+1 (208) 254-6996 [email protected]
  

Annotation is a crucial component of good data visualization.  It can turn a boring graphic into an interesting and insightful way to convey information.  This week, please navigate to any site and find a graphic that could use some annotation work.  Add the graphic and the website it is found as an attachment to this post and note what you would do to enhance the graphic and note why you would make these decisions.

Word Count: 300 APA7

Don't use plagiarized sources. Get Your Custom Essay on
Analyzing And Visualizing Data
Just from $13/Page
Order Essay


At least one scholarly article reference

Social Behavior and Personality , Volume 47, Issue 7, e8124

https://doi.org/10.2224/sbp.8124

www.sbp-journal.com

Why are we having this innovation? Employee attributions of innovation and implementation behavior

Se Yeon Choi

1

, Goo Hyeok Chung

2

, Jin Nam Choi

1

1

College of Business Administration, Seoul National University, Republic of Korea

2

College of Business Administration, Kwangwoon University, Republic of Korea

How to cite: Choi, S. Y., Chung, G. H., & Choi, J. N. (2019). Why are we having this innovation? Employee attributions of innovation

and implementation behavior. Social Behavior and Personality: An international journal, 47(7), e8124

We used attribution theory to explain employee behavior toward

innovation implementation. We focused on employee innovation

attributions to organizational intentionality as employees’ sensemaking

of why their organization has adopted an innovation. We identified two

types of employee attributions: to constructive intentionality and to

deceptive intentionality. We collected data from 397 employees and 84

managers of Chinese and Korean organizations. Results showed that

employee attribution to constructive intentionality enhanced

innovation effectiveness by increasing active implementation and

decreasing implementation avoidance. By contrast, employee

attribution to deceptive intentionality diminished innovation

effectiveness by increasing implementation avoidance. These findings

enrich the innovation implementation literature by introducing the

attribution-based perspective of sensemaking.

Keywords innovation attributions;

attribution to constructive

intentionality; attribution

to deceptive

intentionality; active

implementation; passive

implementation;

implementation

avoidance; innovation

effectiveness

Innovation has been identified as the key to the survival and growth of firms in a rapidly changing and

competitive business environment (Greenhalgh et al., 2005). In the past, researchers paid close attention to

organizational innovation adoption, because they considered implementation to be a relatively automatic

and static process (Choi & Chang, 2009). However, as researchers have recently realized that innovation

success depends not only on the adoption of innovation, but also on employees’ consistent use of the

innovation, they have shifted their attention to implementation (Birken et al., 2015; Chung & Choi, 2018).

As the role of employees in shaping implementation processes and outcomes is critical, the way in which

they perceive and react to innovation needs to be understood.

Various theoretical models have been used to explain employee perceptions and behavior toward

innovation. For example, the technology acceptance model suggests that individual cognitive evaluations,

such as perceived usefulness and ease of use, are positively related to innovation use (F. D. Davis, 1989).

Similarly, the theory of planned behavior identifies perceived behavioral control as a critical determinant of

intention and behavior in relation to innovation (Ajzen, 1991). Researchers have drawn on coping theory to

propose that innovation use depends on the cognitive appraisal of innovations as a threat or an opportunity

(Beaudry & Pinsonneault, 2005). The focus in these theoretical accounts has mostly been on employee

expectations of the cost and benefit of an innovation, with these expectations affecting subsequent

implementation behavior.

Whereas previous researchers have focused on expectations of future utility functions of innovation use, we

have examined innovation implementation by highlighting the role of attribution. Expectation refers to

CORRESPONDENCE Jin Nam Choi, College of Business Administration, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul

08826, Republic of Korea. Email: [email protected]

© 2019 Scientific Journal Publishers Limited. All Rights Reserved.mailto:[email protected]

Choi, Chung, Choi

future consequences or the prediction of the result of an event, whereas attribution is related to the

perceived cause of an outcome or the interpretation of the result of an event (Seifert, 2004). As a

fundamental cognitive process, attributions are considered a core mechanism of sensemaking, influencing

emotional, attitudinal, and behavioral reactions as well as expectations (Fiske & Taylor, 2013; Martinko &

Gardner, 1982; Weiner, 1985). In this study, we proposed that attributions have incremental value in

explaining employee implementation behavior over and above expectations. We drew on the attribution of

intentionality model (Ferris, Bhawuk, Fedor, & Judge, 1995) and identified two types of employee

attributions of an organization’s perceived intentionality in innovation adoption, that is, attributions to

constructive and deceptive intentionality. We proposed that these attributions would engender distinct

behavioral reactions to an innovation.

Although employees confronting innovation tend to exhibit different behaviors (Greenhalgh et al., 2005),

previous researchers investigating behavioral reactions to innovation have examined only a single behavior

of either innovation acceptance and use, or resistance to innovation (Choi & Moon, 2013). As employees

may exhibit behavior beyond using or rejecting an innovation (Chung & Choi, 2018; Greenhalgh et al.,

2005), in our examination of the role of attributions of an innovation, we used three forms of

implementation behavior based on engagement level. These may offer a more realistic picture of innovation

implementation in organizations. We identified active implementation, passive implementation, and

implementation avoidance as employee behavior with high, medium, and low engagement with an

innovation, respectively. We proposed that these implementation patterns would affect the ultimate

outcome of innovation effectiveness, which refers to each employee’s performance gain or achievement of

desired outcomes, such as skill acquisition and improved productivity through innovation (Klein, Conn, &

Sorra, 2001).

