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INTERNATIONAL JOURNAL OF RESEARCH IN BUSINESS AND SOCIAL SCIENCE 11(9)(2022) 77-89
* Corresponding author. ORCID ID: 0000-0002-4605-9847
© 2022 by the authors. Hosting by SSBFNET. Peer review under responsibility of Center for Strategic Studies in Business and Finance.
https://doi.org/10.20525/ijrbs.v11i9.2177
Assessment of behavioral maintenance for organizational change in
the context of Ethiopian commercial banks
Abay Kidane (a)* Zhao Xuefeng (b)
(a)Department of Management, Huazhong University of Science and Technology, Louyu Road, 1037, Hongshan District, Wuhan, China
(b)Professor, Management Science and Information Management, Huazhong University of Science and Technology, Wuhan, China
A R T I C L E I N F O
Article history:
Received 22 October 2022
Received in rev. form 26 Nov. 2022
Accepted 14 December 2022
Keywords:
Behavioral maintenance,
organizational change, mixed-
analytical approach, quantitative
study, commercial banks
JEL Classification:
M12, M54
A B S T R A C T
Behavior change maintenance can guide the development and evaluation of interventions promoting
sustained behaviors in organizational changes. This research aims to examine the factors that influence
behavioral maintenance for organizational change in Ethiopian commercial banks. The study
developed a comprehensive model to explain the mechanism of behavioral maintenance for
organizational change by employees, using self-determination theory with two additional exogenous
constructs, value congruence and excessive work demands. Applying mixed-analytical approaches,
including SEM and fsQCA, advances the knowledge of how employees motivate to maintain their
behavior regarding the organizational change. The target population consists of lower-level managers
and 317 valid responses were retained for further analysis. In our findings, the SEM results reveal that
perceived relatedness, perceived competency, perceived autonomy, and perceived enjoyment influence
employees' behavioral maintenance for organizational change, the fsQCA results indicated that value
congruence must always be combined in these variables. The findings suggested an alternative path
that might serve as the basis for sustaining organizational change.
© 2022 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
(http://creativecommons.org/licenses/by/4.0/).
Introduction
There are two primary challenges for many organizations today. The first challenge is determining actions to take in response to
emerging business needs. The second challenge, and perhaps the more difficult one, is to determine how to ensure that the change
becomes part of the organization's fabric in the long term (Andrew Ronnie Mugenyi et al., 2022).
Despite the best efforts of those involved, many change efforts fail to persist in organizations (Raetzell et al., 2018). As per the
researcher, the persistence of change is distressingly low in most organization development interventions. Most of the efforts either
made some initial improvements but failed to sustain them or made no improvements. Thus, there is compelling evidence to suggest
the continuing uncertainty of maintaining organizational change over time, despite the ongoing need for managers to implement
change to remain productive and competitive.
Behavioral maintenance has emerged as an issue among researchers and managers (Ruggerio, 2021). Maintenance means that the
change effort has received sufficient acceptance by individuals and groups to achieve the intended goals (Andrew Ronnie Mugenyi
et al., 2022). Of concern here is whether the desired change becomes part of the organization's ongoing activities to replace what
existed beforehand. Maintenance depends on individuals to model the change behaviors for others to observe and repeat (Raetzell et
al., 2018; Owen et al., 2021, Andrew Ronnie Mugenyi et al., 2022).
Organizational change maintenance implies that new working methods and performance levels persist for a period appropriate to the
setting. At the same time, behavioral maintenance is the continuous performance of behavior following an initial intentional change
at a level that significantly differs from the baseline performance in the intended direction.
Research in Business & Social Science
IJRBS VOL 11 NO 9 (2022) ISSN: 2147-4478
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Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
78
This paper surveys the emerging literature on behavioral maintenance for organizational change. The aims are to advance theoretical
understanding and to develop a provisional model that meets two criteria. First, it should articulate the attributes and complexities of
the process. Second, it should inform further empirical research. Therefore, the primary audience for this survey is managers of
Ethiopian commercial banks concerned with organizational change. However, a literature review shows that few studies have been
conducted to understand behavioral maintenance for organizational change. If behavioral maintenance can ensure organizational
change's success, and if few studies have been done to understand current practices that would help guide future practices, then more
needs to be known about current behavioral maintenance practices.
In this study, the researcher drew from the processual approach to behavioral maintenance to develop the study's conceptual
framework. The processual approach helps examine behavioral maintenance because it emphasizes the "flow of events in a wider
spatial, temporal, and political context" (Buchanan et al., 2005). As a result, the researcher will investigate how the intrinsic form of
motivational factors (Deci & Ryan, 1985; 2008) and excessive work demands and value congruence (Dominika Kwasnicka et al.,
2016) influence behavioral maintenance for organizational change. Hence, behavioral maintenance can involve deliberate steps to
integrate organizational change outcomes into the organization's operations, processes, and culture. Unfortunately, there has been
little empirical research into factors affecting behavioral maintenance, which this study addresses. Therefore, this study addresses
the following research question: "What factors influence behavioral maintenance for organizational change?".
