-Related Advice Giving

Abstract

This study investigates the effects of organizational change–related training on the diffusion of change through the employee advice network. It also examines the contribution of employee proactivity to the effects of that training. We compare trainees (N = 46) and nontrained peers (N = 47) doing similar work at pre- and posttest to examine how training and individual proactivity contribute to the change process. Results indicate that training is associated with increased change-related knowledge and skills and greater change-related advice giving (i.e., in-degree centrality). Additionally, proactivity is positively related to change-related advice giving and seeking (i.e., in- and out-degree centrality). Our findings also show that the effect of training on change-related advice giving is stronger for more proactive trainees than less proactive ones. We discuss our study’s theoretical and practical implications for both organizational change and employee proactivity.

Keywords

organizational change employee proactivity training diffusion of change advice network

Employee proactive involvement in the change process is often critical to active acceptance and implementation of organizational change (Oreg, Vakola, & Armenakis, 2011). In contrast to structural changes, the success of technological change depends on employee involvement in learning, using, and sharing new work procedures, as is the case of expanded use of information systems (Sashkin & Burke, 1987). Organizations often implement training programs in support of change efforts in order to provide employees with change-related knowledge and skills (Arthur, Bennett, Edens, & Bell, 2003; Blume, Ford, Baldwin, & Huang, 2010). As part of organizational change, training can serve as a vehicle for trainees to act as change agents (Lam & Schaubroeck, 2000) engaging them in the change process themselves. Training can thus be used to help individuals disseminate knowledge to their peers and diffuse the change throughout the larger organization (Keys & Bartunek, 1979; Rogers, 1995)—particularly, when training is intensive, costly, and not all employees can be trained.

Despite considerable research regarding the effects of training on skill acquisition (Arthur et al., 2003; Blume et al., 2010), less is known regarding how change-related training contributes to the dissemination of knowledge and skills from trainees to their peers (Bell, Tannenbaum, Ford, Noe, & Kraiger, 2017). Recent studies have identified the role of training as a change vehicle for change-related learning (Sartori, Costantini, Ceschi, & Tommasi, 2018), and examined the effect of a knowledge translation intervention on practitioners’ engagement in new clinical practices (Eames et al., 2018) As yet little is known about how training might contribute to the diffusion of organizational change. Nonetheless, training can play a special role in technological change if it contributes to the dissemination of change-related knowledge and skills (Jacobs & Russ-Eft, 2001). Training may influence the spread of change-related knowledge in several ways. One way training does so is through train-the-trainer interventions whereby select employees are targeted for training due to their role as future instructors of others in the organization. Another way is by training individuals in the knowledge and skills needed to introduce new work practices that they in turn share with the others with whom they work. In the context of technological change, the sharing of change-related information, particularly between trainees and nontrainees, occurs via the advice networks they use to communicate with others (Cross & Parker, 2004; Ibarra & Andrews, 1993; McGrath & Krackhardt, 2003). In an advice network, those organizational members who actively give advice to others are more central: Meaning they have more ties to others, reflecting social perceptions of the advice-giver’s competence and willingness to help others on job-related issues (Gibbons, 2004). At the same time, organizational members who seek change-related advice through their networks are inclined to be proactive in an effort to embrace the organizational change and adapt to it (Petrou, Demerouti, & Schaufeli, 2015). Since organizations may lack the resources to train all employees, diffusing knowledge and skills from trainees to others can play a critical role in change success. Moreover, an employee’s initiative to seek out change-related information can also contribute to a successful change. Previous research has identified the importance of change agents in the diffusion of the change (Lam & Schaubroeck, 2000), yet how training contributes to the diffusion of change is largely unknown (for a review, see Whelan-Berry & Somerville, 2010). This lack of knowledge is an important concern given that organizations frequently invest considerable resources in change-related training often with a limited benefit (Kotter, 1995).

In the context of technological change, this study aims to provide insight into how change-related training can contribute to the transfer of learning from trainees to the rest of the organization. We draw from research on proactive behavior (Grant & Ashford, 2008; Parker, Bindl, & Strauss, 2010) and social network theory (Borgatti & Cross, 2003; Casciaro et al., 2015; Krackhardt, 1987) to explain the process of change diffusion. Proactivity is the individual predisposition to bring about change within one’s self and/or the work environment (Crant, 2000; Parker et al., 2010), and plays a significant role in organizational change (Ghitulescu, 2012; Hornung & Rousseau, 2007). Proactive individuals are more likely to alter the prevailing status quo and improve existing procedures both by changing their own behavior and attitude and by altering the environment to introduce or support change than are less proactive ones (Parker et al., 2010). Social network research also offers insight into interpersonal influence and the diffusion of change-related information and attitudes. The efficacy of change agents derives not only from their skills but also from their network ties to others (Battilana & Casciaro, 2012; Lam & Schaubroeck, 2000). Thus, considering the proactivity and social network literature together may inform both theory and practice related to the role of training in change diffusion.

The present study examines how change-related training and employee proactivity influence the diffusion of change-related technological advice giving and seeking. Toward this end, it also investigates the potential moderating role of employee proactivity in the effect of training on the diffusion of change. The context of this study is the launching of a new enterprise-wide data management system by a large medical center. The goal of its training program was to enhance the trainee’s skills and knowledge in order to promote their effective participation in developing and using new working systems. The training was intended to support employee efforts to help identify and co-create new information technology applications and implement them within the health-care system. Its curriculum included training in using diverse sources of information to make decisions (evidence-based management), forecasting and optimization techniques, statistical analyses, and data mining (see the appendix).