Literature Review and Hypothesis Development Innovation is defined as “an idea, practice, or object that is perceived as new by an individual or other unit

of adoption” (Rogers, 2003, p. 12). Once an innovation is adopted by an organization, employees confront

challenges, and are under pressure to change work routines, update skills, and adapt to different work styles

and task roles. These equivocal circumstances trigger sensemaking (C. G. Davis, Nolen-Hoeksema, &

Larson, 1998; Weick, Sutcliffe, & Obstfeld, 2005). Employees attempt to label and assign meaning to these

situations by interpreting the cause of the innovation (Maitlis & Christianson, 2014; Park, 2010). As a core

driver of sensemaking, attributions of intentionality underlying the adoption of an innovation play a crucial

role in labeling the situation and determining subsequent behavioral reactions.

Innovation Implementation Behavior

Researchers in social psychology have demonstrated that behavior can be exhibited in various ways when

individuals confront social situations (Fiske & Taylor, 2013). Social behavior is broadly classified into

prosocial and antisocial, and prosocial behavior is specified as extrarole and role-prescribed (Dovidio,

Piliavin, Schroeder, & Penner, 2006). Work-related behavior is categorized into extrarole, in-role, and

counterproductive work behavior domains, which are relatively independent and characterized by different

antecedents and consequences (Dalal, 2005; Spector & Fox, 2010). Accordingly, we applied these three

domains to the innovation context and proposed three forms of implementation, namely, active, passive,

and avoidance, on the basis of engagement level.

Active implementation refers to employees’ spontaneous and voluntary engagement in innovation

implementation. Active implementation is a form of proactive extrarole behavior in an implementation

context, and is characterized by the self-initiated action of challenging the status quo and creating favorable

conditions for implementing the innovation (Parker, Williams, & Turner, 2006). By contrast, passive

implementation refers to employees’ compliant implementation behavior in accordance with organizational

requirements and directions. It is a form of in-role prescribed behavior in an implementation context (Klein

2© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Social Behavior and Personality: an international journal

et al., 2001). Employees engaging in passive implementation follow innovation-related instructions carefully

(Chung & Choi, 2018; Dusenbury, Brannigan, Falco, & Hansen, 2003). Finally, implementation avoidance

is the withdrawal of employees from an innovation implementation. Implementation avoidance is a passive

form of counterproductive or deviant behavior whereby the employee avoids work or intentionally reduces

attention to, or interest in, innovation (Dalal, 2005). To maintain the status quo, employees who avoid

implementation fail to conform to innovation initiatives by refusing to, or even pretending not to, recognize

such initiatives (Chung & Choi, 2018; Erwin & Garman, 2010).

Employee Attribution of Innovation to Organizational Intentionality

In social psychology, attribution theory proposes that to predict and control the environment, individuals

tend to seek the causes of an event (Gilbert, 1998). The search for causal explanations involves ascribing

meaning and labels to events or to other individuals’ actions, which affects subsequent attitudes and

behavior (Fiske & Taylor, 2013). Causal attribution thus considerably influences individuals’ sensemaking

of, and behavioral reactions to, events with or without expectations (Fiske & Taylor, 2013; Jacquart &

Antonakis, 2015; Rodell & Lynch, 2016; Weiner, 1985).

According to Ferris et al. (1995), an observer attributes an actor’s behavior to positive (authentic and

sincere) or negative (self-serving and manipulative) intentions. In an organizational context, employees

tend to attribute decisions to the organization’s intentions or motives. For example, Nishii, Lepak, and

Schneider (2008) divided employee attribution of motivation underlying human resource practices into

commitment-focused (i.e., promoting service quality and employee development) and control-focused

attributions (i.e., reducing costs and exploiting employees). These attributions affect employees’

interpretation and labeling of, and responses to, human resource practices.

In the innovation implementation context, attributions to intentionality trigger employees’ sensemaking of

the organization’s innovation adoption. Accordingly, we proposed that employees would attribute an

organization’s innovation adoption decision to either positive (i.e., constructive intentionality) or negative

intentions (i.e., deceptive intentionality). Attribution to constructive intentionality refers to employees’

reasoning that their organization has adopted an innovation with authentic and sincere intentions of

achieving desirable outcomes, such as organizational development and employee well-being. Attribution to

deceptive intentionality is defined as employees’ reasoning that their organization has adopted an

innovation with self-serving, manipulative intentions, such as catching up with a managerial fad or

increasing political power and management control to exploit employees. Although these attributions are

independent, they are not mutually exclusive. We expected them to trigger different labeling of innovation,

thereby leading to disparate implementation.

Attribution to constructive intentionality. When innovation adoption is attributed to constructive intentionality, employees tend to develop favorable attitudes toward, and behavioral engagement with, the

innovation (Ferris et al., 1995). Employees’ belief that the organization’s intentions are genuine increases

their sense of control, satisfaction, and organizational commitment, thereby promoting proactive and

extrarole behavior (Bala & Venkatesh, 2016; Dalal, 2005). Accordingly, we proposed that employees with

attributions of constructive intentionality would implement an innovation with enthusiastic commitment.

They would be unlikely to withdraw from its implementation because their positive attribution discourages

negative reactions (Byrne, Kacmar, Stoner, & Hochwarter, 2005; Nishii et al., 2008; Parker et al., 2006).

