This week’s journal article focuses on attribution theory and how it influences the implementation of innovation technologies. Two types of employee attributions are noted in the article (intentionality and deceptive intentionality), please review these concepts and answer the following questions:
Provide a high-level overview/ summary of the case study
Note how constructive intentionality impacts innovation implementations
Find another article that adds to the overall findings of the case and note how attribution-based perspective enhances successful innovation implementations. Please be explicit and detailed in answering this question.
Be sure to use the UC Library for scholarly research. Google Scholar is also a great source for research. Please be sure that journal articles are peer-reviewed and are published within the last five years. The paper should meet the following requirements:
4-5 pages in length (not including title page or references)
APA guidelines must be followed. The paper must include a cover page, an introduction, a body with fully developed content, and a conclusion.
A minimum of five peer-reviewed journal articles.
Social Behavior and Personality , Volume 47, Issue 7, e8124
Why are we having this innovation? Employee attributions of
innovation and implementation behavior
Se Yeon Choi
, Goo Hyeok Chung
, Jin Nam Choi
College of Business Administration, Seoul National University, Republic of Korea
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.
attribution to constructive
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
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]
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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, &
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
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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
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Choi, Chung, Choi
the following hypotheses:
Hypothesis 1a: Employee attribution to constructive intentionality will be positively related to active
Hypothesis 1b: Employee attribution to constructive intentionality will be positively related to passive
Hypothesis 1c: Employee attribution to constructive intentionality will be negatively related to
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
Hypothesis 2b: Employee attribution to deceptive intentionality will be negatively related to passive
Hypothesis 2c: Employee attribution to deceptive intentionality will be positively related to
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
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
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Social Behavior and Personality: an international journal
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
Hypothesis 4a: Attribution to constructive intentionality will have an indirect positive effect on
innovation effectiveness through increased active and passive implementation and decreased
Hypothesis 4b: Attribution to deceptive intentionality will have an indirect negative effect on innovation
effectiveness through decreased active and passive implementation and increased implementation
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
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.
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).
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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
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
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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).
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
(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
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
Note. N = 397.
Country (0 = Korea, 1 = China),
Innovation type (0 = administrative innovation, 1 =
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
(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
(df = 14) = 34.06, p < .01, CFI =
.98, RMSEA = .06, but did not significantly improve the fit of the hypothesized model, Δχ
(df = 2) = 5.83,
ns. In addition, no direct effect path was statistically significant. Thus, we adopted the original hypothesized
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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
Note. N = 397. CI = confidence interval, LL = lower limit, UL = upper limit. Number of bootstrap
resamples = 1,000.
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