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Human Resource Management Review journal homepage: www.elsevier.com/locate/hrmr
An integrative literature review of employee engagement and innovative behavior: Revisiting the JD-R model Kibum Kwona, Taesung Kimb,
⁎
a
Department of Higher Education and Learning Technologies, Young Education North #104D, Texas A&M University-Commerce, Commerce, TX 75429, United States of America Department of Creative HRD & Institute of Social Sciences, Incheon National University, 119 Academi-ro, Yeonsu-gu, Incheon 22012, South Korea
b
ARTICLE INFO
ABSTRACT
Keywords: Employee engagement Innovative behavior Job resources Job demands The JD-R model Integrative literature review
The purpose of the current literature review is to (a) provide a comprehensive understanding of the relationship between employee engagement and innovative behavior through the lens of the JD-R model; (b) identify and revisit the guiding theories underpinning employee engagement studies; and (c) construct an integrated conceptual framework based on empirically validated factors and their relationships, along with relevant theories. An integrative literature review of 34 empirical studies indicates that employees perceive a mix of reasonably high demands and high resources to be ideal for their engagement, innovative behavior is a consequence of these delicate interactions, and engaged employees are more likely to behave innovatively by activating coping strategies to deal with challenges. Together, these findings suggest an integrated conceptual framework that refines the original JD-R model and that in doing so, better explicates the dynamics surrounding employee engagement and innovative behavior. Key implications for research and practice are provided.
1. Introduction Creativity and innovation, the hallmarks of contemporary business, are regarded as critical components of organizational success in today’s economy. Creativity provides the seeds for the development of innovative products, services, solutions, and processes (Woodman, Sawyer, & Griffin, 1993); innovation sprouts and ultimately blooms if an organization nourishes the seeds with care and support (Tierney & Farmer, 2002). In practice, this rhetoric is manifested in relentless attempts to realize novel ideas that may lead to innovative performance. Innovation is often depicted as happening in a flash of momentary inspiration, but the actual process is “messy, reiterative, and often [involves] two steps forward for one step backwards plus several side steps” (Anderson, Potočnik, & Zhou, 2014, p. 3). It takes intense cognitive, psychological, and physical exertions on the part of the individual, while requiring circumstantial conditions conducive to the undertaking. In fact, many attempts at innovation fail on account of both individual and organizational reasons, including the pressure of business-as-usual, skepticism, fatigue, and complicated and laborious logistics (Amabile, Barsade, Mueller, & Staw, 2005). Given this, it is imperative to examine the factors and dynamics affecting employees’ innovative behavior in organizations (Amabile et al., 2005; Anderson et al., 2014). Assuming that innovative behaviors stem not only from an individual’s natural traits but also from an individual’s job attitudes, scholars have begun to pay greater attention to the attitudinal factors that help induce innovative behavior. One of these factors is
⁎
Corresponding author. E-mail addresses:
[email protected] (K. Kwon),
[email protected] (T. Kim).
https://doi.org/10.1016/j.hrmr.2019.100704 Received 17 September 2018; Received in revised form 31 July 2019; Accepted 1 August 2019 1053-4822/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Kibum Kwon and Taesung Kim, Human Resource Management Review, https://doi.org/10.1016/j.hrmr.2019.100704
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employee engagement, which is “operationalized by the intensity and direction of cognitive, emotional, and behavioral energy” (Shuck et al., 2017, p. 2), because the three-fold energy is required for individuals to take on the difficult process of innovation. Scholars also recognize that the various individual and circumstantial factors that influence employee engagement have been understudied in the specific context of innovative behavior (e.g., Hakanen, Perhoniemi, & Toppinen-Tanner, 2008). Although there are some findings in this regard, they offer only a limited portrayal because they remain immersed in their respective research contexts without being integrated all across. Variables, their relationships, and underpinning theories are all informative on their own; however, they should be considered collectively to produce a more robust understanding. In this vein, an integrative review of research is undertaken to help conceptualize a comprehensive mechanism surrounding employee engagement and innovative behavior. As a scaffolding for the review, the job demands-resources (JD-R) model is used because it is regarded as being highly integrative of predictors and consequences of employee engagement and is referred to by numerous studies involving employee engagement (Bakker & Demerouti, 2007). The JD-R model presents dynamic interactions between diverse job demands and job/personal resources that influence employee engagement; it also considers employees’ wellbeing and job performances, for example, as a consequence of those interactions (Bakker, Demerouti, de Boer, & Schaufeli, 2003). Meanwhile, it is uncertain whether the JD-R model remains convincing when innovative behavior is incorporated as an outcome, given that innovative behavior is fairly distinct from conventional performances around which the model is built. There might be required revisions and/or expansions of the model with innovative behavior at play. Put together, the JD-R model is harnessed for an integration of knowledge assets and revisited for the formulation of a framework specific to the engagement-innovation context. In a nutshell, the purpose of the current research is to (a) provide a comprehensive understanding of the relationship between employee engagement and innovative behavior through the lens of the JD-R model; (b) identify and revisit the guiding theories underpinning employee engagement studies; and (c) construct an integrated conceptual framework based on empirically validated factors and their relationships, along with relevant theories. The research purpose is achieved through an integrative literature review of 34 empirical studies, after which implications for research and practice are offered. 2. Theoretical backgrounds This part contextualizes innovative behavior in the JD-R model by exploring the linkage between the components of job resources, job demands, employee engagement, and innovative behavior. 