This study makes four contributions. First, it addresses the effects of change-related training on relevant knowledge and skills and on the sharing of change-related information via data analytics–related advice networks. Second, it identifies an important condition for diffusion, the role of employee proactivity, and the role of training in facilitating its effects. Third, it advances understanding of the link between individual proactivity and change-related social behavior by providing empirical evidence of the effect of proactivity on use of advice ties. Last, through a research design using treatment and comparison groups, the study answers the call for greater use of controlled studies in assessing the effects of change (Barends, Janssen, ten Have, & ten Have, 2014) and greater attention to network and social interaction-based factors in change implementation (Stouten, Rousseau, & De Cremer, 2018).

Literature Review and Hypotheses

Training constitutes a common supporting intervention within a larger change process, particularly in the case of technological changes where new technical capabilities are required (Blume et al., 2010). In the change literature, training is recognized to serve several functions. Training can increase change-related knowledge and ability, which in turn facilitate change implementation (Arthur et al., 2003; Blume et al., 2010). It can help mobilize energy for change by helping individuals see the value and purpose of the planned change (Kotter, 1995). Prescriptive writers on change management such as Hiatt (2006) view training as essential when learning is needed to enact change. Both Hiatt (2006) and Kotter (1995) note that even employees motivated to change can lack sufficient skill to do so. Helping employees develop new skills and knowledge can thus be important to successful change (Hiatt, 2006).

Research evidence demonstrates the importance of individual learning and motivation in successful change (Oreg et al., 2011). A critical factor in behavioral change is having the requisite ability to engage in the new behavior (Ajzen, 1991). However, change-related uncertainty as experienced by change recipients can make it difficult to store, retrieve, and use new information (Schechter & Qadach, 2012). Training can help overcome change-related uncertainty thereby providing important support to change recipients (Beer, 1980) by enhancing their change-related self-efficacy (Kao, 2017). If training is provided in a fashion that brings with it valued knowledge and skills, including potential career opportunity and status, it can be particularly important in motivating employee commitment to both the organization and the change (Colquitt, LePine, & Noe, 2000; Martins & Kellermanns, 2004). Past research has shown that relevant training increases the individual’s use of new technology (Brings, Daun, Brinckmann, Keller, & Weyer, 2018; Gegenfurtner, Quesada-Pallarès, & Knogler, 2014). We hypothesize that change-related training will increase employee skills and knowledge relevant to the change. As such, we make the following hypotheses:

Hypothesis 1: Change-related training is expected to be positively related to the participant’s change-related knowledge and skills (a) and the use of new change-related work systems (b).

Training and the Diffusion of Change

Training can play a role in the diffusion of change. Change agents, individuals who promote or enable change, are important for the success of organizational change (Caldwell, 2003; Lam & Schaubroeck, 2000) since they are expected to encourage others to adopt new work practices (Van de Ven, 1986). Training can be used as a means of preparing trainees to act as change agents. When people are confident in their skills, they are more likely to take action than when they lack skills (Vroom, 1964). Particularly where it is difficult to train everyone in the organization, the training of those who will serve as change agents can help to efficiently diffuse knowledge and skills throughout the organization. Diffusion is broadly defined as “the spread of something within a social system” (Strang & Soule, 1998, p. 266). As conceptualized, this spread denotes flow or movement from a source to an adopter via communication and influence. Diffusion comprises many forms of social influence including contagion, mimicry, social learning, organized dissemination (Strang & Soule, 1998) as well as liking (Cialdini, 2007). In the context of technological organizational change, diffusion involves both the large-scale implementation of new ways of doing work and the flow of change-related knowledge and skill (Burke et al., 2011). This conceptualization differs from extant research on diffusion of innovation focusing on organization-level diffusion (Rogers, 1995) or training transfer emphasizing the posttraining application of learning to one’s own work context (Baldwin & Ford, 1988). Instead, we focus on the diffusion of change as the spread of change-related information, knowledge, and skill across organizational members. Toward this end, we investigate the role of training in affecting the capacity of trainees to communicate change-related knowledge to others, thereby creating a second-order effect of developing organization members who themselves did not participate in training.

Our diffusion construct is operationalized as in social network research as degree network centrality. This operationalization refers to the number of others with whom an individual is directly connected (Freeman, 1978). In-degree centrality can be assessed as the number of links or ties in which the focal person is the object of the connection. Out-degree centrality can be measured as the number of links in which the focal person is the subject of the connection (Burkhardt & Brass, 1990; Freeman, 1978). Since degree centrality focuses on the existence (or nonexistence) of a tie between two network members, the source of a tie (e.g., who initiates it) is irrelevant. If communication (e.g., diffusion of information) occurs among network members, its activities will be reflected through degree centrality (Burkhardt & Brass, 1990). Among various social networks in the workplace (e.g., friendship, advice, trust, or hindrance networks), the advice network reflects job-related information exchanges among individuals (Cross & Parker, 2004; Ibarra & Andrews, 1993). Employees centrally located in an advice network tend to communicate with a large number of people (Freeman, 1978; Ibarra, 1993). In the context of our study on technological change-related advice networks, high in-degree centrality indicates that the focal person provides advice (i.e., change-related knowledge and skills) to many others. Similarly, high out-degree centrality implies that the focal person seeks advice from many people. This study uses in- and out-degree centrality to operationalize change-related advice networks.

