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Abstract

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.
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 Choi1, Goo Hyeok Chung2, Jin Nam Choi1
1College of Business Administration, Seoul National University, Republic of Korea
2College 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
attributionstoorganizationalintentionalityasemployees’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: jnchoi@snu.kr
© 2019 Scientific Journal Publishers Limited. All Rights Reserved.
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
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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
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
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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).
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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
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
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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, χ2(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 χ2tests = 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. aCountry (0 = Korea, 1 = China), bInnovation type (0 = administrative innovation, 1 =
technological innovation), cGender (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, χ2(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, χ2(df = 14) = 34.06, p< .01, CFI =
.98, RMSEA = .06, but did not significantly improve the fit of the hypothesized model, Δχ2(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
Implementation Behavior
Note. N = 397. CI = confidence interval, LL = lower limit, UL = upper limit. Number of bootstrap
resamples = 1,000.
Hypothesis Testing
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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
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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.
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Social Behavior and Personality: an international journal
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... Technology. An applied digital innovation, idea, practice, or object that is new to users (Choi et al., 2019;Paulikas, 2018). ...
... In a holistic framework, successful change requires employees to strategically align with the company culture, the goals and vision of leadership, and needs of their specific business unit (Pak et al., 2019;Ratana et al., 2020). If an employee exhibits resistance towards new technology, efforts to change the employee attitude will have increased effectiveness if the company culture is encouraging of learning, if social influences/coworkers are positive towards the new technology, and leadership is unified and supportive of the changes (Choi et al., 2019;Ozyilmaz et al., 2018;Tayal et al., 2018). Both the content and context in which the technology is being implemented can affect employee acceptance, thus effective change management needs to address both, recombining solutions until the proper mix is developed (Fugate & Soenen, 2018;Ratana et al., 2020). ...
... Confidence in the ability of oneself, or self-efficacy, needs to be understood within the context of trust in organization. Some contextual factors which impact employee attitudes towards new technology use include overall company culture, business purpose driving the change, training methods for implementing change, and motivation techniques to encourage adaptation (Choi et al., 2019;Fugate & Soenen, 2018). Organizational culture relates to the collection of beliefs, assumptions, and values that members of an organization hold in common and which affect the social relationships in a business environment (Mohtaramzadeh et al., 2018). ...
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During the COVID-19 pandemic manufacturing companies found that to remain competitive increased automation and technological advancement was of increasing importance. Many organizations faced a lack of employees who had the necessary skills to be receptive to the needs of an increasingly automated work environment, leading to a gap between current staff and talent abilities. Instead of hiring new workers and adding time and financial expense, training current workers to have increased acceptance towards changing technology can be a viable option, if training can be strategized to address the needs of a specific workplace. This mixed methods study uses a modified unified theory of acceptance and use of technology (UTAUT) survey to quantitatively establish a baseline for technology acceptance and usage among a group of manufacturing workers in the United States, correlated to qualitative interviews with some staff to determine which training methods were found to be most effective. Overall attitudes towards new technology usage and behavioral intent were found to be positive at this facility. Through multiple regression analysis it was discovered that current usage behavior of new technology at the facility was most influenced by facilitating conditions and social influence, whereas behavioral intent was most influenced by effort expectancy and social influence. Recommendations for future training methods to specifically address areas of weakness are suggested through correlating UTAUT variables to principles found in the adult learning theory of andragogy, extrinsic and intrinsic motivators, and both synchronous and asynchronous training methods.
... Linked to the coping theory, Beaudry and Pinsonneault (2005) argue that innovation use depends on the cognitive appraisal of innovations as a threat or an opportunity. The emphasis in these theoretical models has mainly been sketched on employees' expectations of the innovation's cost and benefit, with these expectations shaping later performance, whereas Choi et al. (2019) explored the implementation of an innovation by emphasizing on the attribution role. Expectation denotes the potential outcomes or the projections of an event's outcomes, whereas attribution refers to the assumed cause of an outcome or the understanding of an event's outcome (Seifert, 2004). ...