2.1. Employee engagement and the JD-R model Kahn (1990) coined the term personal engagement to capture a psychological state in which employees harness their full personal selves by investing physical, cognitive, and emotional energies into their work and the level of energies invested leads to substantially different outcomes. Schaufeli, Salanova, González-Romá, and Bakker (2002) elaborated on Khan’s idea with the concept engagement at work, and defined it as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” (p. 74). Used interchangeably, the terms employee/work engagement are conceptualized as an overarching construct comprising physical, cognitive, and emotional energies and manifested as a state of devoting all the energies towards work to make a difference (Mackay, Allen, & Landis, 2017). Bakker and Demerouti (2007) expanded upon the employee engagement research stream by proposing the concepts of job demands and job/personal resources as antecedents to employee engagement, better known as the JD-R model. Job resources refer to job-related attributes that positively influence an employee’s work achievement, physical and psychological well-being, and learning and growth; personal resources refer to an individual’s sense of his or her ability to successfully control and impact circumstances (Hobfoll, 2001). In contrast, job demands refer to job-related characteristics that require significant physical and psychological investment and if overwhelming, hinder performance outcomes (Hakanen & Roodt, 2010). According to the JD-R model featuring the dual process (i.e., an energy sapping process in which demands cause exhaustion vs. a positive motivational process in which resources foster engagement), decreasing job demands helps employees concentrate on their jobs and minimizes moments of unproductivity; increasing job/personal resources helps employees preserve energies and remain engaged. In addition to the linear relationships, there are buffering and coping mechamisms at play. For instance, if sufficient job resources are readily available, they can buffer the adverse effects of demands and thereby help secure high levels of engagement and subsequent positive outcomes (Bakker & Demerouti, 2007). Personal resources, such as self-efficacy, optimism, hope, and resilience, also function as psychological capital to help buffer burnout (Sweetman & Luthans, 2010). Besides, job demands might not be necessarily problematic unless excessive because engagement exists in an emotional ambivalence in which one would feel uncomfortable with demands yet driven to cope with them (Fong, 2006). That is, having controllable levels of challenge promotes work-related motivation, and successfully managing the challenge is likely to facilitate an enhanced sense of meaningfulness at work (Tims, Bakker, & Derks, 2012). In sum, employee engagement is an activated state of full selves to bring something different to work; job/personal resources help employees remain engaged, and at the same time, serve as a buffer against demands; job demands are generally regarded as a strain but may function as either a hindrance that constrains or a challenge that promotes desirable behaviors, depending on an employee’s capacity for coping. Therefore, maintaining an exquisite balance that goes beyond the resource-demand dichotomy is imperative to keep employees engaged, even in the face of demanding tasks such as innovation. 2
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2.2. Innovative behavior as a consequence Innovative behavior is defined as the intentional proposal and application of novel and improved ideas, processes, practices, and policies aimed at organizational effectiveness, business success, and long-term sustainability (Anderson et al., 2014; Janssen, 2000). Distinguished from creativity, which pinpoints the novelty and radicalness of ideas, innovative behavior encompasses an interindividual socio-psychological process that is concerned more about the execution and realization of ideas (Anderson et al., 2014; Rank, Pace, & Frese, 2004). Highlighting the intentional and practical characteristics of innovative behavior, Cardellino and Finch (2006) argued that innovative behavior often occurs over the course of planned change with certain goals in mind; and involves actions such as seeking out new ideas, championing new initiatives, and securing planning/funding for the ideas’ implementation. In the studies on employee engagement, innovative behavior is often discussed as part of job performance, which is generally broken down into two types—in-role and extra-role performance. In-role performance refers to formally prescribed behaviors and outcomes as part of an incumbent’s work requirements, while extra-role performance refers to discretionary behaviors that enrich an organization’s functioning but may not be formally rewarded or sanctioned by the organization (Christian, Garza, & Slaughter, 2011; Mäkinen, 2013). Some scholars (e.g., Gorgievski and Bakker, 2014; Karatepe & Olugbade, 2016) have considered innovative behavior as a type of in-role performance, while others (e.g., Chughtai & Buckley, 2011; Demerouti, Bakker, & Gevers, 2015; Eldor & Harpaz, 2016) have operationalized innovative behavior as constituting extra-role performance by pointing out that it is not a formally prescribed job requirement. However, recent studies using the JD-R model have reconsidered this classfication. They argue that innovative behavior is a type of performance distinguished from either in-role or extra-role performance because it goes beyond prescribed requirements and is no longer merely discretionary (e.g., Aryee, Walumbwa, Zhou, & Hartnell, 2012; Chughtai & Buckley, 2011; Rodríguez-Sánchez, Devloo, Rico, Salanova, & Anseel, 2017). Goering, Shimazu, Zhou, Wada, and Sakai (2017) went further to maintain that this taken-forgranted dichotomy of job performance may have misled employee engagement research. In fact, challenging the status quo and thinking outside the box might not be explicit in a job description, but are tacit norms required of employees in today’s workplace, making the boundaries between in- and extra-role behaviors weak at best (Schaufeli & Bakker, 2010). In essence, innovative behavior should be considered a significant yet distinctive type of performance that engaged employees are more likely to demonstrate, and by being distinctive, would be in a unique relationship with affecting factors. 2.3. Employee engagement and innovative behavior In research and practice, job attitudes are widely explored in relation to performance. Although some argue weak linkages between them (e.g., Judge, Thoresen, Bono, & Patton, 2001; Saks, 2008) and others critique a proliferation of similar concepts (Harter & Schmidt, 2008; Newman & Harrison, 2008), job attitudes remain in the focal interest of scholarly inquiry. In particular, employee engagement has attracted much attention since it is postulated as a stonger predictor of performance than other attitudinal constructs such as job satisfaction, job involvement, and organizational commitment (e.g., Rich, LePine, & Crawford, 2010). Furthermore, although some see employee engagement partially overlapping with or simply distinct from these attitudinal constructs (Hallberg & Schaufeli, 2006; Shuck, Ghosh, Zigarmi, & Nimon, 2013), it is broadly acknolwedged as a higher-order global concept that incorporates and activates diverse sets of enablers (Christian et al., 2011; Mackay et al., 2017). That is, employee engagement establishes a strong motivational basis of desirable behaviors (Tims et al., 2012), and engaged employees are believed to work better and smarter (Kim, Kolb, & Kim, 2013). In particular, due to the synergy of cognitive, emotional, and physical energies, employee engagement is expected to fuel innovative behavior (Hakanen, Schaufeli, & Ahola, 2008). The three-fold nature of engagement fits well with innovative behavior that represents a change-oriented iteration of idea generation, promotion, and realization with the purpose of achieving something different and/or unprecedented (Janssen, 2004). Idea generation occurs not only during the initial stage of brainstorming, but also in the ongoing cognitive process of solving problems and taking action. Idea promotion involves socio-psychological activities intended to identify potential allies such as colleagues, backers, and sponsors and to establish a coalition of advocates that can help actualize nascent ideas. Finally, idea realization refers to persistently developing prototypes, actualizing new products and services, and materializing new models to deliver differentiated values within and beyond the organization (Janssen, 2004). Shuck, Adelson, and Reio Jr (2017) highlighted cognitive engagement as the mental energy that is required for innovative behaviors. During idea generation, employees