An individual’s centrality can be influenced by many factors including personality and demographics (Balkundi, Kilduff, Barsness, & Michael, 2007), proximity (Borgatti & Cross, 2003), and rank and position (Lincoln & Miller, 1979). A small body of studies has examined the effect of knowledge on centrality (Burkhardt & Brass, 1990; Keith, Demirkan, & Goul, 2010). Burkhardt and Brass (1990) found that early adopters of the new technology increase their centrality to a greater degree than later adopters. More recently, Keith et al. (2010) found that the knowledge individuals have regarding new technology influences their relationship with others by leading them to become more central in the advice network. In our context, change-related training is expected to provide a valuable resource to both trainees and their peers, forming a basis for network ties between them (Emerson, 1976). In doing so, trainees are likely to locate themselves more centrally in the change-related advice network as they provide advice regarding change implementation.

Although an individual’s connections within the change-related advice network can occur in both directions (i.e., in-degree and out-degree), in the context of diffusion of change via training, we focus on the number of people who contact the focal person for change-related advice (i.e., in-degree centrality). In our context, in-degree centrality indicates the extent an individual provides change-related advice to his or her peers. On the other hand, out-degree centrality implies the extent an individual seeks advice from others. Since the knowledge and information obtained from the training tend to flow from trainees to others, our focus is on the link between training and advice giving (in-degree centrality). We hypothesize the following:

Hypothesis 2: Participation in change-related training is expected to be positively related to change-related advice giving.

Employee Proactivity and the Diffusion of Change

Employee proactivity refers to any anticipatory action that employees take to bring about change within themselves (e.g., learning new skills to cope with work demands) and/or to improve their work environment (e.g., introducing new work methods) (Crant, 2000; Parker et al., 2010). It can improve individual and organizational performance as well as learning outcomes by anticipating future problems and engaging in problem solving (Bindl & Parker, 2011). Proactivity is positively related to task mastery (Morrison, 1993), job performance (Grant, Parker, & Collins, 2009; Griffin, Neal, & Parker, 2007), and team-level learning (Druskat & Kayes, 2000). As such, when organizational change requires learning, proactivity may facilitate mastery of relevant knowledge and skills. Since proactive individuals tend to challenge the status quo and alter their work environment to make change happen (Parker et al., 2010), they may not merely comply with the change but also step up to promote it among other members.

Little research exists on the role of proactivity in diffusing organizational change. Organizational change entails both implementation (e.g., adopting new work methods or procedures) and diffusion (e.g., disseminating new work methods or procedures to others). Proactive people are shown to actively implement change and can become change agents (Hornung & Rousseau, 2007). However, little research addresses how proactive individuals might diffuse the change to others, particularly, through change-related advice giving and seeking activities. Extant studies exploring the link between proactivity and networks focus on the direction of the effect (e.g., whether proactivity changes network position or vice versa, Lee, Qureshi, Konrad, & Bhardwaj, 2014). Left unstudied is whether proactive employees differ from other employees with respect to their social network behavior during the change. This activity can take the form of either seeking more information regarding the change or giving change-related advice to colleagues. A small but growing body of research examines the relationship between individual proactivity and workplace social networks (Lee et al., 2014; Wichmann, Carter, & Kaufmann, 2015). A social network approach provides an opportunity to study the interpersonal activities individuals undertake in the context of change. For instance, employees actively involved in the change process may show more network connections (i.e., degree centrality) indicative of active communication (Freeman, 1978).

Proactive engagement in the change process may include activities such as disseminating new knowledge and skills and seeking further information regarding the implementation of a new work procedure. Thus, proactive employees may both provide and seek change-related advice (e.g., knowledge, information, skills). Proactive employees may provide advice to peers to encourage them to actively participate in the change process. Similarly, proactive employees may seek advice from others because they need more information and knowledge in order to adopt a new work procedure (Petrou, Demerouti, & Schaufeli, 2018). Thus, in the context of proactivity, we hypothesize effects on both advice giving and seeking:

Hypothesis 3: Employee proactivity is positively related to change-related advice giving (a) and advice seeking (b).

Now we turn to the conditions that may facilitate trainee participation in the diffusion of change-related knowledge. Certain factors are known to enhance knowledge sharing and transfer between trainees and their peers, including organizational incentives (Hu & Randel, 2014), supportive climate (Grossman & Salas, 2011), and individual motivation and personality (Blume et al., 2010). As an important individual difference in the context of change, proactivity may play a significant role in facilitating the relationship between training and the diffusion of learning.

Diffusion of new skills and knowledge following change-related training requires individuals to invest resources (e.g., time, effort) into the diffusion process. Since sharing knowledge and skills can be effortful, individual trainees must be motivated to participate in diffusion. We expect that proactive employees are more motivated than less proactive ones to provide change-related advice in order to spread change-related knowledge and skills among others. As such, employee proactivity may facilitate the positive relationship between change training and change-related advice giving. We hypothesize the following:

Hypothesis 4: Employee proactivity moderates the expected positive relationship between training and advice giving, such that when employee proactivity is higher, the positive relationship between the training and advice giving is more strongly positive.