The target population consists of lower-level managers and a mixed-method analytical approach, including PLS-SEM and fsQCA,
was utilized together. The remaining portions of the study are structured as follows: Section 2 reviews earlier studies that are relevant
to our investigation. Section 3 incorporates the research methodology. The outcomes of the data analysis are then displayed and
explained in Section 4. The conclusion, limitations, implications, recommendations, and suggestions for future research are provided
in Section 5.
Literature Review
There is considerable evidence that behavior can be effectively modified through behavior change interventions (Albarracin et al.,
2005; Hobbs et al., 2013). However, evidence for the sustainability of behavior change in response to interventions is limited (Avenell
et al., 2004; Carpenter et al., 2013; Dombrowski, Knittle, Avenell, Araújo-Soares, & Sniehotta, 2014; Fjeldsoe, Neuhaus, Winkler,
& Dakin, 2011). This is partly because few studies evaluate long-term effects and partly because intervention effects diminish over
time and relapse rates are high (Curium & Lourenco, 2005; Dombrowski, Avenell, & Sniehotta, 2010).
The theory of behavior change maintenance can guide the development and evaluation of interventions promoting sustained
behaviors in organizational changes. Current evidence about the effectiveness of theory-based interventions in organizational change-
related behaviors is inconsistent (Dominika Kwasnicka et al., 2016). The inconsistency may partly be due to the lack of theoretical
elaboration on the maintenance process after the initial change.
Prior behavior may be the dominant response across times and contexts before behavior changes. After behavior change, newly
adopted behavior may become the dominant response in many contexts. The new behavior will likely be maintained over time if it
becomes the dominant response across contexts (Avenell, Araújo-Soares, & Sniehotta, 2014). Thus, a theoretical analysis of behavior
change maintenance will need to consider various behavioral options and the probability of them being enacted over time and across
contexts. It is currently unclear what conditions are required to maintain the new behavior and prevent relapse or to re-establish the
new behavior after relapse.
Perceived Enjoyment
Behavior is more likely to be sustained if the reinforcement structure emphasizes immediate and affective outcomes rather than
rational and long-term outcomes. Motivation to avoid negative consequences is hypothesized to be insufficient to maintain preventive
behavior that requires maintained effort; therefore, positive maintenance motives are needed (Acharya and Jena, 2016; Weinstein &
Sandman, 1992; Weinstein, 1988). Individuals engage more strongly in what they do if they feel right about it and if it fits with their
decisions and prior engagement (Maier et al., 2016; Higgins, 2006). Their engagement may include the enjoyment of performing the
behavior as such or the enjoyment of immediate outcomes of the behavior (Raetzell et al., 2018; Hall & Fong, 2007; Rothman, 2000;
Rothman et al., 2004; Stevens, Bult, de Greef, Lemmink, & Rispens, 1999). After the initial adoption of new behaviors, individuals
are theorized to evaluate the results of their efforts cognitively and emotionally. If the actual results reflect the desired results, the
initial motivation is reinforced, and individuals are likely to make positive self-judgments and sustain their efforts (Shilpa, 2021; De
Bruin, Hosters, Van Den Borne, Kok, & Prins, 2005). The nature and timing of anticipated and experienced outcomes impact behavior
change maintenance. Thus, the following hypothesis is proposed:
H-1: Perceived Enjoyment has a strong significant effect on behavioral maintenance for organizational change.
Perceived Competency
It is an individual's self-perception of their capabilities and ability to control their environment and situation. It is how skilled and
influential people perceive themselves in a particular situation (Frank Martela and Tapani J. J. Riekki, 2018). Individuals typically
choose challenges suitable to their capabilities and need to gain mastery of tasks and learn different skills. When people feel they
Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
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have the skills needed for success, they are more likely to take action to help them achieve their goals (Martela et al., 2017). An
essential characteristic of perceived competence is the need to effect change in the environment and attain valued outcomes (Chen et
al., 2015). This distinguishes tasks that will satisfy the need for competence from other mundane, trivial, or personally meaningless
tasks, the performance and mastery of which would not be expected to satisfy the need. In this sense, competence is more than merely
some 'ability' to perform a task and includes consideration of the personal importance of the task (Rodgers, W.M., Markland, D.,
Selzer, A-M, Murray, T.C., & Wilson, P.M., 2014). Developing mastery or perceived competency can help maintain new behaviors.
Thus, the following hypothesis is proposed:
H-2: Perceived Competency has a strong significant effect on behavioral maintenance for organizational change.