Thus, attribution of constructive intentionality stimulates employees to actively engage in implementation

by identifying and addressing implementation barriers and modifying the features and components of an

innovation to realize potential benefits for the organization and themselves.

This positive labeling of innovation adoption may engender employees’ affective commitment to innovation,

and thus urge them to exhibit passive implementation, which is faithful innovation implementation by

conforming to innovation-related directions and instructions (Parker et al., 2006). Therefore, we proposed

3© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Choi, Chung, Choi

the following hypotheses:

Hypothesis 1a: Employee attribution to constructive intentionality will be positively related to active

implementation.

Hypothesis 1b: Employee attribution to constructive intentionality will be positively related to passive

implementation.

Hypothesis 1c: Employee attribution to constructive intentionality will be negatively related to

implementation avoidance.

Attribution to deceptive intentionality. When employees attribute an innovation to deceptive intentionality, they are likely to label the situation as unfavorable and harmful and to react negatively to

implementation. As this attribution is likely to engender passive maladaptive behavior and reduced task

engagement (Martinko & Gardner, 1982), employees’ passion and responsibility to implement the

innovation is diminished, because they are unconvinced of the value and necessity of the innovation

(Stanley, Meyer, & Topolnytsky, 2005). Therefore, employees with deceptive attribution exhibit neither

active nor passive implementation. This negative labeling may render employees reluctant to implement an

innovation even under pressure to do so (Chung & Choi, 2018). By justifying the withdrawal from, or

rejection of, an innovation (Olson-Buchanan & Boswell, 2008), employees with attribution to deceptive

intentionality are likely to withdraw support and avoid involvement with the innovation as much as

possible. Therefore, we proposed the following hypotheses:

Hypothesis 2a: Employee attribution to deceptive intentionality will be negatively related to active

implementation.

Hypothesis 2b: Employee attribution to deceptive intentionality will be negatively related to passive

implementation.

Hypothesis 2c: Employee attribution to deceptive intentionality will be positively related to

implementation avoidance.

Implementation Behavior and Innovation Effectiveness

The manner in which an innovation is implemented determines its success or innovation effectiveness,

which refers to the extent to which each employee’s performance-related consequences, benefits, or

outcomes are accrued as expected from the innovation (Klein et al., 2001). Previous findings have

demonstrated a significant association between implementation behavior and innovation outcome (Choi &

Chang, 2009; Klein et al., 2001). We therefore predicted that implementation behavior would affect

innovation effectiveness in different ways.

First, as researchers have suggested a strong positive relationship between proactive behavior and

innovative performance (Baer & Frese, 2003), employees exhibiting active implementation exert extra effort

to fully use the innovation in their task roles and they optimize it in their work context. They can thus use

the innovation effectively and fully accrue its expected benefits. Second, study findings on innovation

implementation with a focus on employee compliance to implementation have revealed a positive

relationship between this behavior and innovation effectiveness (Choi & Chang, 2009; Klein et al., 2001). By

eliciting compliant effort toward implementation, passive implementation can generate the intended

positive outcomes when employees use the innovation. Third, regardless of how useful an innovation is, it

cannot achieve its potential or positive outcomes when employees avoid it and fail to use it (Real & Poole,

2005). When employees stop implementing an innovation, the expected outcome cannot be realized (Jones,

2001). Thus, implementation avoidance hinders the success of an innovation. We therefore proposed the

following hypotheses:

Hypothesis 3a: Innovation effectiveness will be positively related to active implementation.

Hypothesis 3b: Innovation effectiveness will be positively related to passive implementation.

Hypothesis 3c: Innovation effectiveness will be negatively related to implementation avoidance.

Implementation Behavior as Mediator of the Effects of Innovation Attribution on Innovation

4© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Social Behavior and Personality: an international journal

Effectiveness

We thus proposed that employee innovation attributions to different intentionalities indirectly affect

innovation effectiveness by shaping implementation behavior. Attribution to constructive intentionality may

promote active and passive implementation and reduce implementation avoidance, leading to positive and

negative innovation outcomes, respectively. We expected that employee attribution of an innovation to

deceptive intentionality may lead to deterioration of innovation effectiveness by undermining active and

passive implementation and by enabling implementation avoidance. Thus, we proposed the following

hypotheses:

Hypothesis 4a: Attribution to constructive intentionality will have an indirect positive effect on

innovation effectiveness through increased active and passive implementation and decreased

implementation avoidance.

Hypothesis 4b: Attribution to deceptive intentionality will have an indirect negative effect on innovation

effectiveness through decreased active and passive implementation and increased implementation

avoidance.

Method

Participants and Procedure

To test our model, we collected field data from China and Korea, as the organizations in these countries

frequently create and adopt innovations, and their employees are exposed to numerous innovation

implementation events that require them to make sense of such events. We contacted managers enrolled in

executive Master of Business Administration programs in two universities, one in China and one in Korea.

With the consent of these managers, we mailed the survey packets to 127 teams. We received usable data

from 84 managers and 397 employees (response rate = 66.1%), with the final sample consisting of 33 teams

from Seoul, Korea, and 51 teams from Shanghai, China.