Figure 1 summarizes our hypotheses.

Figure 1.

Hypothetical framework.

Method

Research Context

The study’s field site is a large U.S. health care provider. It devoted a multimillion-dollar budget to implementing an organization-wide database management system intended to integrate clinical, financial, administrative, and operational data across its hospitals, health plan, and related operations. The stated goal of this change was to drive better patient outcomes through decisions informed by cross-functional organizational data. During the initial planning stages, the organization commissioned a needs analysis of the skills, competencies, and infrastructure required for it to successfully implement the new system. That analysis informed the development of a training program intended to foster appropriate skills for the development and use of the new system. The result was a 36-week training program conducted at a university by its faculty for a subset of employees (N = 77, referred to here as “trainees”), identified by management as potential change agents. Note that the program did not explicitly provide training with regard to the roles trainees should play in the change. Instead, its focus was on the technical skills and knowledge trainees would need to contribute to the design and use of the new database management system. Thus, the program focused on technology-oriented training; change diffusion was neither its focus nor a formal topic. We note that the trainees represented an array of functional areas where change leaders saw cross-functional opportunities to design and develop new forms of data sharing and analysis. Trainees were expected to contribute to the design of the new system as well as to make use of it in their own work. Having trainees help their colleagues learn to use the new system was a hoped-for spillover of the broader training program but change diffusion per se was not a topic the training covered. Trainees typically were managers and other professionals from diverse functions including finance, health-care services, research, insurance, and strategic planning whose work involved considerable analysis and interpretation of data. On program completion, trainees were expected to help develop, design, and use the new database management system and help their colleagues to do so too in order to spread the change organization-wide.

Data Collection

We began with in-depth interviews with 11 members of the executive board and key personnel to better understand the nature of the change and its planned implementation. Based on these interviews, we developed a survey to administer to trainees and their peers. Trainees were asked to nominate a list of peers reporting to the same manager and performing work related to data analytics. A total of 203 peers were identified. Data were collected for the program’s first five cohorts (between 12 and 18 members each) and their peers (Table 1). We collected data from both trainees and peers not in the training program at two time points: a pretest at the start of training and a posttest at the conclusion of Cohort 5. Because there were multiple cohorts, the chronological date for the pretest varied by cohort. Thus, we controlled for cohort in our regressions. In total, we invited 77 trainees and 203 peers to participate in the study. Among these trainees, 98.7% (N = 76) completed the pretest during their first week in the program. Among the peers identified, 42.4% (N = 86) participated at pretest. Overall, 162 employees participated in the study at Time 1. Twenty-four employees subsequently left the organization before the study was completed. Of the 138 employees remaining at the end of the study, 93 (46 trainees, 47 peers) participated in the posttest, constituting the final data set used in our analyses (response rate 33.2%). Average respondent age was 38.7 years. Women were 55% of the sample. As for education, 45.2% held a bachelor’s degree, 39.8% a master’s, and 15% a doctoral-level degree.

Table 1.

Response Rate.

			Trainees	Peers	Total
Pretest	Invitation		77	203	280
	Response		76	86	162
Attrition during training	Attrition		8	16	24
Posttest	Invitation		68	70	138
	Response	Total	46	47	93
		First stage	20	27	47
		Second stage	26	20	46
Overall response rate			59.7%	23.2%	33.2%

Table 2 presents the timing of our measures. At pretest, we measured two independent variables (i.e., individual proactivity, training) and a training-related outcome variable (i.e., self-evaluated level of expertise regarding the new work system) as a baseline measure. We also measured demographic and control variables. At posttest, we measured four variables: proactivity, advice network centrality, and two training-related outcomes (i.e., use of new work systems and self-evaluated level of expertise regarding the new work system). In our first effort to gather posttest data, we collected data on these four variables from 47 respondents (20 trainees and 27 peers). In an effort to increase our sample size, we subsequently shortened the assessment to include only proactivity and advice network centrality, yielding an additional 46 responses (26 trainees and 20 peers), for a total of 46 and 47, respectively. No differences were found between the two stages at Time 2: gender, t(91) = −0.32, ns; education, t(91) = 0.86, ns; age, t(91) = 1.31, ns; proactivity at pretest, t(91) = −0.13, ns; and posttest, t(91) = −1.81, ns; advice giving, t(91) = 1.81, ns; and seeking, t(91) = 0.35, ns. Data from the two posttest stages were combined.

Table 2.

Variables and Data Collection.

Variables		Pretest (N = 93)		Posttest (N = 93)
		Trainees at Time 1 (N = 46)	Peers at Time 1 (N = 47)	Trainees (N = 46)		Peers (N = 47)
		Trainees (N = 46)	Peers (N = 47)	Time 2 (N = 20)	Time 2′ (N = 26)	Time 2 (N = 27)	Time 2′ (N = 20)
Independent variables	Participation in training	⚈	⚈
	Proactivity	⚈	⚈	⚈	⚈	⚈	⚈
Dependent variables	Advice network centrality			⚈	⚈	⚈	⚈
	Change in use of new work systems			⚈		⚈
	Expertise in relevant knowledge and skills	⚈	⚈	⚈		⚈
Control variables	Gender, age, education, functional area)	⚈	⚈

Note. ⚈Indicates data collected during the corresponding periods.