use cognitive flexibility and invest extra cognitive effort in deviating from systems and processes already in place in favor of better and different ones (Seligman & Csikszentmihalyi, 2000). That is, cognitive engagement mobilizes innovative behaviors by encouraging employees to revisit existing knowledge structures, broaden the scope of their cognition and perception, and attempt non-traditional tweaks and combinations of ideas (Fredrickson, 2001). During idea promotion, there might be stakeholders who adhere to familiar routines out of uncertainty and insecurity, who try to revert back to the status quo when faced with potential disadvantages, and who express skepticism and even cynicism to justify their longstanding value systems (Janssen, 2004). It is emotionally arduous to persuade stakeholders with varied interests to join new initiatives, and innovative attempts often fizzle out or fail due to explicit and implicit resistance. In other words, emotional engagement should help employees to feel confident in the purpose and meaningfulness of innovative efforts, to communicate their optimism to others, and to help fuel proactive behaviors across the organization (Demerouti & Cropanzano, 2010; Shuck et al., 2017). Another reason for failure in innovation is employees’ fatigue and burnout because of physically stressful demands, as well as a lack of sufficient time, support, and job resources (Christian et al., 2011). As stated, the process of innovation is a complex and strenuous one that takes considerable time and effort before seeing results (Anderson et al., 2014). Further complicating this is that 3
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employees are bombarded by innovations taking place one after another, which often leaves employees with innovation fatigue along the way. Those left exhausted lose their intrinsic motivation to propose creative ideas and subsequently become disengaged from new initiatives and even others. Therefore, physical engagement is another determinant in realizing ideas and maintaining innovative behaviors (Shuck et al., 2017). In sum, it seems plausible that cognitive, emotional, and physical engagement should be activated at every stage of innovation since the cohesive dimensions have roles to play, separately and collectively, and that those engaged will better manage challenges even in uncertainty and go the extra mile to achieve something new and better (Bakker, 2011). These deductions and surrounding mechanisms should become clearer with an extensive examination of research assets on employee engagement and innovative behavior. 3. Methodology The current study adopted the method used for the integrative literature review by Callahan (2010), presenting: (a) where the literature was found (databases and search engines); (b) when the search was conducted; (c) who conducted the search; (d) how the literature was found (keyword combinations); (e) the number of articles that appeared as a result of each combination of keywords, as well as the final count of included articles; and (f) why some articles were chosen for inclusion over others. The literature search took place in April 2018 using various databases (e.g., PsycARTICLES, PsycINFO, ERIC, ProQuest Education Journals, ABI/INFORM Complete) with the keyword combinations of “work engagement,” “employee engagement,” “job engagement,” “personal engagement,” “psychological engagement,” or “organizational engagement” and “creativity,” “creative,” “innovation,” or “innovative.” Cross-check and additional search for papers not found in the databases was conducted through Google Scholar. The initial search yielded 147 matching articles. Then, the articles were pared down using the staged-review process (Torraco, 2005) in which only the most relevant ones were chosen for further consideration. The keywords and abstract of each article were given an initial review, and the full text was examined when necessary. The relevance of a given article was determined based on whether the article researched the relationship in earnest, whether it was empirical, and whether it provided an in-depth discussion of the relationship instead of merely mentioning it in service to other purposes. Ultimately, 34 empirical studies were selected for further review. In following the guidelines set forth by Petticrew and Roberts (2006), a table to summarize the various quantitative studies was generated, providing a comprehensive snapshot of all the studies included in the review (see Table 1). The table contains information on the articles’ authors and years of publication, theoretical frameworks, major variables (i.e., antecedents, mediators/moderators, and dependent variables), research contexts, participants, data types, instruments, and data analysis methods. 4. Mechanisms surrounding employee engagement and innovative behavior What follows represents scholars’ aggregate knowledge of the relationship between employee engagement and innovative behavior in light of the JD-R model and other relevant theories. 4.1. Job resources Job resources generally contribute to better results. Resources can be classified as: (a) organization-level (organizational practices and culture), (b) team-level (leadership styles and interpersonal relationships at work), or (c) individual-level (personal resources and job characteristics). 4.1.1. Organization-level A theoretical perspective that explains organization-level antecedents and related behaviors is social exchange theory (SET), which suggests that “employees are more likely to exchange their engagement for resources and benefits provided by their organization” (Saks, 2006, p. 603). SET assumes that if an organization offers employees economic and socio-emotional resources, the employees feel obligated to respond with a corresponding level of engagement. Conversely, they might be disengaged if their organization fails to deliver on its promise by foregoing adequate compensation, promotions, job security, training opportunities, and other desirable incentives (Cropanzano & Mitchell, 2005). SET also implies that, when job demands gradually increase with more expectations for fewer resources, employees will tend to squeeze something out of current routines instead of experimenting with risky alternatives. They will play it safe unless convinced of the organization’s reciprocity—that they will be entitled to a matching benefit, whether or not already specified, for venturing to do something differently. Therefore, the presence of mutuality is imperative for effort-reward fairness (Janssen, 2000), which allows for a psychological contract in which employees trust in the receipt of future returns and the organization recognizes its obligations. Building on this theoretical foundation, several studies have looked into contextual antecedents that take the form of organizational policies. Maden (2015) proposed empowerment, competence development, information sharing, recognition, and fair reward as high-involvement HR practices that provide intrinsic and extrinsic stimuli for employee engagement and innovative behavior. Karatepe and Olugbade (2016) pinpointed the importance of selective staffing, job security, and career opportunities, and Bakker and Xanthopoulou (2013) viewed opportunities for professional development as a type of situational signal to employees that they are valued, which mobilizes them. Scholars have argued that organizations characterized by continuous learning, inquiry and dialogue, and empowerment toward a 4
Author(s)
Chen and Huang (2016)
Eldor and Harpaz (2016)
Toyama and Mauno (2017)
Bakker and Xanthopoulou (2013)
Kim and Park (2017)
Agarwal (2014a)
Chen (2016)
Stock et al. (2017)
No
1
2
3
4
5
5
6
7
8
COR
SET, B&B
SET
B&B, COR
B&B, COR
Kahn's Personal Engagement Theory
Theory
Table 1 An overview of the included studies.