Of the participants, 76 managers identified administrative innovations (e.g., organizational culture change)

as their target innovation, whereas eight managers named technological innovations (e.g., introduction of

new information technology) as coded by two graduate research assistants. We adopted this innovation

typology because of its prevalence and significance in the implementation context (Kim & Chung, 2017).

Team manager participants were 16 women and 68 men, with an average age of 38.8 years (SD = 6.3) and

an average tenure of 9.2 years (SD = 6.8). Eight managers held degrees from two- or three-year colleges or

high schools (9.5%), 51 had a bachelor’s degree (60.7%), and 25 had graduate degrees (29.8%). Employee

participants were 139 women and 258 men with an average age of 31.6 years (SD = 5.9) and an average

tenure of 4.9 years (SD = 4.9). Of these participants, 80 employees had obtained degrees from two- or three-

year colleges or high schools (20.2%), 280 had bachelor’s degrees (70.5%), and 37 had graduate degrees

(9.3%).

We initially asked managers to identify an innovation that had been recently adopted and was in the process

of implementation at the time of the data collection. Employees reported their attributions related to

innovation, and their supervising managers rated implementation behavior and the outcome of their

employees, that is, each employee’s performance gain or achievement of the desired outcomes, such as skill

acquisition and improved productivity through the innovation.

Measures

We assessed all variables with multi-item measures rated on a 5-point Likert scale (1 = strongly disagree

and 5 = strongly agree). All measures exhibited acceptable internal consistency reliability coefficients. We

translated all items from English to Korean and Chinese using the standard translation/back-translation

procedure (Brislin, 1986).

5© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Choi, Chung, Choi

Attribution to constructive intentionality. We adopted Nishii et al.’s (2008) measure of human resource attributions. We used a four-item index (α = .86) to assess the employees’ attribution that their organization

adopted an innovation to obtain organizationally desirable outcomes. The four items are (a) “This

innovation was adopted because it would deliver high-quality service and products to customers,” (b) “This

innovation was adopted because it would improve internal workflows and processes,” (c) “This innovation

was adopted because it would increase productivity,” and (d) “This innovation was adopted because it would

improve overall efficiency.”

Attribution to deceptive intentionality. We used human resource attribution items from Nishii et al.’s (2008) study, and constructed a four-item measure (α = .85) to assess the employees’ attribution that their

organization adopted an innovation for manipulation or exploitation. The four items are (a) “This

innovation was adopted for no reason but to show someone’s power,” (b) “This innovation was adopted just

for political reasons,” (c) “This innovation was adopted because it was a kind of fad without any substantial

benefit for my organization,” and (d) “This innovation was adopted with the goal of exploiting employees

rather than enhancing employees’ income and well-being.”

Active implementation. We measured the employees’ active implementation of an innovation by adapting items from proactive behavior and innovative behavior scales (Choi, 2007; Morrison & Phelps, 1999). We

developed a three-item index (α = .88) to measure the employees’ active implementation of an innovation.

The managers rated the three items: (a) “This employee provides suggestions to improve the process of

implementing the innovation,” (b) “This employee actively solves problems occurring during the

implementation of the innovation,” and (c) “This employee suggests ideas to enhance the quality of the

implemented innovation.”

Passive implementation. We took the in-role behavior items from Van Dyne and LePine’s (1998) study to construct a three-item measure (α = .88) for managers to assess employees’ innovation-targeted in-role

behavior. The three items are (a) “This employee fulfills his/her job responsibilities specified in the

innovation,” (b) “This employee adequately completes his/her responsibilities related to the innovation,”

and (c) “This employee meets job performance expectations related to the innovation.”

Implementation avoidance. We used three implementation ineffectiveness items (α = .84) from Klein et al.’s (2001) scale to measure employee avoidance of an innovation. The managers rated the three items: (a)

“When this employee can do a task by either using or not using the innovation, he/she usually chooses not

to use the innovation,” (b) “Even when this employee can do a task using the innovation, he/she still uses

the old system or work process most of the time,” and (c) “I think that this employee believes that the

innovation is a waste of time and money for the organization.”

Innovation effectiveness. We used three innovation effectiveness items (α = .90) from Klein et al.’s (2001) scale to assess the positive outcomes or performance gains from an innovation accrued to each

employee. The managers rated the three items: (a) “Because of this innovation this employee improved the

quality of his/her product, service, or administration,” (b) “Because of this innovation this employee’s

morale improved,” and (c) “Because of this innovation this employee’s productivity improved.”

Control variables. We controlled for gender (0 = female, 1 = male), age, education, employees’ organizational tenure, and managers’ tenure as the leader of the current team, because these demographics

have been found to affect implementation behavior (Damanpour & Schneider, 2006). We included a country

dummy (0 = Korea, 1 = China) because the data were collected from two countries. The innovation type (0 =

administrative innovation, 1 = technological innovation) was controlled for because innovation types may

stimulate different implementation behavior (Kim & Chung, 2017).

Finally, we controlled for employees’ innovation expectations to examine the incremental contribution of

6© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Social Behavior and Personality: an international journal

employees’ attributions over and above innovation expectations (Ajzen, 1991; Beaudry & Pinsonneault,

2005). Innovation expectations were assessed by two items (α = .72) from Klein et al.’s (2001) study: (a) “I

think my organization made a good decision in adopting the innovation,” and (b) “I think the innovation is a

waste of time and money for my organization” (reverse scored). The hypothesis testing results were identical

with and without these control variables in all analyses (Becker, 2005).