Measurement

Participation in the Training Program

Roughly half of the respondents (49.4%) participated in the training program. The total length of the program was 36 weeks and consisted of 6-week courses including statistical analysis, data mining, and evidence-based management. Employees participating in the program constitute trainees, while their peers are referred to as nontrainees.

Employee Proactivity

We assessed both pre-and posttest proactivity using three items adapted from Morrison and Phelps’s (1999) taking charge scale, which captures an individual’s discretionary behavior intended to influence functional change in an organization. Participants responded on a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree). Items were slightly reworded to reflect employee involvement in new work methods in his or her work unit (e.g., department, team). A sample item is “I often try to institute new work methods that are more effective for my work unit.” Cronbach’s α was .78.

Change-Related Advice Giving and Seeking

At posttest, two items adapted from Ibarra (1993) measured in-degree centrality (for advice giving) and out-degree centrality (for advice seeking). In-degree centrality refers to the extent an individual gives data analytics–related advice to others. Out-degree centrality is the extent an individual seeks data analytics–related advice from others. Each asked the respondent whether he or she goes to a specified another person for data analytics–related advice (or vice versa). Respondents checked names from the list of all study respondents including both other trainees and their peers. To measure network centrality, a network’s boundary must be defined (Ibarra, 1993; Wasserman & Faust, 1994). Since all study participants performed data analytics–related work, they were the first in the organization whose job tasks and roles were directly affected by the launch of the change. Thus, communications regarding the implementation of the new database system are likely to occur primarily among them. Typically a single sociometric question is used to measure network activity since a network index is reliable “when measures are taken to facilitate individuals’ capacity to recall and report their network links accurately” (Ibarra, 1993, p. 481; Marsden, 1990). We constructed each network question to be specific: Respondents indicated whom they go to or who comes to them “for data analytics–related advice.” We used the statnet package in R to compute normalized centrality scores.

Training-Related Outcomes

We assessed two change-related outcomes relevant to training participation: change in use of new work systems (posttest), and expertise in relevant knowledge and skills (pre-and posttest). At the posttest, we assessed change in use of new work systems by asking respondents to evaluate the change in their own use of new work systems during the past 12 months from −5 = decreased by 80%-100%, to 0 = no change, to +5 = increased by 80%-100% using the question “In the last year, has your use of advanced data analytics in your job changed (for example, decision modeling, data mining, predictive modeling, and visual models such as graphs and plots)?” About half of the sample (N = 47; 20 trainees and 27 peers) responded to these questions (Table 2). The average for use of new work systems is 1.02 (SD = 1.11), which differs significantly from zero—t(45) = 6.27; p < .01—indicating an average 20% increase for the overall sample. The level of change-related knowledge and skill is operationalized here in terms of expertise in relevant knowledge and skills. Respondents were asked to evaluate their own expertise level from 1 = novice (“Know little about advanced data analytics practices and concepts”) to 5 = expert (“Deep understanding of relevant practices and concepts of advanced data analytics; able to apply to novel situations, and to adjust application to exceptional circumstances”). A paired comparison of 47 respondents who completed both pre- and posttest surveys shows that the average posttest expertise level (M = 2.93, SD = 0.88) is higher than the pretraining average (M = 2.35, SD = 1.10); t(45) = 3.97; p < .01.

Control Variables

We controlled for demographic variables including age, gender, and education, along with pretest data analytics knowledge and expertise, function (i.e., finance, provider, research, health plan, and strategic planning) and training cohort (i.e., Cohort 1-5), the latter two because individuals are more likely to build advice network ties when they work in the same organizational function or train together (Borgatti & Cross, 2003).

Preliminary Analyses for Comparisons of Trainees and Nontrainees

Demographics Comparisons

We compared trainees (N = 46) with nontrainees (N = 47) at pretest and found no differences in gender and education, and or proactivity (Table 3). Trainees (M = 3.11, SD = 1.08) were slightly older than nontrainees (M = 2.64, SD = 1.13); t(91) = 2.05, p < .05. Expertise in relevant skills or knowledge at pretest was higher for trainees (M = 2.49, SD = 0.97) than nontrainees (M = 2.02, SD = 1.19); t(90) = 2.06, p < .05.

Table 3.

Group Differences and Changes Over Time.

	Pretest			Posttest			Changes over time
	Trainees	Peers	Difference	Trainees	Peers	Difference	Trainees	Peers
Proactivity	5.75	5.43	0.32	6.07	5.59	0.48**	0.32**	0.16
Advice giving	—	—	—	6.37	3.32	3.05**
Advice seeking	—	—	—	6.28	4.40	1.88
Use of new systems	—	—	—	1.47	0.70	0.77*
Expertise	2.49 (2.53)	2.02 (2.22)	0.47* (.30)	3.16	2.78	0.37	0.63**	0.55*
Gender	0.50	0.60	−0.10	—	—	—
Age	3.11	2.64	0.47*	—	—	—
Education	2.37	2.06	0.31	—	—	—

Note. N = 93 (46 trainees and 47 peers). A paired sample (N = 47; 20 trainees and 27 peers) is used in calculating expertise level and the use of new work system at posttest. Parentheses indicate means and difference using a paired sample (N = 47; 20 trainees and 27 peers).

p < .05. **p < .01. ***p < .001.