- Leader-member exchange - Perceived organizational support - Personal resources (creative selfefficacy) - Personality (openness to experience) - Job satisfaction
- Job resources (autonomy, social support, performance feedback, professional development) - Personal resources (self-efficacy, resiliency) - Procedural justice
- Emotional intelligence - Social support
- Charismatic leadership style - Colleague support - Self-esteem - Perceived learning climate
Antecedents
- Customer aggression
- Employee engagement
- Customer loyalty
- Employee engagement
- Knowledge sharing - Innovative behavior - Innovative behavior
- Creativity - Charismatic leadership
- Employee engagement
- Employee engagement
- Creativity
- Innovative behavior - Work-family conflicts - Extra-role performance (proactivity, knowledge sharing, creativity, adaptivity)
Dependent variables
- Employee engagement
- Employees engagement - Job satisfaction - Job involvement
- Personal engagement
Mediators/ moderators
Germany
USA
India
South Korea
Netherland
Japan
Israel
China
Country
Frontline service employees in
A knowledge process outsourcing firm and an IT company Faculty members from a science and technology institute
Employees
Female school principals and teachers
Employees and their supervisors in communications/ technology, service, financial, and municipal sectors Eldercare nurses
R&D employees in IT businesses
Sample
Individual
Individual
UWES-9
Job engagement scale (Rich et al., 2010)
Individual
UWES-9
Individual
Organization
UWES-9
UWES-9
Individual
Hierarchical multiple regression Multilevel
SEM
SEM
Moderated mediation analysis SEM
Multilevel regression
HLM
Team
Organization
Method
Unit of analysis
UWES-17
UWES-9
Personal engagement (physical, emotional, cognitive)
Measures
Yes
Yes
Yes
(continued on next page)
Other-rated (customer)
Self-rated
Self-rated
Self-rated
Other-rated (teacher)
Yes
Yes
Self-rated
Other-rated (supervisor)
Self-rated
Source of outcome measure
Yes
Yes
No (Times 1, 2, 3)
Crosssectional
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Demerouti et al. (2015)
de Spiegelaere, van Gyes, de Witte, and van Hootegem (2015) Park, Song, Yoon, and Kim (2014)
Chughtai (2013)
11
12
14
13
Bae et al. (2013)
Choi et al. (2015).
9
10
Author(s)
No
B&B
- Affective supervisory commitment
- Employee engagement
- Employee engagement
- Autonomy - Job insecurity - Creativity as a job requirement
Job demandcontrol model (strain and learning hypotheses)
- Learning organization
- Time pressure
- Job crafting
Absorptive capacity theory, SDT, B&B
- Employee engagement - Knowledge creation - Employee engagement - Flourishing
- Underemployment - Colleague support - Supervisor support - Innovative behavior - Customeroriented behavior - Customer delight - Customer satisfaction - Employee creativity - Affective organizational commitment
Mediators/ moderators
- Transformational leadership
- Inclusive leadership
- Emotional employeeengagement
Antecedents
Theory of planned behavior and interaction pattern theory COR
SET
Theory
Table 1 (continued)
- Innovative behavior
- Innovative behavior
- Innovative behavior - Employee engagement
- Creativity - Contextual performance (citizenship behavior)
- Teachers' creativity
- Employee engagement
Dependent variables
Ireland
South Korea
Belgium
Netherland
South Korea
Vietnam
Country
Employees in various industries (manufacturing, construction, IT, and electronics) Research scientists in scientific
Employees in the service industries (banking, telecommunication, and hospitality) Career and technical education (CTE) school teachers Employees in various sectors (public, trade, industry, business services, and healthcare) Employees in various industries
business-toconsumer (B2C) industries (retail, crafts and hair salons, hospitality services and tourism, health services, other services)
Sample
Individual
Individual
UWES-9
UWES-17
Organization
Individual
Individual
Individual
Unit of analysis
UWES-9
UWES-9
UWES-9
UWES-9
Measures
SEM
SEM
Multilevel regression
SEM
SEM
SEM
regression
Method
Yes
Yes
Yes
Yes
Yes
Yes
Self-rated
Self-rated
Self-rated
Other-rated (supervisor)
Self-rated
Self-rated
Source of outcome measure
(continued on next page)
Crosssectional
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Author(s)
Maden (2015)
Agarwal (2014b)
Agarwal, Datta, BlakeBeard and Bhargava (2012)
Eldor and Harpaz (2016)
Bhatnagar (2012)
No
15
16
17
18
19
B&B, COR
SET
SET, equity theory
B&B, SET, COR
Theory
Table 1 (continued)
7
- Psychological empowerment (meaning, competence, selfdetermination, impact)
- Employee engagement
- High-involvement HR practices (empowerment, competence development, information sharing, recognition, fair reward) - Psychological contract fulfillment - Procedural justice - Interactional justice - Leader-member exchange
Antecedents
- Employee engagement
- Organizational politics
- Employee engagement
- Trust - Employee engagement
- Employee engagement - Learning-goal orientation
Mediators/ moderators
- Knowledge sharing - Creativity - Proactivity - Adaptability - Innovation - Turnover intention
- Innovative behavior - Turnover intention
- Innovative behavior
- Feedback seeking for selfimprovement - Error reporting - Employee proactivity (individual innovation, feedback inquiry)
Dependent variables
India
Israel
India
India
Turkey
Country
R&D employees in pharmaceutical, heavy engineering, IT, electronics and aeronautics engineering industries
Employees in investment bank, business process outsourcing firm, knowledge process outsourcing firm, IT company, telecommunication company, retail company Employees and their supervisors in a high-tech company
Employees in manufacturing and pharmaceutical companies
Employees in financial services, education, and telecommunication and technology
research centers linked to an university