Perceived Autonomy
Autonomy refers to people's ability to choose how to carry out their responsibilities. Giving people autonomy at work is critical to
individual and organizational success since autonomous employees are free to select how to execute their tasks and be more
productive (Malinowska et al., 2018). Job autonomy is a suitable working setting that allows individuals to use their decision-making
skills in carrying out job-related duties (Zhang et al., 2017). Employees' perceptions of their job autonomy, according to Hackman
and Oldham's model, tend to influence their psychological states of "experienced meaningfulness of work" (i.e., how work makes a
difference to others), "felt responsibility" (i.e., the degree of responsibility assumed for work), and "knowledge of results" (i.e.,
awareness of work quality) (Nwoksu,2013; Lin & Ping, 2016). Furthermore, improving autonomy support in the workplace entails
the management acknowledging an employee's point of view, providing an explanation, and presenting scenarios that allow workers
to choose (Shao et al., 2017). Hackman and Oldham's model states autonomy is more important in determining motivating potential.
The motivating potential score will be very low if someone's job has no autonomy, regardless of variety, identity, or significance
levels. A job with a high degree of autonomy instills in the employee a sense of responsibility and increases their involvement. When
employees believe the organizational change could alleviate workplace problems, enhance the current working condition, or give job
autonomy, they are more willing to integrate change outcomes into their daily operations and persist associated behaviors within the
organization after the adaption phase. Thus, the following hypothesis is proposed:
H-3: Perceived Autonomy has a strong significant effect on behavioral maintenance for organizational change.
Perceived Relatedness
It involves experiencing a sense of purpose in what one does and how one relates to others. It indicates the amount of acceptance and
support from others, involvement, sharing, good communication, and help when needed (M. Anthony Machin et al., 2009). While
the motivation of employees is directly related to the types of interactions they get regularly, the current research considered perceived
relatedness as a predictor of behavioral maintenance for organizational change (Kimberlee Leonard, 2018). Several scholars say
positive interactions increase good feelings, morale, and work satisfaction. Understanding perceived relatedness will motivate people
to mitigate uncertainties, which adversely affect work efficiency and behavioral maintenance for organizational change (Chambers
et al., 2013). In light of a positive working climate, employees are more likely to maintain their behavioral support for organizational
change. Thus, this study hypothesizes the following:
H-4: Perceived Relatedness has a strong significant impact on behavioral maintenance for organizational change.
Value Congruence
It is the extent to which personal identity and beliefs agree with the organization (Atkinson, Kaplan, Matsumura, & Young, 2012).
In other words, it is deemed as the intent of organizational control (Malmi & Brown, 2008). Employees enjoy engaging in the
behavior; if it is congruent with their values. Scholars have argued that individual and organizational values are inevitably at odds
with one another (Yamoah, 2014, Ding Jingjing, 2017). Yamoah (2014) suggests value congruence is significant to attain an
organization's strategic objectives and ensuring the coordination and motivation of all employees concerned. If value incongruence
is not stopped in time, it will encourage organizational actors to pursue individual values at the expense of the official organizational
objectives. Also, they may avoid the change from becoming part of the organization's fabric in the long term. There has been a
research gap in the literature about how value congruence influences organizational changes' sustainability. Thus, this study believes
that behavioral maintenance for organizational change is determined by value congruence and hypothesizes that:
H-5: Value Congruence significantly positively affects behavioral maintenance for organizational change.
Excessive Work Demand
It reflects a negative perception of the workplace through the extent to which staff is overloaded with constant pressure to keep
working, leaving no time to relax (Shilpa, 2021). Overwhelming workloads are beyond the limits and set unrealistic expectations.
Research has shown that high work demands, such as long hours or the pressure to work very hard or fast, may result in cognitive
and emotional exhaustion, such as difficulty concentrating, lack of motivation, or trouble staying on task (Ruggerio, 2021). According
to Raetzell et al. (2018), the persistence of change is distressingly low in most organization development interventions. Most of the
efforts either made some initial improvements but failed to sustain them or made no improvements. Thus, there is compelling
evidence to suggest the continuing uncertainty of maintaining organizational change over time, which might be due to excessive
Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
80
work demands resulting from change adaption. Consequently, excessive work demands have a significant detrimental impact on
behavioral maintenance for organizational change. Thus, this study proposed the following hypothesis:
H-6: Excessive Work Demands significantly negatively affect behavioral maintenance for organizational change.
Research and Methodology
The target population consists of lower-level managers. Lower-level managers serve as a liaison between the organization's leadership
that sponsors a change initiative and the people impacted by the change. They help articulate reasons for the change, answer questions,
and persuade others on the necessity of the initiative while bringing concerns voiced by the organization to the attention of leadership.
Yamane's (1967) formula [𝑛 = 𝑁/(1 + 𝑁(
〖
𝑒
〗
^2 )] was used to calculate the sample, which was adopted by (AlAmeri, 2017).
They define the sample size as n, the population size as N, and the level of precision as e (95 percent or 0.05). It is also compared to
Glenn's (1992) published tables, which Singh, Ajay S; et al. (2014) suggested. Employees with the job titles "Branch Manager",
"Customer Service Manager," and "Business Development Manager" represented corporates at the organization's lower hierarchical
level, with daily contact with internal and external customers. In the title mentioned above, 3813 employees work in Addis Ababa
branches. Among them, a total number of 362 people were selected from Ethiopian commercial banks in Addis Ababa. The sample
included only managers with a three-year or longer tenure in the organization and managers from Grade A branches (a branch that
manages several employees and has excellent banking transactions).