Results We conducted a confirmatory factor analysis to investigate the empirical distinctiveness of the variables.

The hypothesized six-factor model showed a satisfactory fit to the data, χ

2

(df = 134) = 275.60, p < .001,

comparative fit index (CFI) = .97, root mean square error of approximation (RMSEA) = .05, and performed

significantly better than the alternative measurement models (all χ

2

tests = p < .001). We then tested the

hypothesized structural relationships. Means, standard deviations, and correlations among all the variables

are presented in Table 1.

Table 1. Means, Standard Deviations, Reliability Coefficients, and Intercorrelations Among Study Variables

Note. N = 397.

a

Country (0 = Korea, 1 = China),

b

Innovation type (0 = administrative innovation, 1 =

technological innovation),

c

Gender (0 = female, 1 = male). Internal consistency reliability coefficients are

shown on the diagonal in parentheses.

* p < .05, ** p < .01.

Because of the high level of model complexity relative to the sample size, we tested the hypothesized model

by employing path analysis and using the scale means of each construct rather than by incorporating item-

level indicators to create latent factors (Bandalos & Finney, 2001). We employed the Mplus 6.12 software

(Muthén & Muthén, 2010) for path analysis on the basis of the theoretical framework.

Hypothesized and Alternative Models

The path analytic model showed a good fit to the data, χ

2

(df = 16) = 39.89, p < .001, CFI = .98, RMSEA =

.06. We used structural equation modeling to further examine if a theoretically plausible alternative model

better explained the observed pattern in the data (Aziz, 2008). We tested an alternative model by adding

direct links from two antecedents (attributions to constructive and deceptive intentionality) to the outcome

(innovation effectiveness). The direct effect model had similar fit indices, χ

2

(df = 14) = 34.06, p < .01, CFI =

.98, RMSEA = .06, but did not significantly improve the fit of the hypothesized model, Δχ

2

(df = 2) = 5.83,

ns. In addition, no direct effect path was statistically significant. Thus, we adopted the original hypothesized

7© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Choi, Chung, Choi

model as the best fitting and parsimonious model for the data (see Figure 1).

Figure 1. Structural path analytic model of innovation attribution. The values are standardized path coefficients. Significant paths only for control variables are shown in the path diagram.

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

Table 2. Indirect Effect of Innovation Attribution on Innovation Effectiveness Through Implementation Behavior

Note. N = 397. CI = confidence interval, LL = lower limit, UL = upper limit. Number of bootstrap

resamples = 1,000.

Hypothesis Testing

8© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Social Behavior and Personality: an international journal

As presented in Figure 1, path analysis indicated that employee attribution to constructive intentionality was

positively and negatively related to active implementation and implementation avoidance (β = .16, p < .01; β

= −.18, p < .05), respectively. Hypotheses 1a and 1c were thus supported. The effect of employee attribution

to constructive intentionality on passive implementation was not significant (β = .14, ns). Thus, Hypothesis

1b was not supported.

By contrast, employee attribution to deceptive intentionality significantly predicted implementation

avoidance alone (β = .17, p < .01), but it was unrelated to employees’ active and passive implementation (β =

.10 and .04, respectively, both ns). Thus, Hypothesis 2c was supported but Hypotheses 2a and 2b were not

supported.

Figure 1 also shows that all three forms of implementation were significant predictors of the ultimate

innovation outcome. As hypothesized, active and passive implementations were positively related to

innovation effectiveness (β = .33 and .39, respectively, both p < .001), whereas implementation avoidance

was negatively related to innovation outcome (β = −.22, p < .001). Thus, Hypotheses 3a, 3b, and 3c, which

involved innovation effectiveness, were supported.

Finally, we tested Hypotheses 4a and 4b by employing a bootstrapping procedure that computes unbiased

indirect effect estimates with a 95% confidence interval (CI; Preacher & Hayes, 2008). As reported in Table

2, employee attribution to constructive intentionality had an indirect positive effect on innovation

effectiveness through its direct effect on active implementation; indirect effect estimate = .05, bootstrapped

SE = 0.02, 95% CI [0.015, 0.107], and implementation avoidance, indirect effect estimate = .04,

bootstrapped SE = 0.02, 95% CI [0.007, 0.085], but not through passive implementation; indirect effect

estimate = .03, bootstrapped SE = 0.03, 95% CI [−0.014, 0.084]. Hypothesis 4a was thus partially

supported. By contrast, employee attribution to deceptive intentionality exhibited a significant negative

indirect effect on innovation effectiveness through its effect on implementation avoidance; indirect effect

estimate = −.04, bootstrapped SE = 0.02, 95% CI [−0.078, −0.009], but not through active and passive

implementation behavior; indirect effect estimate = .03, bootstrapped SE = 0.02, 95% CI [−0.004, 0.084];

indirect effect estimate = .01, bootstrapped SE = 0.02, 95% CI [−0.032, 0.058], respectively. Thus,

Hypothesis 4b was partially supported.

Discussion In this study, we introduced a well-established social psychological theory of causal attribution to the

organizational innovation literature. Our findings demonstrate that the attribution of constructive

intentionality exerts positive indirect effects on innovation effectiveness through its positive direct effect on

active implementation and negative direct effect on implementation avoidance, but not through passive

implementation. By contrast, the attribution of deceptive intentionality exerts a negative indirect effect on

innovation effectiveness through its effect on implementation avoidance but not through active or passive

implementation.