Sample
Individual
Individual
UWES-17
Individual
UWES-9
UWES-9
Individual
Individual
Unit of analysis
UWES-9
UWES-9
Measures
SEM
SEM
SEM
SEM
SEM
Method
Yes
Yes
Yes
Yes
Yes
Self-rated
Other-rated (supervisor)
Self-rated
Self-rated
Self-rated
Source of outcome measure
(continued on next page)
Crosssectional
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Koch, Binnewies, and Dormann (2015)
de Spiegelaere, van Gyes, and van Hootegem (2016)
de Spiegelaere, van Gyes, de Witte, Niesen, and van Hootegem (2014) Hakanen, Perhoniemi, and ToppinenTanner (2008) Ahmetoglu, Harding, Akhtar, and ChamorroPremuzic (2015) Chang, Hsu, Liou, and Tsai (2013)
20
21
22
8
25
24
23
Author(s)
No
COR, B&B
Theory
Table 1 (continued)
- Psychological contracts (transactional, relational)
- Job resources (craftsmanship, pride in the profession, direct and long-term results) - Perfectionism - Entrepreneurial tendency and ability
- Autonomy - Job insecurity
- Autonomy (work method, work scheduling, work time, locational)
- Employee engagement
Antecedents
- Work unit innovativeness
- Creative achievement - Entrepreneurial achievement - Work meaningfulness - Responsibility for the job - Employee engagement - Innovative behavior
- Employee engagement
- Organizational resources (supervisory performance feedback, coaching) - Social resources (colleague support)
- Innovative behavior
- Innovation - School projects - External communication - Internal communication - Innovative behavior
Dependent variables
- Employee engagement - Personal initiative
- Employee engagement
- Employee engagement
- Creativity
Mediators/ moderators
Taiwan
Finland
Belgium
Belgium
Germany
Country
R&D engineers and their supervisors in high-tech companies
Employees (fulltime, part-time, self-employed)
Employees in banking, retail, hotels and restaurants, chemical industry, and the sector of social work Employees in banking, retail, hotels and restaurants, chemical industry, and the sector of social work Dentists
School principals and teachers
Sample
Individual
Dyads
UWES-17
Individual
Individual
Individual
Organization
Unit of analysis
UWES-9
UWES-17
UWES-9
UWES-9
UWES-9
Measures
Moderated path analysis
SEM
SEM
SEM
SEM
Multilevel SEM
Method
Other-rated (supervisor)
Yes
(continued on next page)
Self-rated
Other-rated (secondary data)
Self-rated
Self-rated
Other-rated (teacher)
Source of outcome measure
Yes
No (Times 1, 2)
Yes
Yes
Yes
Crosssectional
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Aryee et al. (2012)
Chughtai and Buckley (2011)
31
Gomes et al. (2015)
28
30
Song et al. (2014)
27
Karatepe and Olugbade (2016)
Gorgievski and Bakker (2014)
26
29
Author(s)
No
9
B&B, SET
SET
SET
SCT, B&B
SDT
B&B
Theory
Table 1 (continued)
- Trust in supervisor - Trust propensity
- Transformational leadership
- High-performance work practices (selective staffing, job security, teamwork, career opportunity)
- Self-leadership
- Innovation support
- Employee engagement - Workaholism
Antecedents
- Employee engagement - Learning goal orientation
- Work meaningfulness - Responsibility for the job - Employee engagement - Leader-member exchange
- Employee engagement
- Employee engagement - Knowledge sharing - Employee engagement
- Positive affect - Negative affect
Mediators/ moderators
- In-role job performance - Innovative behavior
- Absence intentions - Service recovery performance - Creative performance - Innovative behavior - Task performance
- Individual innovation
- Knowledge creation
- Innovative behavior - Business growth - Subjective business success
Dependent variables
Research scientists in scientific research centers linked to an university
Employees in a large telecommunication company
China
Ireland
Frontline employees and their supervisors in international chain hotel
Doctors and nurses
Entrepreneurs in various industries (financial services, consumer services, software and computer services, transportation and communication) High school teachers
Sample
Nigeria
Portugal
South Korea
Spain
Country
Individual
Individual
UWES-9
Dyads
Individual
Individual
Individual
Unit of analysis
UWES-17
UWES-9
UWES-9
UWES-9
UWES-9
Measures
SEM and hierarchical multiple regression SEM
Multiple regression SEM
SEM
SEM
Method
(continued on next page)
Self-rated
Other-rated (supervisor)
Yes
Yes
Other-rated (supervisor)
Self-rated
Self-rated
Self-rated
Source of outcome measure
No (Times 1, 2, 3)
Yes
Yes
Yes
Crosssectional
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RodríguezSánchez et al. (2017)
Orth and Volmer (2017)
de Spiegelaere et al. (2017)
32
33
34
B&B
COR
Theory
- Job resources (job autonomy, organizing tasks, information provision, task completeness, contact opportunities) - Job demands (complexity, time pressure, emotional pressure, job insecurity, job content insecurity)
- Daily job autonomy - Daily employee engagement
- Team cohesion
Antecedents
- Creative selfefficacy
- Collective task engagement
Mediators/ moderators
- Employee engagement - Innovative behavior
- Daily innovative behavior
- Perceived team performance - Task output creativity
Dependent variables
Belgium
Germany
Spain
Country
Employees in electricity sector
University students, fulltime workers, and unemployed people Employees in a broad variety of sectors
Sample
UWES-9
UWES-17
UWES-9
Measures
Individual
Individual
Team
Unit of analysis
LPA
HLM
SEM
Method
No (daily diary data at least three measurements) Yes
No (Times 1, 2, 3)
Crosssectional
Self-rated
Self-rated
Other-rated (external coders)
Source of outcome measure
Note. B&B = broaden and build theory; SET = social exchange theory; COR = conservation of resources theory; SCT = social cognitive theory; SDT = self-determination theory; HLM = hierarchical linear model; SEM = structural equation modeling; LPA = latent profile analysis; UWES = Utrecht work engagement scale.