However, data were collected using a non-probability convenient sampling technique (Iqbal & Iqbal, 2020). Nobody was asked for
any identifying information, and confidentiality was thus ensured. The questionnaire was completely anonymous, and the data
obtained was kept strictly confidential. The questionnaire began with an overview of the research objective and ensured oral consent
to participate in the study.
A self-reported survey questionnaire, including demographic information, was distributed to target participants online (Telegram,
Messenger). Direct data were collected from different branches of the commercial banks and human resource offices. Throughout
the survey, a total of 334 responses were received. Initially, data were thoroughly reviewed for fraudulent content. Accordingly, 317
valid responses were retained for further analysis. The findings revealed that Cronbach's alpha, which represents the reliability of a
measurement, was above the recommended threshold of 0.70 (Hair et al., 2013).
Measurement Instruments
The survey questionnaire consisted of two sections. The first section included demographic information of the participants, while the
second section investigated behavioral maintenance for organizational change. To further comprehend demographic information, the
following questions: age, gender, education, positional tenure, position, frequency of organizational change, experience regarding
organizational change, and frequency of follow-up as a manager was asked.
The construct and its corresponding items were taken from the relevant literature to confirm the content validity. The survey
instruments were adopted with seven constructs. Among them, perceived relatedness (four items), perceived competency (four items),
perceived autonomy (five items), and perceived enjoyment (three items) were adopted and modified from Deci, Ryan, Gagné, Leone,
Usunov, & Kornazheva (2001); Ilardi, Leone, Kasser, & Ryan (1993); Kasser, Davey, & Ryan (1992); Dominika Kwasnicka (2016).
To scale of excessive work demands and values congruence were derived from Andrew Ronnie Mugenyi et al. (2022) and Becker et
al. (1996), and Michael E. Brown et al. (2006), respectively. Additionally, to scale of behavioral maintenance for organizational
change (six items) was derived from Trish Reay et al. (2013) and Andrew Ronnie Mugenyi et al. (2022). Details of the items are to
be found in the supplementary document. The respondents were asked to rate their feelings by answering a 5-point Likert scale, with
one (1) strongly disagreeing and five (5) strongly agreeing.
Findings and Discussions
Measurement Model
The researcher tested the model fitness of all constructs in this study to prevent model misspecification (Hu & Bentler, 1998; Arshad
et al., 2018). Several studies suggested that the standard values for the various components of model fitness, such as standardized
root mean square residual (SRMR), should be less than or equal to 0.08, and the values of NFI (normated fit index) should be greater
than 0.90 for the model to be significant. The model's results revealed the best fit: SRMR =0.073; and NFI =0. 923. The outer model
validation is the first step in using the PLS-SEM method, followed by the inner model path calculation. Validating the outer model
entails determining the constructs' convergent and discriminant validity and their reliability (Wetzels et al., 2009). After validating
the model, the inner model is fitted by calculating the path coefficients. This study took into account the Alpha Cronbach's, Average
Variance Extracted (AVE), and Composite Reliability (CR) to confirm convergent validity, and both Fornell-Larcker and Heterotrait-
Monotrait Ratio were applied to justify discriminant validity (Hair, Howard, & Nitzl, 2020). Table 1 confirms Cronbach's alpha
values higher than 0.50 have been used to measure the internal reliability of the constructs. The average variance extracted, which
reflects the overall variance in the indicators accounted for by the latent construct, exceeded the recommended value of 0.5. In
contrast, composite reliability values, which show how well the construct indicators indicate the latent construct, exceeded the
recommended value of 0.7 (Hair et al., 2013). Moreover, the average shared variance of any construct and its indicators is greater
Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
81
than any of the shared variances with other constructs, demonstrating discriminant validity (Fornell and Larcker, 1981). As shown
in Table 2, the square root of AVE is greater than the inner correlation of the constructs, which portrays that this research satisfies
the criteria for discriminant validity. Finally, Henseler et al. (2015) proposed an alternative approach to assessing discriminant
validity: the heterotrait-monotrait correlation ratio (HTMT). This new method was used to test discriminant validity, and the results
are shown in Table 3. If the HTMT value is greater than the HTMT.85 value of 0.85 (kline,2011), then discriminant validity is a
problem. However, as shown in Table 3, all values do not exceed HTMT.85, confirming the qualified discriminant validity.