Theoretical Implications

We have contributed to the innovation literature. We have advanced the current theoretical framework by

applying attribution theory to a new context of innovation implementation in organizations. We have

identified employees’ attributions of the cause of innovation adoption as a critical driver of their

sensemaking of innovation implementation (C. G. Davis et al., 1998; Maitlis & Christianson, 2014; Weick et

al., 2005). The results indicate the incremental effects of attributions on the implementation process are

over and above expectations of costs and benefits (e.g., Ajzen, 1991; Beaudry & Pinsonneault, 2005; F. D.

Davis, 1989).

Our theoretical and empirical analysis confirms that attribution to constructive intentionality is positively

9© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Choi, Chung, Choi

related to active implementation but not significantly related to passive implementation. This pattern

suggests that when employees attribute the organization’s intention of adopting an innovation to a genuine,

constructive cause, they tend to implement it enthusiastically instead of being passive and simply following

the minimum requirements for implementation (Parker et al., 2006). Further, attribution to constructive

intentionality diminishes undesirable implementation behavior among employees, such as avoiding or

ignoring innovation.

By contrast, attribution to deceptive intentionality increases implementation avoidance, although such

negative attribution is unrelated to either active or passive implementation behavior. The negative effect of

attribution on innovation may not reduce the positive forms of implementation behavior among employees

when organizational forces for implementation and situational pressure are present (Marler, Fisher, & Ke,

2009).

We identified and empirically differentiated three forms of implementation behavior that are in line with

the three domains of task behavior, namely, in-role, extrarole, and deviant behavior (Dalal, 2005; Klein et

al., 2001). These behavioral reactions represent different levels of employee engagement in innovation

implementation that may provide a finer grained explanation than that of previous findings based on typical

dichotomization of either acceptance or rejection of innovation or a singular focus on resistance to change

(Choi & Moon, 2013; Greenhalgh et al., 2005). These forms of implementation behavior exhibit disparate

patterns relative to innovation-targeted attributions and exert varying effects on innovation effectiveness,

thereby supporting their conceptual and functional distinctiveness in innovation implementation.

Limitations and Directions for Future Research

There are limitations in this study. First, as data were collected at a single point in time, this prevented us

from making causal inferences. Although the causal flow of the attribution–behavior–outcome relationship

is theoretically justifiable (Ferris et al., 1995; Nishii et al., 2008), the possibility of reversed causality that

innovation outcomes affect employee attributions is plausible. Second, we assessed the innovation

effectiveness measure at the individual level and evaluated specific employee performance gains (skill

acquisition, improved morale, and productivity) attributable to the innovation. These measures may be

insufficient to reveal the overall success or failure of an innovation. Finally, although we controlled for the

effects of country settings and innovation types, we acknowledge that these cultural or innovation-specific

factors can be critical in shaping innovation-related attributions and corresponding employee attitudes and

behavior.

We have nevertheless advanced the innovation literature by applying attribution theory to explain multiple

forms of implementation behavior. Thus, our findings enable future researchers to comprehensively

investigate the cognitive processes underlying organizational innovation beyond the appraisal of anticipated

outcomes or expectations based on the cost–benefit analysis of innovation implementation. Future

empirical and conceptual researchers can integrate these cognitive underpinnings, namely, attributions and

expectations, underlying innovation implementation with emotional dynamics induced by innovation

adoption.

Acknowledgements

This work was supported by the Ministry of Education of the Republic of Korea, the National Research

Foundation of Korea (NRF-2015S1A5A2A03048150), the Institute of Industrial Relations at Seoul National

University, and a Research Grant of Kwangwoon University in 2019.

References

10© 2019 Scientific Journal Publishers Limited. All Rights Reserved.

Social Behavior and Personality: an international journal

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes,

50, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Aziz, Y. A. (2008). Structural equation modeling (SEM): An alternative approach in data analysis for social

sciences studies. Integration & Dissemination, 3, 14–17.

Baer, M., & Frese, M. (2003). Innovation is not enough: Climates for initiative and psychological safety,

process innovations, and firm performance. Journal of Organizational Behavior, 24, 45–68.

https://doi.org/10.1002/job.1769

Bala, H., & Venkatesh, V. (2016). Adaptation to information technology: A holistic nomological network

from implementation to job outcomes. Management Science, 62, 156–179.

https://doi.org/10.1287/mnsc.2014.2111

Bandalos, D. L., & Finney, S. J. (2001). Item parceling issues in structural equation modeling. In G. A.

Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation

modeling (pp. 269–296). Mahwah, NJ: Erlbaum.

Beaudry, A., & Pinsonneault, A. (2005). Understanding user responses to information technology: A coping

model of user adaptation. MIS Quarterly, 29, 493–524. https://doi.org/10.2307/25148693

Becker, T. E. (2005). Potential problems in the statistical control of variables in organizational research: A

qualitative analysis with recommendations. Organizational Research Methods, 8, 274–289.

https://doi.org/10.1177/1094428105278021

Birken, S. A., Lee, S. Y. D., Weiner, B. J., Chin, M. H., Chiu, M., & Schaefer, C. T. (2015). From strategy to

action: How top managers’ support increases middle managers’ commitment to innovation implementation

in healthcare organizations. Health Care Management Review, 40, 159–168.

https://doi.org/10.1097/HMR.0000000000000018

Brislin, R. W. (1986). The wording and translation of research instruments. In W. J. Lonner & J. W. Berry

(Eds.), Field methods in cross-cultural research (Vol. 8, pp. 137–164). Thousand Oaks, CA: Sage.