Author(s)
No
Table 1 (continued)
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collective vision support employee engagement at work (Bakker & Demerouti, 2007). Indeed, an organizational climate conducive to learning positively influences employees’ proactivity, knowledge sharing, creativity, adaptivity, and innovative behavior (Eldor & Harpaz, 2016; Park, Song, Yoon, & Kim, 2014). Organizational justice is another contextual antecedent to employees’ engagement and their involvement in innovation. Kim and Park (2017) argued that procedural justice promotes knowledge sharing behaviors since employees’ discretionary work behaviors are encouraged in the fair organizational atmosphere; Agarwal (2014b) examined procedural justice, interactional justice, and the psychological contract to highlight that innovative behaviors require psychological safety that is built on the predictability and consistency of the organization. 4.1.2. Team-level Conservation of resources (COR) theory serves to explain team-level antecedents by suggesting that acquiring and accumulating resources is imperative for employees to sustain engagement and generate innovative behavior (Hobfoll, 2001; Leiter & Maslach, 2010). COR suggests that employees take advantage of resources to deal with demanding work assignments and that social support from supervisors and colleagues within reach act as resource caravans to help with coping, adaptation, and improvisation. Perceived social resources make employees feel psychologically safe, knowing that trial-and-error is allowed and even beneficial for their organizations as well as themselves (Edmondson, 1999). Amabile, Conti, Coon, Lazenby, and Herron (1996) elaborated on this understanding of social resources by suggesting that supervisory encouragement and work group support are major team-level stimulants of creativity. Creativity and subsequent follow-up behaviors are spurred when resources are well-conserved, but they are threatened when resources have yet to be secured or have already been dried up. Given these conceptual underpinnings, leadership styles have been explored as antecedents to employee engagement and innovative behavior. For example, leadership at the team level has been proven to be a potential resource for innovative behavior. In the leader-centered perspective, supervisors lead employees by forming a shared mental model and facilitating collective moves, including innovative behaviors (Mehra, Smith, Dixon, & Robertson, 2006). Meanwhile, transformational leadership is positively related to the generation of creative ideas and innovative work behaviors (Aryee et al., 2012; Bae, Song, Park, & Kim, 2013), and charismatic leadership mobilizes innovative behavior through the mediation of employee engagement (Chen & Huang, 2016). Employee-centered leadership is also discussed as a resource that encourages employees to make a difference with what they do. Leaders with this perspective help employees to take the lead and support their proactive behaviors (Hannah & Lester, 2009). Choi, Tran, and Park (2015) argued that inclusive leadership is positively related to employee engagement and creativity, as it encourages openness to employee input and helps with solving problems that arise during attempts at innovation. The quality of an employee’s interpersonal relationships at work serves as another antecedent to engagement and subsequent innovative behavior. Chughtai and Buckley (2011) found that if employees trust their supervisors, seeing them as capable, honest, and credible, they will engage more strongly in their job and act innovatively. Agarwal (2014a) found that innovative behavior is influenced more strongly by relationships with close supervisors than it is by distal organizational support, and that it is moderated significantly by leader-member exchange. Chen and Huang (2016) and Toyama and Mauno (2017) found that colleague support inspires employees to take risks without fearing the consequences of failing; Rodríguez-Sánchez et al. (2017) found a reciprocal process wherein team cohesion results in collective task engagement and creative team performance which, in turn, feeds back to future team cohesion. 4.1.3. Individual-level Individual-level antecedents are classified along two axes, personal resources and job characteristics, each of which has been discussed from different perspectives. Broaden-and-build (B&B) theory provides a conceptual framework by suggesting that positive emotions broaden employees’ momentary thought-action repertoire and build their lasting personal resources that function as reserves to address future stressors (Fredrickson, 2001). Unlike conventional performances, innovative behaviors are manifested in employees choosing non-traditional approaches, taking risks, and pushing back against stereotypes. Employees with positivity, such as self-efficacy, self-esteem, and resiliency, are more confident in making such choices. Chen and Huang (2016) found that self-esteem is a key driver of psychological availability for engagement and innovative behavior and that employees with higher levels of self-esteem appraise job demands with confidence that they can meet the requirements. Gomes, Curral, and Caetano (2015) reported that self-leadership provides cognitive strategies, such as self-analysis, improved belief systems, mental imagery of successful outcomes, and positive self-talk, that propel positive attitudes towards innovation. Chen (2016) agreed that those with creative self-efficacy are confident in taking the initiative and able to maintain consistent levels of efficacy during the implementation of ideas even in the face of obstacles. Self-determination theory (SDT) is used to explain job characteristics, the other axis of the individual-level antecedents to engagement and innovation. SDT maintains that self-determination takes place when an employee perceives a sense of choice of, autonomy in, and control over his/her work without any external psychological burden (Deci & Ryan, 2008). Employees are more determined to engage in enterprising behaviors if they have autonomous job designs and substantial ownership of their daily work practices (Amabile et al., 1996). Autonomy, a combination of an employee’s authority to make decisions on task distribution, work pace, and collaborators, as well as the breadth of skills used on the job (Karasek, 1979), is a job characteristic often considered in discussions of employee engagement and innovative behavior. Spiegelaere, Gyes, and Hootegem (2016) found that work method autonomy, or the exercise of discretion in how to perform a job, is a significant predictor of employee engagement and innovative behavior. Demerouti et al. (2015) showed that the acts of seeking resources and reducing demands are positively associated with contextual performance and 11