Table 1: Analysis of Convergent Validity
Cronbach's Alpha
rho_A
Composite Reliability
Average Variance
Extracted (AVE)
BMC
0.763
0.766
0.863
0.791
EWD
0.746
0.756
0.845
0.574
PAut
0.646
0.676
0.846
0.783
PCom
0.738
0.749
0.838
0.768
PEnj
0.608
0.616
0.909
0.733
PRel
0.728
0.731
0.829
0.778
VC
0.649
0.657
0.849
0.704
Source: Adopted by authors
Table 2: Discriminant Validity Analysis (Fornell-Larcker Criterion)
BMC
EWD
PAut
PCom
PEnj
PRel
VC
BMC
0.87
EWD
0.16
0.81
PAut
0.17
0.07
0.86
PCom
0.26
0.17
0.19
0.79
PEnj
0.09
0.04
0.18
0.13
0.76
PRel
0.28
0.17
0.22
0.19
0.48
0.84
VC
0.36
0.27
0.17
0.29
0.26
0.16
0.88
Source: Adopted by authors
Table 3: Heterotrait-Monotrait ratio (HTMT)
BMC
EWD
PAut
PCom
PEnj
PRel
VC
BMC
EWD
0.21
PAut
0.27
0.18
PCom
0.46
0.26
0.29
PEnj
0.14
0.11
0.28
0.23
PRel
0.39
0.27
0.31
0.29
0.51
VC
0.43
0.34
0.21
0.38
0.31
0.42
Shaded boxes are the standard reporting format for the HTMT procedure.
Source: Adopted by authors
Structural Model
Although the measurement model yielded significant results, this study continued to investigate the structural model before drawing
any conclusions. A bootstrapping method with 5,000 sub-samples was used to ensure that the model accurately depicts the
relationship between various paths (Hair, Anderson, Babin, & Black, 2010). In statistical hypothesis testing, Path coefficient (β), T-
Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
82
Statistics, and P-values were reported to decide whether the hypotheses were accepted. Evidence from Table 4 and Figure 1, perceived
enjoyment (β = 0.162, P < 0.05), perceived competency (β = 0.375, P < 0.05), perceived autonomy (β = 0.394, P < 0.001), and
perceived relatedness (β = 0.243, P < 0.05) have significant positive impact on behavioral maintenance for organizational change,
which supports the H-1, H-2, H-3, and H-4 respectively. Despite these results, value congruence (β = 0.083, P > 0.05) and excessive
work demands (β = -0.083, P > 0.05) show insignificant positive and negative impact on behavioral maintenance for organizational
change respectively. Thus, hypotheses H-5 and H-6 were insignificant. Surprisingly, the relationship between value congruence and
BMC does not seem to influence substantially. This is because sometimes employees pursue organizational values at the expense of
individual objectives. Finally, there is a possible reason for the insignificant findings of excessive work demands that pressure at
work was insufficient to influence behavioral maintenance negatively. The tested model R2 results demonstrated that the model
could explain an acceptable portion of the variance of the constructs (R2 =0.675). These findings were consistent with the criteria
proposed by (Chin, 1998; Hair et al., 2011 & Hair et al., 2013); thus, the model's nomological validity is deemed satisfactory (Chin,
1998). Then effect sizes (f2) are calculated. The p-value in the results shows the significance of the relationships but not the size of
an effect. As a result, readers have difficulty interpreting data and results. As a result, both substantive (f2) and statistical significance
(p) must be reported (Hair et al., 2013). The researcher used Cohen's (1988) guidelines to calculate effect size: >=0.02 for small
effects, >=0.15 for medium effects, and >=0.35 for large effects. The effect size of all relationships is shown in Table 6.6. The results
indicate that perceived autonomy has a large effect (f2 = 0.362) on behavioral maintenance for organizational change. Aside from the
size of R2 and f2, the predictive sample reuse technique (Q2) can effectively demonstrate predictive relevance (Chin et al., 2008). Q2
demonstrates how well data can be empirically reconstructed using the model and the PLS parameters based on the blindfolding
procedure. A Q2 greater than 0 indicates that the model is predictively relevant, whereas a Q2 less than 0 indicates that the model is
not predictively relevant. Our model correctly predicted behavioral maintenance for organizational change since Q2 is equal to 0.530
in Fig.1.