Byrne, Z. S., Kacmar, C., Stoner, J., & Hochwarter, W. A. (2005). The relationship between perceptions of

politics and depressed mood at work: Unique moderators across three levels. Journal of Occupational

Health Psychology, 10, 330–343. https://doi.org/10.1037/1076-8998.10.4.330

Choi, J. N. (2007). Change-oriented organizational citizenship behavior: Effects of work environment

characteristics and intervening psychological processes. Journal of Organizational Behavior, 28, 467–484.

https://doi.org/10.1002/job.433

Choi, J. N., & Chang, J. Y. (2009). Innovation implementation in the public sector: An integration of

institutional and collective dynamics. Journal of Applied Psychology, 94, 245–253.

https://doi.org/10.1037/a0012994

Choi, J. N., & Moon, W. J. (2013). Multiple forms of innovation implementation: The role of innovation,

individuals, and the implementation context. Organizational Dynamics, 42, 290–297.

https://doi.org/10.1016/j.orgdyn.2013.07.007

Chung, G. H., & Choi, J. N. (2018). Innovation implementation as a dynamic equilibrium: Emergent

processes and divergent outcomes. Group & Organization Management, 43, 999–1036.

https://doi.org/10.1177/1059601116645913

Dalal, R. S. (2005). A meta-analysis of the relationship between organizational citizenship behavior and

counterproductive work behavior. Journal of Applied Psychology, 90, 1241–1255.

https://doi.org/10.1037/0021-9010.90.6.1241

Damanpour, F., & Schneider, M. (2006). Phases of the adoption of innovation in organizations: Effects of

environment, organization and top managers. British Journal of Management, 17, 215–236.

11© 2019 Scientific Journal Publishers Limited. All Rights Reserved.https://doi.org/10.1016/0749-5978(91)90020-Thttps://doi.org/10.1002/job.1769https://doi.org/10.1287/mnsc.2014.2111https://doi.org/10.2307/25148693https://doi.org/10.1177/1094428105278021https://doi.org/10.1097/HMR.0000000000000018https://doi.org/10.1037/1076-8998.10.4.330https://doi.org/10.1002/job.433https://doi.org/10.1037/a0012994https://doi.org/10.1016/j.orgdyn.2013.07.007https://doi.org/10.1177/1059601116645913https://doi.org/10.1037/0021-9010.90.6.1241

Choi, Chung, Choi

https://doi.org/10.1111/j.1467-8551.2006.00498.x

Davis, C. G., Nolen-Hoeksema, S., & Larson, J. (1998). Making sense of loss and benefiting from the

experience: Two construals of meaning. Journal of Personality and Social Psychology, 75,

561–574. https://doi.org/10.1037/0022-3514.75.2.561

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information

technology. MIS Quarterly, 13, 319–340. https://doi.org/10.2307/249008

Dovidio, J. F., Piliavin, J. A., Schroeder, D. A., & Penner, L. A. (2006). The social psychology of prosocial

behavior. Mahwah, NJ: Erlbaum.

Dusenbury, L., Brannigan, R., Falco, M., & Hansen, W. B. (2003). A review of research on fidelity of

implementation: Implications for drug abuse prevention in school settings. Health Education Research, 18,

237–256. https://doi.org/10.1093/her/18.2.237

Erwin, D. G., & Garman, A. N. (2010). Resistance to organizational change: Linking research and practice.

Leadership & Organization Development Journal, 31, 39–56.

https://doi.org/10.1108/0143773101101371

Ferris, G. R., Bhawuk, D. P. S., Fedor, D. B., & Judge, T. A. (1995). Organizational politics and citizenship:

Attributions of intentionality and construct definition. In M. J. Martinko (Ed.), Advances in attribution

theory: An organizational perspective (pp. 231–252). Delray Beach, FL: St. Lucie Press.

Fiske, S. T., & Taylor, S. E. (2013). Social cognition: From brains to culture (2nd ed.). Thousand Oaks, CA:

Sage.

Gilbert, D. T. (1998). Ordinary personology. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook

of social psychology (4th ed., Vol. 2, pp. 89–150). New York, NY: McGraw-Hill.

Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., Kyriakidou, O., & Peacock, R. (2005). Storylines of

research in diffusion of innovation: A meta-narrative approach to systematic review. Social Science &

Medicine, 61, 417–430. https://doi.org/10.1016/j.socscimed.2004.12.001

Jacquart, P., & Antonakis, J. (2015). When does charisma matter for top-level leaders? Effect of

attributional ambiguity. Academy of Management Journal, 58, 1051–1074.

https://doi.org/10.5465/amj.2012.0831

Jones, G. R. (2001). Organizational theory: Text and cases (3rd ed.). Upper Saddle River, NJ: Prentice

Hall.