Training-Related Outcome Comparisons

We compared the differences in training outcomes between trainees and nontrainees at posttest. Results indicate differences in most training-related outcome variables, proactivity and advice network activity. First, trainees were higher (M = 1.47; SD = 1.26) than nontrainees (M = 0.70; SD = 0.87) in Time 2 use of new work systems, indicating an increased use of approximately 30% in the past 12 months, while nontrainees reported an approximately 15%, a significant difference, t(44) = 2.45, p < .05. A paired comparison showed that expertise in relevant knowledge and skills increased between pre- and posttests for both trainees and nontrainees: from 2.53 (SD = 0.90) to 3.16 (SD = 0.69) for trainees, t(18) = 3.31, p < .01, and from 2.22 (SD = 1.22) to 2.78 (SD = 0.97), t(26) = 2.58, p < .05, for nontrainees, with no difference between the two at posttest. Thus, while trainees reported themselves as increasing their active use of new work systems at a higher level than nontrainees, both groups perceived a gain in their change-related expertise over time.

Proactivity Change Comparison

Trainee proactivity increased from 5.75 (SD = 0.73) at pretest to 6.07 (SD = 0.60) at posttest, t(45) = 3.27, p < .01, while no change occurred for nontrainees, t(46) = 1.14, ns. Proactivity of trainees (M = 6.07, SD = 0.60) and nontrainees (M = 5.59, SD = 1.04) at posttest differed significantly, t(91) = 2.75, p < .01. This suggests that trainees are likely to have taken ownership of their role as change agents.

Advice Network Comparison

Posttest advice giving among trainees (M = 6.37, SD = 6.96) is higher than nontrainees (M = 3.32; SD = 4.20); t(91) = 2.56, p < .01. As expected, no difference exists for advice seeking between trainees (M = 6.28, SD = 7.01) and nontrainees (M = 4.40, SD = 3.41); t(91) = 1.65, ns. Since an increase in proactivity among trainees might affect our hypothesis testing, we conducted a series of preliminary regression analyses to test the effects of changes in proactivity. A significant regression weight for training on posttest proactivity controlling for pretest proactivity ( $β$ = 0.29, p < .05) suggests that training enhanced proactivity. Regression of the change in proactivity over time on advice giving ( $β$ = −0.94, ns) and seeking ( $β$ =−0.33, ns) indicated no effect of proactivity change on advice network activity. Pretest proactivity is thus used to test the hypotheses.

Hypothesis Testing

t-Tests are used to test Hypotheses 1a and 1b regarding the effect of training on change-related expertise and use of new work systems. We test Hypotheses 2 to 4 regarding individual proactivity and training and their effects on advice network centrality via a series of generalized linear model analyses because the outcome (i.e., degree centrality) is a nonnegative count variable with skew to the right (Cameron & Trivedi, 2013). First, we entered the control variables predicting degree centrality. Second, we entered change-related job training and individual proactivity as predictors. Finally, we added the interaction term between training and proactivity (Baron & Kenny, 1986).

Results

Table 4 shows descriptive statistics and correlations for study variables. Hypothesis 1 predicted that participation in the change-related training program would be positively associated with use of new work systems and expertise in change-related knowledge and skills. As shown in Table 4, there was significantly greater use of new work systems reported by trainees, t(44) = 2.45, p < .05, supporting Hypothesis 1a. However, there was no difference in self-reported expertise between trainees and nontrainees, t(45) = 1.47, ns, at odds with Hypothesis 1b. We note that between pretest and posttest both trainees and their peers reported increases in their levels of change-related expertise, t(18) = 3.31, p < .01 and t(26) = 2.58, p < .05, respectively, possibly a by-product of the change roll out and diffusion. Thus, Hypothesis 1 is only partially supported.

Table 4.

Descriptive Statistics and Correlations.

	N	M	SD	1	2	3	4	5	6	7	8	9	10
1. Gender	93	.55	.50
2. Age	93	2.87	1.13	−.03
3. Education	93	2.22	1.18	−.02	.53***
4. Expertise (Pretest)	92	2.25	1.11	−.27***	.07	−.06
5. Training	93	.49	.50	−.10	.21**	.13	−.04
6. Proactivity (Pretest)	93	5.59	.96	−.11	.24**	.29***	.05	.16
7. Proactivity (Posttest)	93	5.83	.88	−.08	.15	.11	−.01	.28***	.61***
8. Use of new work systems (Posttest)	46	1.02	1.11	.02	.06	−.02	−.03	.35**	−.09	.01
9. Expertise (Posttest)	47	2.94	.87	−.22	−.02	.04	.31**	.21	.02	.15	.25*
10. Advice giving	93	4.83	5.91	−.21**	−.13	−.13	.05	.26**	.14	.13	.06	.30**
11. Advice seeking	93	5.33	5.54	.05	−.20*	−.18*	.05	.17	.04	.12	.00	.19	.65***

Note. N = 93. Gender (1 = female; 0 = male), Age (1 = <25 years old; 2 = 25-34; 3 = 35-44; 4 = 45-54; 5 = 55-64; 6 = >64), Education (1 = college; 2 = some graduate work; 3 = Master; 4 = PhD), Expertise (1 = novice to 5 = expertise), Training (1 = trainees; 0 = peers).

p < .05. **p < .01. ***p < .001.