Table 4: PLS-SEM Path Analysis
Hypothesis
β
T Statistics
P Values
2.50%
97.50%
Decision
f2
H6-1
PEnj -> BMC
0.162
3.783
0.021
0.051
0.203
Supported
0.046
H6-2
PCom -> BMC
0.375
7.034
0.004
0.292
0.473
Supported
0.039
H6-3
PAut -> BMC
0.394
8.807
0.000
0.321
0.491
Supported
0.362
H6-4
PRel -> BMC
0.243
4.519
0.038
0.047
0.224
Supported
0.017
H6-5
VC -> BMC
0.083
0.061
0.951
-1.62
2.529
Unsupported
0.194
H6-6
EWD -> BMC
0.083
0.022
0.983
-3.206
3.159
Unsupported
0.089
Predictive Relevance: R-Square: 0.675, Q-Square: 0.530 (DV = BMC)
Source: Adopted by authors
Perceived Enjoyment
Perceived Competency
Perceived Autonomy
Value Congruence
Self-determination
Theory
Excessive Work Demands
Perceived Relatedness
Behavioral Maintenance
for Organizational Change
R2 = 0.675
Q2 = 0.530
β =0.394
β =0.083
β =0.083
⁎ Significant at a 0.05 level ⁎⁎ Significant at a 0.01 level
Figure 1: Results of the Proposed Model; Source: Adopted by authors
Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
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Post-hoc Analysis of PLS-SEM
The IPMA allows researchers to supplement their PLS-SEM findings (Ringle and Sarstedt, 2016). The primary goal of the IPMA is
to investigate important predictors that allow for significant impact while having a lower average latent factor score (Pisitsankkhakarn
& Vassanadumrongdee, 2020). To better understand behavioral maintenance for organizational change, we used IPMA in
conjunction with standard PLS analysis. Table 5 and Figure 2 exhibit IPMA findings, which imply that perceived autonomy has a
substantial effect on behavioral maintenance for organizational change, with the highest total effect score of 0.394 at the performance
level of 41.853. By looking at this figure, increasing one unit of "perceived autonomy" will increase behavioral maintenance by
0.394. Accordingly, perceived competency enhances behavioral maintenance at a total effect of 0.375, with an overall performance
level of 52.964. These findings confirm that commercial banks should emphasize these predictors a lot.
Table 5: Importance-Performance Analysis
Constructs
Importance
Performance
EWD
0.083
47.014
PAut
0.394
41.853
PCom
0.375
52.964
PEnj
0.162
47.462
PRel
0.243
46.91
VC
0.083
48.689
Source: Adopted by authors
Figure 2: Importance-Performance Map Analysis; Source: Adopted by authors
Asymmetric Analysis (fsQCA)
The use of fsQCA can offer several benefits compared to traditional analysis methods. To capture combinations of conditions
sufficient for an outcome to occur, fsQCA uses both qualitative and quantitative assessments and computes the degree to which a
case belongs to a set (Ragin, 2000; Rihoux & Ragin, 2009), thus creating a bridge between qualitative and quantitative methods.
FsQCA uses calibrated measures as data are transformed into the [0, 1] range. The main benefits of fsQCA occur when compared to
typical variance-based methods and the latter's limitations (El Sawy et al., 2010; Liu et al., 2017; Woodside, 2013, 2014). In general,
variance-based methods examine variables in a competing environment as they compute the net effect between variables in a model.
At the same time, fsQCA focuses on the complex and asymmetric relations between the outcome of interest and its antecedents. An
outcome may be the result of a variety of combinations, and each combination contributes independently to it. As fsQCA is based on
fuzzy sets, the tool enables capturing conditions that are sufficient or necessary to explain the outcome and insufficient on their own
but are necessary parts of solutions that can explain the result. These are called INUS conditions; insufficient but necessary parts of
a condition that is itself unnecessary but sufficient for the result (Mackie, 1965). Such conditions may be present or absent in a
solution or maybe conditions for which we "do not care". The "do not care" situation indicates that the outcome may either be
present or absent and does not play a role in a specific configuration (Fiss, 2011). Thus, using fsQCA, researchers can identify which
conditions are indispensable (or not needed) for an outcome and which combinations of conditions are more (or less) important than
others. While symmetric analysis implies that high coefficient values for predictor variables are necessary and sufficient to predict
outcome variables, asymmetric analysis suggests that high coefficient values for predictor variables are sufficient but not necessarily
essential to predict outcome variables (Kaya et al., 2020).
Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
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Calibration
The most crucial step in fsQCA is data calibration. When a variable or construct is measured with multiple items, we need to compute
one value per construct that will be used as input in fsQCA. In other words, for each case (row) in our dataset, we need one value for
every construct (column). The simplest way is to compute the mean of all the items to create one single value per case (such as when
testing correlations test). (DiStefano, Zhu, & Mindrila, 2009). In fsQCA, we need to calibrate our variables to form fuzzy sets with
values ranging from 0 to 1 (Ragin, 2008). The fact that all values range from 0 to 1 means that a case with a fuzzy membership score
of 1 is a full member of a fuzzy set (entirely in the set), and a case with a membership score of 0 is a full non-member of the set
(entirely out of the set). A membership score of 0.5 is precisely in the middle; thus, a case would be both a member of the fuzzy set
and a non-member and a member of what is known as the intermediate set. We used percentiles to find which values in our dataset
correspond to 0.95, 0.50, and 0.05. The percentiles allow the calibration of any measure regardless of its original values. In detail,
we computed the 95 %, 50 %, and 5 % of our measures and used these values as the three thresholds in fsQCA software. Table 6
presents the original values that correspond to each threshold.