Kim, J. S., & Chung, G. H. (2017). Implementing innovations within organizations: A systematic review and

research agenda. Innovation, 19, 372–399. https://doi.org/10.1080/14479338.2017.1335943

Klein, K. J., Conn, A. B., & Sorra, J. S. (2001). Implementing computerized technology: An organizational

analysis. Journal of Applied Psychology, 86, 811–824. https://doi.org/10.1037/0021-9010.86.5.811

Maitlis, S., & Christianson, M. (2014). Sensemaking in organizations: Taking stock and moving forward. The

Academy of Management Annals, 8, 57–125. https://doi.org/10.1080/19416520.2014.873177

Marler, J. H., Fisher, S. L., & Ke, W. (2009). Employee self-service technology acceptance: A comparison of

pre-implementation and post-implementation relationships. Personnel Psychology, 62, 327–358.

https://doi.org/10.1111/j.1744-6570.2009.01140.x

Martinko, M. J., & Gardner, W. L. (1982). Learned helplessness: An alternative explanation for performance

deficits. Academy of Management Review, 7, 195–204. https://doi.org/10.2307/257297

Morrison, E. W., & Phelps, C. C. (1999). Taking charge at work: Extrarole efforts to initiate workplace

change. Academy of Management Journal, 42, 403–419. https://doi.org/10.5465/257011

12© 2019 Scientific Journal Publishers Limited. All Rights Reserved.https://doi.org/10.1111/j.1467-8551.2006.00498.xhttps://doi.org/10.1037/0022-3514.75.2.561https://doi.org/10.2307/249008https://doi.org/10.1093/her/18.2.237https://doi.org/10.1108/0143773101101371https://doi.org/10.1016/j.socscimed.2004.12.001https://doi.org/10.5465/amj.2012.0831https://doi.org/10.1080/14479338.2017.1335943https://doi.org/10.1037/0021-9010.86.5.811https://doi.org/10.1080/19416520.2014.873177https://doi.org/10.1111/j.1744-6570.2009.01140.xhttps://doi.org/10.2307/257297https://doi.org/10.5465/257011

Social Behavior and Personality: an international journal

Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Author.

Nishii, L. H., Lepak, D. P., & Schneider, B. (2008). Employee attributions of the “why” of HR practices:

Their effects on employee attitudes and behaviors, and customer satisfaction. Personnel Psychology, 61,

503–545. https://doi.org/10.1111/j.1744-6570.2008.00121.x

Olson-Buchanan, J. B., & Boswell, W. R. (2008). An integrative model of experiencing and responding to

mistreatment at work. The Academy of Management Review, 33, 76–96.

https://doi.org/10.2307/20159377

Park, C. L. (2010). Making sense of the meaning literature: An integrative review of meaning making and its

effects on adjustment to stressful life events. Psychological Bulletin, 136, 257–301.

https://doi.org/10.1037/a0018301

Parker, S. K., Williams, H. M., & Turner, N. (2006). Modeling the antecedents of proactive behavior at work.

Journal of Applied Psychology, 91, 636–652. https://doi.org/10.1037/0021-9010.91.3.636

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing

indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891.

https://doi.org/10.3758/BRM.40.3.879

Real, K., & Poole, M. S. (2005). Innovation implementation: Conceptualization and measurement in

organizational research. In R. W. Woodman, W. A. Pasmore, & A. B. Shani (Eds.), Research in

organizational change and development (Vol. 15, pp. 63–134). Bingley, UK: Emerald Group Publishing.

Rodell, J. B., & Lynch, J. W. (2016). Perceptions of employee volunteering: Is it “credited” or “stigmatized”

by colleagues? Academy of Management Journal, 59, 611–635.

https://doi.org/10.5465/amj.2013.0566

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.

Seifert, T. (2004). Understanding student motivation. Educational Research, 46, 137–149.

https://doi.org/10.1080/0013188042000222421

Spector, P. E., & Fox, S. (2010). Theorizing about the deviant citizen: An attributional explanation of the

interplay of organizational citizenship and counterproductive work behavior. Human Resource

Management Review, 20, 132–143. https://doi.org/10.1016/j.hrmr.2009.06.002

Stanley, D. J., Meyer, J. P., & Topolnytsky, L. (2005). Employee cynicism and resistance to organizational

change. Journal of Business and Psychology, 19, 429–459.

https://doi.org/10.1007/s10869-005-4518-2

Van Dyne, L., & LePine, J. A. (1998). Helping and voice extra-role behaviors: Evidence of construct and

predictive validity. Academy of Management Journal, 41, 108–119. https://doi.org/10.5465/256902

Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the process of sensemaking.

Organization Science, 16, 409–421. https://doi.org/10.1287/orsc.1050.0133

Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review,

92, 548–573. https://doi.org/10.1037/0033-295X.92.4.548

Powered by TCPDF (www.tcpdf.org)

13© 2019 Scientific Journal Publishers Limited. All Rights Reserved.https://doi.org/10.1111/j.1744-6570.2008.00121.xhttps://doi.org/10.2307/20159377https://doi.org/10.1037/a0018301https://doi.org/10.1037/0021-9010.91.3.636https://doi.org/10.3758/BRM.40.3.879https://doi.org/10.5465/amj.2013.0566https://doi.org/10.1080/0013188042000222421https://doi.org/10.1016/j.hrmr.2009.06.002https://doi.org/10.1007/s10869-005-4518-2https://doi.org/10.5465/256902https://doi.org/10.1287/orsc.1050.0133https://doi.org/10.1037/0033-295X.92.4.548http://www.tcpdf.org

Copyright of Social Behavior & Personality: an international journal is the property of Society for Personality Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.

Order your essay today and save 10% with the discount code ESSAYHELP