To test Hypotheses 2 through 4, we used generalized linear regression (Table 5). Hypothesis 2 predicted that employees who participated in change-related training are likely to provide change-related advice to their coworkers. The direct effect of training on advice giving was significant ( $β$ = 0.54, p < .01), supporting the hypothesis. Although we did not posit a formal hypothesis, our analysis showed that the effect of training on posttraining advice seeking was also significant ( $β$ = 0.37, p < .01). Hypotheses 3a and 3b predicted that proactive employees are likely to provide and seek change-related advice. The direct effect of employee proactivity was significant for advice giving ( $β$ = 0.18, p < .01), providing support for the Hypothesis 3a. However, the effect of proactivity for advice seeking in Model 5 was only marginally significant ( $β$ = 0.10, p = .06), offering limited support for Hypothesis 3b. Hypothesis 4 predicted that proactivity positively moderates the effect of training on advice giving. Consistent with the hypothesis, we found a positive effect for training on advice giving for those high on proactivity (β = 0.27, p < .01; Figure 2). This moderating effect exceeded the direct effects of training and proactivity ( $Δ χ^{2}$ = 7.50, p < .05; Figure 3). No interaction was found for training and proactivity on advice seeking ( $β$ = 0.03, ns), and none was hypothesized. Figure 2 displays our findings and Figure 3 the moderating effect of proactivity.

Table 5.

Generalized Linear Regression Analysis Predicting Advice Network Centrality.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Constant	1.61*** (.28)	.56 (.44)	1.25* (.53)	1.70*** (.25)	1.12** (.38)	1.20** (.45)
Gender	−.47*** (.11)	−.36*** (.11)	−.38*** (.11)	.14 (.10)	.20* (.10)	.20 (.10)
Education	−.04 (.05)	−.06 (.05)	−.05 (.05)	−.05 (.05)	−.08 (.05)	−.08 (.05)
Age	−.11* (.05)	−.18*** (.05)	−.18** (.06)	−.13** (.05)	−.17*** (.05)	−.17*** (.05)
Function (Finance)	.12 (.20)	.10 (.19)	.06 (.20)	−.01 (.17)	.01 (.16)	−.00 (.17)
Function (Health Plan)	.23 (.14)	.29* (.14)	.26 (.14)	−.04 (.12)	.02 (.12)	.01 (.13)
Function (Strategy)	−.70 (.50)	−.78 (.51)	−1.01 (.52)	−.69 (.38)	−.67 (.38)	−.70 (.39)
Cohort 2	.03 (.23)	−.11 (.23)	−.13 (.23)	.19 (.21)	.13 (.21)	.12 (.21)
Cohort 3	−.93*** (.26)	−.89*** (.25)	−.87*** (.25)	−.00 (.21)	.01 (.21)	.01 (.21)
Cohort 4	−.33 (.23)	−.37 (.23)	−.36 (.23)	−.40 (.22)	−.41 (.22)	−.41 (.22)
Cohort 5	−.11* (.16)	−.11 (.17)	−.11 (.17)	.14 (.16)	.13 (.16)	.13 (.16)
Expertise at pretest	.28*** (.05)	.25*** (.05)	.26*** (.05)	.17*** (.04)	.14** (.05)	.14** (.05)
Training		.54*** (.11)	−1.02 (.73)		.37*** (.10)	.18 (.62)
Proactivity		.18** (.07)	.06 (.09)		.10 (.06)	.09 (.07)
Training × Proactivity			.27* (.13)			.03 (.11)
AIC	638.1	602.2	599.4	624.4	608.0	609.9
χ²	131.7	162.7	170.2	69.17	86.24	86.53

Note. Standard errors in parentheses. AIC = Akaike information criterion.

p < .05. **p < .01. ***p < .001.

Figure 2.

Hypothesis test.

Figure 3.

Interaction effect of individual proactivity on advice giving and advice seeking.

We probed the significant interaction effect using ±1SD around the mean of the moderator (Preacher, Curran, & Bauer, 2006). For Hypothesis 4, simple slope analyses indicated that the slope at −1SD (estimate = .24, t = 1.33, ns) was insignificant, whereas the slope at +1SD (estimate = .76, t = 5.15, p < .01) was significant, indicating that the effect of training on advice giving was stronger for employees with higher scores of proactivity. The region of significance on the moderator ranged from −18.29 to 4.88, indicating that any simple slope outside this range is significant. Given that the proactivity scores (M = 5.59, SD = 0.96) ranged from 2.66 to 7, the effect of training on advice giving is significant and positive for higher (>4.88) proactivity scores. Thus, Hypothesis 4 was fully supported.

Discussion

This study makes several important contributions. First, our findings demonstrate that change-related training not only can increase the use of new work methods but can also motivate trainees to communicate change-related information to other organization members. Trainees do so by participating in change-related advice networks, in which they provide advice to others as well as seek additional information from them pertaining to the change. Our findings, particularly in the context of technological change, provide an important addition to our understanding of training’s role in organizational change (Baldwin & Ford, 1988; Kozlowski & Salas, 2010). We find that, in addition to its potential effects on learning (Arthur et al., 2003) and transfer (Blume et al., 2010), training also can have a diffusion effect, by promoting technological change–related advice to others in a social network. These results are in line with research linking individual knowledge and advice network structure (Keith et al., 2010). Our findings contribute to both the organizational change literature and social network theory, demonstrating how knowledge obtained from training can influence advice network activity.