Table 6: Compute thresholds using percentiles
PRel
PCom
PAut
PEnj
EWD
VC
BMC
5%a
2.5
2
1.75
2.5
2.25
2.5
1.8333
50%b
4
4.5
3.75
4
4
4
4
95%c
5
5
5
5
5
5
4.8333
a Full non-membership, b Cross-over point, c Full membership
Source: Adopted by authors
Analysis of Necessary Conditions
In fsQCA, the step of formulating necessary conditions should always come first. This study examines 'behavioral maintenance for
organizational change' in the PLS-SEM model (Figure 1), as well as the outcome conditions that are associated with it. The fsQCA
analysis examines the conditions of six predictors for the outcome variable, like with the SEM model, that affects 'behavioral
maintenance for organizational change' and investigates all conditions that influence and do not influence the outcome results.
According to Rihoux & Ragin (2009), the consistency range is from 0-1. Typically, the consistency range should never be less than
0.75 but greater than 0.80 (Greckhameret al., 2018). Table 7 represents the specific findings, revealing considerable consistency.
Table 7: Analysis of Necessary Conditions (Outcome variable: BMC)
Conditions tested
Consistency
Coverage
Conditions tested
Consistency
Coverage
PRel
0.981382
0.940995
~PRel
0.798707
0.929154
PCom
0.990568
0.892059
~PCom
0.607923
0.951698
PAut
0.864938
0.990426
~PAut
0.972649
0.687655
PEnj
0.964083
0.940228
~PEnj
0.799515
0.871878
EWD
0.968655
0.924629
~EWD
0.741714
0.878551
VC
0.977757
0.887635
~VC
0.595123
0.891063
Source: Adopted by authors
Analysis of Sufficient Conditions
The first step in executing a sufficient condition analysis is creating the truth table (Rihoux & Ragin, 2009). A row represents every
causal condition combination in the truth table, with 2k rows (k is the number of conditions). The truth table for this study on
behavioral maintenance for organizational transformation was created using the fsQCA approach. Greckhamer et al. (2018) advise
setting the frequency cut-off at 3 (or higher) for the big sample size. The combined assessment has provided meaningful solutions
when consistency scores are more than 0.74 (Dul, 2016). Since we have 317 participants in our sample, the frequency threshold value
is set at 5, and any combinations with a lower frequency are disqualified from further analysis. Next, to improve the presentation of
the findings, we can transform the solutions from fsQCA output into a table that is easier to read (Table 8). Typically, the presence
of a condition is indicated with a black circle (●), the absence/negation with a crossed-out circle (⊗), and the "do not care" condition
with a blank space (Fiss, 2011). Table 8 also represents each solution's raw consistency, similar to the regression coefficient
measurement. In addition, coverage scoring for each solution and circumstance indicates the size of the effects in hypothesis testing
(Pappas, 2018). Finally, when evaluating the overall solution coverage, similar to the R-square value given in variable-based
Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
85
approaches (Woodside, 2013), it is possible to see whether the revealed configurations influence behavioral maintenance for
organizational change.
Table 8: fsQCA analysis (intermediate solution)
Model: BMC = f (PRel, PCom, PAut, PEnj, EWD, VC)
Configuration
Solution 1
Solution 2
Solution 3
Solution 4
PRel
●
●
●
⊗
PCom
●
●
●
●
PAut
●
⊗
●
PEnj
●
●
⊗
●
EWD
●
●
●
VC
●
●
⊗
⊗
Raw coverage
0.844796
0.921368
0.659446
0.586541
Unique coverage
0.0160227
0.0806507
0.0185353
0.0317162
Consistency
0.994714
0.97895
0.87726
0.99164
Solution coverage: 0.942416
Solution consistency: 0.97807
Source: Adopted by authors
Table 8 illustrates that no single predictors would provide superior performance rather than combining. The results of the fsQCA
demonstrate that four pathways related to behavioral maintenance for organizational change are possible. However, all the solutions
have high raw consistency (above 0.90) except solution 3 (0.87726), which has been identified as leading to high performance in
behavioral maintenance for organizational change. In particular, the findings demonstrated that the combination of perceived
relatedness, perceived competency, perceived autonomy, perceived enjoyment, and value congruence, except for excessive work
demands (solution 1), is more likely to achieve high performance than the other combinations with a consistency score of 0.994714.
Most 84.4% of employees have supported solution 1 (Raw coverage). According to Solution 2, all six predictors are equally
significant except perceived autonomy, which is supported by 92.1% of respondents with a consistency of 0.97895. Solution 3 has
comparatively low consistency (0.87726) with the full importance of perceived relatedness, perceived competency, and excessive
work demands, negating the importance of the other three predictors. Alternatively, the combination of less perceived relatedness ×
perceived competency perceived autonomy × perceived enjoyment × excessive work demands × low-value congruence (solution 4)
is equally expected to provide excellent performance since it has a consistency score of 0.99164 and is accepted by 58.6% of
employees. However, the above six solutions explain 94.2% of the likelihood of achieving high performance. In summary, the
findings suggest that having the same causative predictors leads to vital behavioral maintenance for organizational change, depending
on how the presence or absence of predictors is configured with other causal factors.