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creativity via work engagement; moreover, job crafting helps employees find resources, seek challenges, and reduce demands to strike a balance between job demands and resources (Tims et al., 2012). 4.2. Job demands Job demands generally come with strain, often making them a hindrance. Yet job demands may also be surmountable challenges. The exact tenor of job demands is determined by the demands themselves and other factors. 4.2.1. Hindrance vs. challenge Job demands have been empirically studied far less frequently than job resources, particularly in relation to innovative behaviors. Nonetheless, there are various job demands in the workplace that affect an employee’s engagement and subsequent behavior. These demands include administrative frustration, resource inadequacy, role conflict, excessive responsibilities, and time urgency, to name a few (LePine, Podsakoff, & LePine, 2005). Although each of these job demands is taxing, they do not have the same level of influence. Podsakoff, LePine, and LePine (2007) argued that job demands are two-dimensional, functioning as both hindrance and challenge. They argued that some demands are perceived as thwarting goal attainment, occupational health, and work-life balance, while others are viewed as containing the potential for enhanced expertise, career advancement, and future growth. Crawford, LePine, and Rich et al. (2010) found in their meta-analytic study that challenge demands are positively related to employee engagement, while hindrance demands are negatively related. That is, hindrance demands bring about emotional distress, while challenge demands can activate a positive drive (van Woerkom, Bakker, & Nishii, 2016). 4.2.2. Hindrance demands de Spiegelaere et al. (2014) examined job insecurity as a hindrance to employee engagement. When there is job insecurity in the workplace, employees feel less obliged to take on work-related problems involving significant changes to the scope of their prescribed jobs (Greenhalgh & Rosenblatt, 1984). Given that innovation is an intensive, long-term endeavor, employees facing job insecurity are less likely to engage in innovative behavior. 4.2.3. Challenge demands On the other hand, when faced with demanding tasks or high-stakes situations, employees might recognize these moments as genuine opportunities and become willing to change their conventional ways and attempt different approaches and determined actions (Demerouti, 2014). de Spiegelaere et al. (2015) found that in an autonomous job condition, time pressure can positively affect innovative behavior as a challenge demand. Eldor (2017) added that organizational politics can be harnessed for creativity, proactivity, and professional achievement. 4.3. Buffering and coping Some demands are regarded as cumbersome and irksome, while others may work in an advantageous way. Indeed, potential adverse effects of job demands can be toned down and even transformed into positive energies depending on how an individual copes with the demands, as well as the buffering effect of resources that exist. As mentioned, job/personal resources can function as a buffering mechanism that attenuates job demands’ negative effects and thereby helps with achieving desired objectives. For example, a supervisor’s active interaction with employees may decrease the negative impact of the employees’ job demands and stimulate improved in-role and extra-role performance because the supervisor’s support and feedback works as a buffer against emotional and physical strains along the way (Bakker, Hakanen, Demerouti, & Xanthopoulou, 2007). Empowering employees to design their work would facilitate a better employee-job fit, prompting employees to reinvest resources into their job and in turn become more immune to stressors (Yu, 2009). In addition, people use various coping strategies to deal with job demands. Coping is defined as “constantly changing cognitive and behavioral efforts designed to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of the person” (Lazarus & Folkman, 1984, p. 141). Coping strategies, whether intra-individual or inter-individual, can be problem-focused, addressing stressors using various problem-solving approaches; they can also be emotion-focused, reducing emotional strains by leaning on others (Catalano, Chan, Wilson, Chiu, & Muller, 2011). Instead of avoiding and resisting job demands, engaged employees can more successfully cope by adapting to changes in task goals, resource allocations, and interpersonal communications (Janssen, 2000); demonstrating their proactivity (Eldor, 2017; Maden, 2015); and sharing knowledge and feedback with others (Wang & Noe, 2010). By mobilizing these coping strategies, they can solve complicated problems, see challenges as an authentic growth opportunity, and actualize change and innovation to meet loftier goals (Chughtai, 2013; Eldor & Harpaz, 2016). Adopting a learning goal orientation is another coping strategy that induces innovative behaviors (Chughtai & Buckley, 2011). While those who have a performance orientation are geared towards external evaluations (e.g., rewards and recognition) in the face of challenging tasks, those who have a learning orientation invest substantial efforts in acquiring new knowledge, seeking feedback from others, and experimenting with innovative approaches (Hirst, van Knippenberg, & Zhou, 2009). The findings regarding these aspects of employee engagement and innovative behavior are presented in Fig. 1. 12
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Fig. 1. Preliminary conceptual model: Overview of the relationship between job resources, job demands, employee engagement, coping, and innovative behavior. Note. Italicized job resources indicate the selected examples as the most frequently or critically researched variables.
5. Discussion This literature review examined 34 empirical studies at the intersection of employee engagement and innovative behavior. The studies suggest that job resources exist at multiple levels depending on employees’ personal characteristics and situational contexts. Job demands also come from different sources and function either negatively or positively. Job resources exercise buffering effects to alleviate stress from demands; coping strategies individuals employ help process demands and potentially turn them into a source of greater accomplishments. The theories applied in the reviewed studies, including SET, COR, B&B, and SDT, combine to provide a fuller understanding of job demands and resources. In particular, multi-layered job resources and their cross interactions were found to be consistent with SET’s assumption that individual-level resources are activated with the appropriate provision of organization- and team-level resources, boosting engagement based on reciprocity (Saks, 2006). COR explains that sufficient resources and perceived social support allow employees to avoid depletion of resources and buffer demands for continued engagement (Hobfoll, 2001); meanwhile, B&B emphasizes how important an individual’s positivity is in reinforcing personal resources to take on challenges and conduct innovative experiments (Fredrickson, 2001). Finally, SDT accounts for an individual’s proactive and self-directed quality and job characteristics that help mobilize engagement and the subsequent behaviors (Bono & Judge, 2003; Deci & Ryan, 2008). These findings indicate that, unless job demands are far beyond the scope of handling, employees may perceive them as surmountable obstacles and consider a mix of reasonably high demands and high resources (or even high demands and low resources) to be ideal, while seeing low demands and high resources as boring (Eldor, 2017). That is, job demands and resources are not entirely adversarial but are instead interwoven in their effect on engagement (de Spiegelaere et al., 2017), and innovative behavior is a consequence of these delicate interactions (Shalley, Gilson, & Blum, 2009; Woodman et al., 1993). It was also found that engaged employees are more likely to behave innovatively by activating coping strategies to deal with challenges, indicating that an employee’s engagement and coping capacity work together in facilitating innovative behavior.
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Social Exchange
Broaden & Build
Coping
Hindrance Challenge Demands Demands Buffering
Innovative Behavior
Employee Engagement
Business Results
Job Resources
Conservation of Resources
Self Determination Fig. 2. The JD-R model refined.