... Being a crucial cognitive procedure, attributions are assumed to be an essential mechanism of sensemaking, affecting emotional, attitudinal, and behavioral responses, in addition to expectations (Fiske & Taylor, 2013;Martinko & Gardner, 1982;Weiner, 1985). Based on the attribution of intentionality model (Ferris et al., 1995), Choi et al. (2019) acknowledged two types of employee attributions of an organization's perceived intentionality in innovation implementation. They are attributions to constructive and deceptive intentionality. ...
... In the context of innovation implementation, attributions to intentionality trigger employees' sensemaking of the organization's innovation adoption. Thus, organization members would attribute an innovation adoption decision, made by higher authorities, to either positive or negative intentions, labeled "constructive intentionality" or "deceptive intentionality," respectively (Choi et al., 2019). Attribution to constructive intentionality refers to the members' rational analysis that their company has implemented a change with a genuine and an honest intent to accomplish beneficial results, such as organizational growth and the well-being of its stakeholders, and its employees specifically. ...
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This study tackles the extent to which employees’ attributions and acknowledgments of the innovation implementation’s urgency play a role in their acceptance and readiness behavior during a crisis. Moreover, it highlights the importance of support and knowledge sharing among organization members on social media, given that an organizational change is taking place during a crisis while everyone is being quarantined. Qualitative data are collected from semi-structured interviews as well as from the chats on the WhatsApp group created for this quick innovation implementation decision. Findings reveal that during a crisis, employees’ sensemaking of the organization’s innovation adoption is triggered by attribution to constructive intentionality. The urgency imposed boosts the contextual dimension of the readiness for change, which enhances organization members’ commitment to implement the change. Moreover, when everyone is quarantined, social media is found to be the only means for maintaining social relations, ensuring colleagues’ support and sharing knowledge; and consequently boosting members’ readiness. The value of this research lies in the topic addressed, and in the unusual context in which the innovation implementation took place.
... In conclusion, the article addressed that, due to no innovation in the education sector, students become hopeless and have no chance other than to learn according to the older education system. Thus, the literature proposed that change in terms of innovation usually faced resistance (Choi et al., 2019;Senbeto et al., 2021). Thus, the following hypothesis is derived from the above debate: ...
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Recently, second-language learning success depends upon the students' interest and motivation by adopting innovations that require regulators' and new researchers' emphasis. Hence, this article explores the role of resistance to innovation, lack of intercultural communication, and students' interest on the students' demotivation. The article also examines the mediating role of students' demotivation among resistance to innovation, lack of intercultural communication, lack of students' interest, and failure of the English education system in China. This study has gathered the data using survey questionnaires and analyzed the collected data using smart-PLS. The results exposed that the resistance to innovation, lack of intercultural communication, and students' interest have a significant and positive linkage with students' demotivation. The findings also indicated that students' demotivation significantly mediates among resistance to innovation, lack of intercultural communication, lack of students' interest, and failure of the English education system in China. This study guides the policymakers to develop the policies related to improving the English education system in China using innovation that enhances students' interest and motivation. JEL Classifications O31, O32, H75.
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Rapid changes and fierce global competition in the fourth industrial revolution are adding complexity to the evolution of organizations’ essential innovations. However, the question of how innovation complexity affects innovation implementation processes is yet to be answered whereas successful implementation of complex innovation determines not only the outcome of the given innovation but also overall firm survival. To fill the gap, the current research, based on the Job Demands-Resources model, aimed to test whether the effect of innovation complexity on innovation effectiveness is mediated by innovation learning whereas a contextual variable, innovation implementation autonomy, moderates the relationship between innovation complexity and innovation learning. In order to test the hypotheses, a survey was conducted to 171 employees and 50 managers who work for a manufacturing company located in China. The results showed that innovation complexity was positively related with both innovation learning and innovation effectiveness. In addition, innovation learning was found to fully mediate the relationship between innovation complexity and innovation effectiveness. As hypothesized, innovation implementation autonomy interacted with innovation complexity to affect innovation learning; as a result, innovation implementation autonomy had a moderated mediation effect in the relationship between innovation complexity and innovation effectiveness. Based on the above findings, theoretical implication in regard to the JD-R model and practical implication on innovation implementation processes were provided.