Second, our findings suggest that employee proactivity plays a significant role in the diffusion of change. Our findings demonstrate a direct relationship between proactivity and change-related advice giving, suggesting a way in which proactive employees can serve as change agents (Rogers, 1995). Despite recognition of the role of proactivity in change agency (Dutton, Ashford, O’Neill, & Lawrence, 2001), little has been known about how proactive people contribute to the change. Our findings illustrate that proactive employees can contribute to the organizational change by providing change-related advice to others to a greater degree than nonproactive peers.

Third, our results demonstrate that proactive trainees are more likely to share change-related knowledge and skills than less proactive ones. These trainees are likely to be serving as change agents helping to diffuse the change in the organization. Proactive individuals are thus not only more likely to give change-related advice to peers but they may also enhance their advice giving following participation in change-related training.

Practical Implications

Existing studies on training have focused on either immediate learning outcomes or the transfer of learning to the trainees’ own work practice. However, we show that training can also facilitate the sharing of learning with others. We advise organizations to carefully design training activities to better help trainees actively share what they have learned. This can take the form of providing trainees with frameworks, decision trees, and checklists that make practices easier to share in order to promote dissemination of newly acquired information. At the same time, giving trainees opportunities to become familiar with each other and build network ties during training can make it easier for them to follow up with others subsequently in supporting change implementation and diffusion.

Another practical implication of this study is the importance of selecting appropriately motivated trainees. To facilitate technological organizational change, we recommend carefully selecting trainees who have demonstrated proactivity. Indeed, change leaders may find that proactivity is a more visible attribute on which to select them than other potentially useful criteria such as network ties. Selecting employees who tend to step up to solve problems and who are known to share and seek out new information could improve the efficacy of change-related training particularly in promoting diffusion. Last, we advise that training be designed in a way that creates and supports the expectation that participants will share their change-related knowledge and skills with others as the change is implemented.

Limitations

This study has some limitations we have worked to mitigate. First, causality cannot be strongly attributed to our findings. Our comparison group is not randomly assigned but represents a comparable sample based on nominations made by trainees using criteria we specified (e.g., similar task, same department). Moreover, we do not have a pretest measure of social network and thus cannot rule out that trainees had different social networks than their peers, a caveat that does not affect the finding of an interaction between training and proactivity. Also, as the change was new at the outset of the training program, it is likely that change-related advice networks formed after the start of training. Also, several of our variables were measured at two points in time offering some evidence of the degree of training-based change. We used these design features to answer the call Barends et al. (2014) have made for more controlled studies of organizational change. Second, while we note that network-based variables tend to have nonsignificant correlations with other study measures, such a pattern is consistent with findings in sparse networks as is the case in our sample (network density at posttraining = .05). Importantly, our network measures are not subject to common methods bias. Our network centrality measure is not a typical Likert-type scale, which helps eliminate common methods bias. Future research would nonetheless benefit from using multiple data sources such as a supervisor-rated measure of proactivity. Third, we note that the pretest data collection was conducted at different times across the five cohorts. However, we collected the data from both trainees and peers at the same manner (i.e., at the beginning of the training program and on completion of Cohort 5) so that the effect of training can be more clearly demonstrated. Fourth, our sample is not large, however, we conducted a power analysis for multiple regression (Cohen, Cohen, West, & Aiken, 1983) using the Stata 15 program. To achieve a power level of .80 for detecting a moderate effect (i.e., β of 0.5 or 0.6), a sample size of 86 (for β = 0.6) to 126 (for β = 0.5) would be required (at p < .05). Additional analyses showed that a minimum sample size for obtaining an R² of .2 with 10 covariates is 75 at a p value of .05. Our sample of 93 respondents thus appears adequate for our hypothesis testing. A final limitation of our study is that it does not account for the effects of environmental factors such as psychological safety, organizational politics, or leader support. Although our study was conducted during a change where trainees were able to apply the training provided, we have limited information about the environmental factors that might have contributed to change diffusion.

Conclusion

Our study highlights the role of both training and proactivity as important factors in the diffusion of change. Both can contribute to the diffusion of change-related knowledge and skills by motivating and enabling the giving of change-related advice to others in the organization. Such findings may be particularly important in the context of technological change where training focuses on supporting the use of new forms of information and the implementation of new work practices.

Footnotes

Appendix

A Curriculum of Training Program

Goal	To instill a culture and working environment across the organization that embraces evidenced-based, analytical methods for improving E-healthcare delivery
Design	A total of 36-week program (six courses; a 6-week curriculum per each course)
Courses	• Evidence-based management
	• Empirical methods
	• Forecasting and optimization
	• Decision making under uncertainty
	• Data management
	• Data mining
Objectives	At the end of the training program, trainees should be able to . . .
	• create robust data architectures
	• utilize the basics of empirical methods
	• apply data mining tools and methodologies
	• utilize basics of data aggregation and warehousing
	• apply what they have learned to practical business scenarios

Acknowledgements

We acknowledge the support of an H. J. Heinz II University Professorship and the Center for the Future of Work of Heinz College at Carnegie Mellon University for this research and thank David Krackhardt, Claire Gubbins, and Veronica Lin for their helpful feedback.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Byeong Jo Kim

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