Conclusions
It is not easy to determine the successfulness of organizational change without evaluating employees’ behavioral maintenance. There
is evidence to suggest that the persistence of change is distressingly low in most organization development interventions. Most of
the efforts either made some initial improvements but failed to sustain them or made no improvements (Raetzell et al. (2018). Maier
et al. (2016) claimed that the persistence and impact of any organizational change intervention are determined by the extent to which
the associated behaviors persist within the organization after the adoption phase. Behavioral maintenance is believed to occur when
two or more people consistently act in a certain way, and their behavior becomes ingrained in the organization's daily operations.
However, the existing literature failed to address this critical issue for the success of the organizational change. This study tried to
fill the gap in the literature by conducting a study on behavioral maintenance for organizational change.
The results confirmed that the intrinsic form of motivational factors (perceived enjoyment, perceived competency, perceived
autonomy, and perceived relatedness) have significant positive impact on behavioral maintenance for organizational change. The
result suggests that the quality of individuals' motivation affects the extent to which individuals will engage in, and persist with,
behaviors.
While the result didn’t show that value congruence positively affects behavioral maintenance for organizational change. This result
is inconsistent with Yamoah (2014). According to Yamoah (2014), if value incongruence is not stopped in time, employees may
avoid the change from becoming part of the organization's fabric in the long term. This is because sometimes employees pursue
organizational values at the expense of individual objectives. Also, the negative effect of excessive work demands on behavioral
maintenance for organizational change doesn’t support by this study, which is inconsistent with previous studies. Research has shown
that high work demands, such as long hours or the pressure to work very hard or fast, may result in cognitive and emotional
exhaustion, such as difficulty concentrating, lack of motivation, or trouble staying on task (Ruggerio, 2021). There is a possible
reason for the insignificant findings of excessive work demands that pressure at work was insufficient to influence behavioral
maintenance negatively.
In particular, the findings demonstrated that the combination of perceived relatedness, perceived competency, perceived autonomy,
perceived enjoyment, and value congruence, except for excessive work demands, is more likely to achieve high performance than
Kidane & Xuefeng, International Journal of Research in Business & Social Science 11(9) (2022), 77-89
86
the other combinations with a high consistency. In summary, the findings suggest that having the same causative predictors leads to
vital behavioral maintenance for organizational change, depending on how the presence or absence of predictors is configured with
other causal factors.
Contributions and Limitations
A significant strength of this study was the methodology used. The majority of earlier research on organizational change have used
a single-stage data analysis technique, mainly the partial least square based structural equation modelling (PLS-SEM). A single-stage
PLS-SEM analysis just capture only the linear relationship between predictors and outcome variables, which may be inadequate to
anticipate real-world complicated decision-making processes. The use of fuzzy-set qualitative comparative analysis (fsQCA) as a
second-generation data analysis technique has been suggested as a possible solution to this constraint. The fsQCA, on the other hand,
has several challenges with respect to determining the “True Table” threshold value since there is no universally accepted rule for
using it. Different cut-off points might lead to various degrees of consistency in the findings.
Despite that, this research also had limitations. This study is quantitative in nature which could have benefited from a qualitative
examination that reinforces the development of the proposed model. Moreover, the results may have been influenced by aspects
specific to the culture of the country under consideration. Also, it is limited under one sector and industry. In addition, the conceptual
model did not consider different theories of regarding behavioral maintenance for organizational change. Other studies can integrate
several theories to fill this gap. Plus, using fsQCA might lead to various degrees of consistency in the findings since there is no
universally accepted rule for determining the “True Table” threshold value.
Implication for Practice and Future Research
In this study, the hypothesized framework provides a more comprehensive knowledge of employees' behavioral maintenance for
organizational change. This study has a significant theoretical contribution to future research since it was investigating the intrinsic
form of motivational factors (relatedness, competency and control) for examining behavioral maintenance for organizational change
using a framework of SDT and a mixed-analytical method fsQCA. While prior literature on organizational change has focused on
behavior change in organizational change planning and implementation, there has been a dearth of research on behavioral
maintenance for organizational change. Behavioral maintenance secures the new state of equilibrium that prevents movement back
toward the status quo. As a result of critically analyzing intrinsic forms of motivational factors, this research contributes to the existing
body of organizational change literature, opening up a new avenue for policymakers to consider when making human resource and
organizational change strategies. In summary, this study contributes to the gap between evaluating the void of findings (Miles, 2017)
and the methodological gap (Dominika Kwasnicka et al., 2016).
Acknowledgement
To all employees who participated in this research. All authors have read and agreed to the published version of the manuscript.
Author Contributions: Conceptualization, A.K., and Z.X.; methodology, A.K., and Z.X.; validation, Z.X.; formal analysis, A.K., and Z.X.;
investigation, A.K.; resources, A.K., and Z.X.; writing—original draft preparation, A.K.; writing—review and editing, A.K., and Z.X.
Funding: The author(s) received no financial support for this article's research, authorship, and publication.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly
available due to restrictions.
Conflicts of Interest: The authors declare no conflict of interest.
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