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Contemporary organizations often adopt innovations as their top priority to survive severe global competition and the rapidly changing business environment. Although organizational gains from using an innovation (i.e. innovation success) appear to largely depend on the correct decision to adopt an effective innovation, ultimately, innovation success cannot be obtained until individuals consistently use or implement the innovation (i.e. implementation success). Considering the importance of innovation implementation, we scrutinize the factors influencing the implementation process and outcomes for all innovation types (e.g., technological and process, service and product, and administrative innovation). Specifically, we conduct a systematic review of the existing studies on innovation implementation and categorize the factors into four groups: innovation characteristics, social factors, organizational factors, and individual factors. Drawing on the findings obtained from our investigation and our insights shaped during the systematic review process, we suggest three future research agendas: (a) consider individual factors as the primary predictors of an individual’s implementation behavior toward innovation; (b) examine the possibility that innovations change during their implementation and that multiple forms of implementation outcomes can result; and (c) uncover the implementation mechanism of organizations that continuously adopt and implement innovations over a prolonged period.
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Information technology (IT) implementation is a major organizational change event that substantially disrupts an employee’s work environment. We develop a model of technology adaptation behaviors that employees perform to cope with a new IT that causes such disruptions. Our model posits technology adaptation behaviors as a key linking mechanism between IT implementation and employee job outcomes, thus offering a holistic nomological network of technology adaptation behaviors. Two field studies conducted over a period of six months, with four waves of data collection each, in two organizations (N = 211 and N = 181) implementing two different ITs, supported the model. We found that employees performed four different technology adaptation behaviors—exploration-to-innovate, exploitation, exploration-to-revert, and avoidance—based on whether they appraised an IT as an opportunity or a threat and whether they had perceptions of control over an IT. Employees’ experiential engagements (i.e., user participation and training effectiveness) and psychological engagements (i.e., user involvement and management support) during the implementation jointly determined their appraisal of an IT. Finally, we found that technology adaptation behaviors influenced changes in two key job outcomes, job performance and job satisfaction. This paper was accepted by Sandra Slaughter, information systems.
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Innovation literature typically postulates a linear and institution-driven implementation process that leads to bifurcated outcomes (i.e., acceptance or rejection) of innovation. Adopting a grounded theory approach and a social constructionist perspective, we explore dynamic, interactive implementation processes unfolding over time; these processes generate divergent and often unexpected outcomes. The present qualitative analysis of 40 cases of innovation reveals that two competing forces shape the implementation process. As initiators of innovation implementation, top managers form a driving force and introduce various tactics to facilitate implementation. Resistors or individuals against innovation form a resisting force and organize various schemes to inhibit implementation. The relative strengths of driving and resisting forces lead to four different patterns of implementation, namely, implementation without change, modified implementation, minimal implementation, and implementation failure. Dynamic interactions between initiators and resistors shift implementation outcomes by changing the perceptions of followers with regard to innovation characteristics. The resulting theoretical framework highlights the political nature of innovation implementation and indicates the need to consider socioeconomic and sociopolitical dynamics involving multiple organizational actors.
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As research begins to accumulate on employee volunteering, it appears that this behavior is largely beneficial to employee performance and commitment. It is less clear, however, how employee volunteering is perceived by others in the workplace. Do colleagues award volunteering "credit" (e.g., associating it with being concerned about others) or do they "stigmatize" it (e.g., associating it with being distracted from work)? Moreover, do those evaluations predict how colleagues actually treat employees who volunteer more often? Adopting a reputation perspective, we draw from theories of person perception and attribution to explore these research questions. The results of a field study reveal that colleagues gave credit to employee volunteering when they attributed it to intrinsic reasons and stigmatized employee volunteering when they attributed it to impression management reasons. Ultimately, through the awarded credits, volunteering was rewarded by supervisors (with the allocation of more resources) and coworkers (with the provision of more helping behavior) when it was attributed to intrinsic motives-a relationship that was amplified when stigmas were low and mitigated when stigmas were high. The results of a laboratory experiment further confirmed that volunteering was both credited and stigmatized, distinguishing it from citizenship behavior, which was credited but not stigmatized.
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This paper presents a theory of learned helplessness to account for passive maladaptive behavior in organizations. The essential hypothesis is that the properties of formal organizations often inadvertently condition employee failure and that this behavior frequently continues even after environmental changes make success possible. A model describing the intrapersonal process of becoming helpless is developed. Particular attention is devoted to the description of strategies for minimizing organizationally induced helplessness.