5.1. Theoretical contribution Based on the review, the current research offers an integrated conceptual framework, which is a refinement of the original JD-R model (Bakker & Demerouti, 2007; Bakker & Leiter, 2010), to better explicate the dynamics surrounding employee engagement and innovative behavior (see Fig. 2). The framework displays relevant theories in the periphery to illustrate the operant psychological forces in the minds of employees and to provide a motivational context for the relationships between the variables. The proposed framework articulates hindrance and challenge demands, alluding to their distinct role; buffering as an interaction effect of resources and demands; and coping that mediates the relationship between engagement and innovative behavior. These variables and their relationships have not secured their places in the JD-R model, and as a result, have been left out of further explorations. However, considering the roles they play in relation to innovative behavior, they deserve to be positioned explicitly in the framework for conceptual and empirical robustness. Furthermore, by putting together a whole spectrum of dynamics and shedding light on their underexplored roles, the proposed framework fine-tunes the dual process of demands and resources linked directly with either burnout or engagement. The concept of engagement has evolved as an antithesis of burnout in the linear dual process to some degree, and many studies have confirmed that more resources enhance engagement and subsequently job performance, while demands work to the contrary. However, the framework herein demonstrates the possibility of dynamic interplay between demands and resources, particularly where innovative behavior is concerned as a consequence. In fact, the manifestation of dynamic interplay is a reflection of the progress made in creativity and innovation research. For example, the tension between resources and hindrance/challenge demands corresponds to the claim that emotional ambivalence, composed of high levels of simultaneous positive and negative emotions, triggers innovative behavior (Anderson et al., 2014; Davis, 2009; Fong, 2006); buffering and coping mechanisms share in common with the dual-tuning model that positivity accelerates flexibility in cognition and negativity sustains perseverance (George & Zhou, 2007). Therefore, the proposed framework incorporates the interactions between conflicting conditions that affect employees’ psychological state and, in turn, their proactive mental function of coping, all of which should be well coordinated to fuel innovative behavior. Above all, unlike the original JD-R model involving various in- and extra-role performances, the proposed framework focuses exclusively on a sole criterion, innovative behavior, to suggest pertinent implications for research and practice. 5.2. Implications for research The proposed framework will serve as a guide to designing empirical studies on engagement-innovation dynamics. Its integrative nature will help generate solid research questions and hypotheses from a much broader perspective. For example, given that there is an interaction between demands and resources, the combination of the two and its distinctive effects on employees’ motivation and subsequent behavior can be explored (de Spiegelaere, Ramioul, & van Gyes, 2017). Specifically, analyzing different combinations of high/low demands and high/low resources and/or classifying participants into groups according to the resultant outcomes would help determine which combinations are most desirable and/or which groups are most engaged and innovative. Given that the concepts of buffering and coping have been shown to be moderating and/or mediating factors in the framework, their complex roles need to be examined further in diverse cultural and industrial settings as well. Another research opportunity can be found with components in the framework. For example, although the JD-R model involves both job demands and resources, the research community has paid uneven attention to various job/personal resources over demands (Lee, Rocco, & Shuck, 2019; Schaufeli & Taris, 2014). However, job demands are as multidimensional; they can be physical (e.g., 14
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workload, time pressure, insufficient investment), psychosocial (e.g., distrust, abusive leadership), or environmental (e.g., faulty systems, hierarchical structures, conventional atmosphere); and they exist at different levels (e.g., organization, team, individual; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). This calls for further research into a variety of demand types and by extension, into the psychological/circumstancial mechanism of hindering and challenging. The component of innovative behavior also needs to be researched to determine whether it is a unique category separate from in-role and extra-role performance. As reported earlier, some studies have considered innovative behavior as in-role performance, while others have viewed it differently. Statistical approaches (e.g., factor analysis, convergent and discriminant validity analysis, and path analysis) to determining the incremental validity of employee engagement in the prediction of in-role performance, extra-role performance, and innovative behavior, as well as a conceptual comparison of the three constructs, could help clarify innovative behavior’s identity. In addition, even though employee engagement is regarded as a higher-order global construct (Christian et al., 2011; Mackay et al., 2017), such attitudinal constructs as job satisfaction, organizational commitment, and job involvement could also be examined conjointly with engagement. This extension would reveal whether and to what extent these differently characterized attitudes explain incremental variance in innovative behavior. A further extension might involve a look into whether these attitudes relate to one another and, if at all, how they can be best configured to amplify an innovative pulse among employees (e.g., does one do it all without the others?, are some necessary, but not all?, does a specific grouping provide the most parsimonious prediction?). These extensions would help test the discriminant validity of the attitudes, tease them out for innovation optimization, and render a structural model with strong explanatory power. Considering that the ultimate goal is to maximize innovative behavior, further research in this area will benefit organizations and the research community as well. 5.3. Implications for practice The ever-shifting nature of today’s workplace requires employees to manage new expectations and keep innovating. This situation seems to fuel the innovation maximization fallacy that “all creativity and innovation is good; and the more, the better” (Anderson et al., 2014, p. 24). However, the perpetual drive for innovation without adequate resources inevitably causes employees to burn out and fail to establish a sustainable formula for innovation. They will experience difficulties in coping with high job demands when “there are limits on the pool of energy and resources available” (Macey & Schneider, 2008, p. 25). Furthermore, even if an employee generates fascinating ideas, innovation will not happen unless the organization is prepared to nourish the ideas with careful incubating or rapid prototyping (Tierney & Farmer, 2002). All these suggest the importance of demand-resource equilibrium in aligning an organization’s push for innovation with its employees’ capacity and behavior. In a similar vein, the behavior engineering model (Gilbert, 1978) suggests that the first step towards driving innovative behavior should be to ensure proper circumstancial arrangements, such as setting the tone for initiatives, providing tools and support systems, and putting incentives in place, rather than relying on employees’ motivation alone. Of course, when designing incentives, social and moral aspects, as well as economic ones, should be considered as people act on many different values (Levitt & Dubner, 2009). In the meantime, innovation should not be the exclusive property of a few gifted individuals but a collective driver and culture of an organization. A culture starts to take shape with more and more people having assumptions and behaviors in common (Tarique, Briscoe, & Schuler, 2016), and an organization’s culture becomes innovative when more employees engage in learning, try new things, and share their experiences (Dobni, 2008; Hurley & Hult, 1998). Considering this, efforts to mobilize innovative behavior should go beyond targeting individuals, aiming instead at nurturing an innovative organizational culture. This is where organization development (OD) comes into play, and the positive OD paradigm seems to be particularly appropriate. Unlike industrial conventions focused on fixing problems with a deficit-based approach, a strengths-based OD, such as appreciative inquiry, is better suited for the workplace context as it is meant to facilitate individuals’ working together to discover what inspires them and to deliver on their goals (Coopperrider & Whitney, 2001). Therefore, leaders are advised to take this positive approach that will motivate individuals to use their unique talents to perform creatively and innovatively (Kim et al., 2013), while boosting their involvement in life-giving communication and support of one another along the way to co-create an ecology of innovation that lasts (Bushe, 2007). 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