Enhancing competitive advantage in Hong Kong higher education: Linking knowledge sharing, absorptive capacity and innovation capability
Abstract
enThis study aims to investigate the relationship between knowledge sharing, absorptive capacity, innovation capability and competitive advantage in Hong Kong Higher Education Industry. A theoretical framework was developed to examine the relationship between knowledge sharing and competitive advantage, mediated by absorptive capacity and innovation capability. A questionnaire was used to survey a randomly selected sample of academics from higher education institutions in Hong Kong. A total of 166 usable responses were received and analysed. PLS-SEM was utilised to test the hypotheses in the theoretical model. The result exhibits that significant positive relationships were identified between knowledge sharing and absorptive capacity, between absorptive capacity and innovation capability and between innovation capability and competitive advantage. Practically, senior management from higher education institutions should develop strategic measures, especially on knowledge sharing, absorptive capacity and innovation capability, in order to gain competitive advantage. Policymakers could also develop policies to facilitate knowledge sharing among local and regional higher education institutions. This study contributes to existing literature by identifying the significant mediating role of absorptive capacity in the linkage between knowledge sharing and innovation capability and by strengthening the positive association between innovation capability and competitive advantage.
摘要
zh本研究旨在探讨香港高等教育界的知识共享，吸收能力，创新能力和竞争优势之间的关系。本文建立了一个理论框架，以研究吸收能力和创新能力为中介的知识共享与竞争优势之间的关系。并以问卷调查的方式收到来自香港高等教育机构的随机抽取的166份有效问卷。透过PLS-SEM的方法分析检验理论模型中的假设，研究结果表明：知识共享与吸收能力之间，吸收能力与创新能力之间，创新能力与竞争优势之间存在显着的正向影响关系。本研究提出实践中的启示：高等教育机构的高级管理人员应在知识共享，吸收能力和创新能力方面指定战略设施以获得竞争优势。政策制定者还可以制定政策，以促进地方和区域高等教育机构之间的知识共享。这项研究同时确定吸收能力在知识共享与创新能力之间的重要中介作用，以及通过加强创新能力与竞争优势之间的正向影响关系，为现有文献做出了贡献。
1 INTRODUCTION
The Hong Kong Higher Education Industry (HKHEI) has faced several key challenges in recent years: (a) lack of visibility of the HKHEI; (b) competitors from other regions; and (c) social and cultural barriers (Lo & Ng, 2013) together with the reduction in the school-age population in higher education, projected to fall from 71,700 in 2013/2014 to 45,100 in 2022/2023. How to sustain competitive advantage in the HKHEI is a crucial question that attracts the attention of many scholars and practitioners. Organisations gain competitive advantage through effective knowledge management strategies. In the domain of knowledge management, there are a number of different processes, for instance, capture, creation, sharing, utilisation and application. Knowledge sharing processes can be conceived as the processes through which employees mutually exchange knowledge and jointly create new knowledge in the organisation and thus foster innovation capacity and develop competitive advantages.
Several recent studies attempted to investigate some of the aforementioned factors, for instance, between knowledge sharing and absorptive capacity (Ali, Musawir, & Ali, 2018; Rafique, Hameed, & Agha, 2018); between knowledge sharing and innovation (Hussein, Singh, Farouk, & Sohal, 2016; Soto-Acosta, Popa, & Palacios-Marqués, 2017); and among knowledge sharing, absorptive capacity and innovation (Curado, Oliveira, Maçada, & Nodari, 2017; Kang & Lee, 2017). As most of these studies are conducted in other countries and industries, their generalisability is still questionable. To the best of our knowledge, there is a lack of empirical studies investigating the relationship between the knowledge sharing process and competitive advantage in HKHEI.
To fill this gap, this study develops a research model that investigates knowledge sharing and its relationships with absorptive capacity, innovative capability and competitive advantage. As the direct impact of knowledge sharing on innovative capability and the mediating effect of absorptive capacity are not fully addressed in prior studies, one of the research objectives is to examine this gap. Another objective is to examine the role of innovative capability in competitive advantage. Therefore, there are two research questions in this study: (a) What are the relationships between knowledge sharing, absorptive capacity and innovative capability? and (b) What is the relationship between innovative capability and competitive advantage? The first section of this paper reviews the relationship between knowledge sharing, absorptive capacity, innovation capability and competitive advantage. It then goes on to hypothesise development, and a conceptual model is built. The third section outlines the methodology, and the results obtained from the data analysis are then presented and discussed. A conclusion and implications are also included in the last section.
2 KNOWLEDGE IN HIGHER EDUCATION
Knowledge is the primary basis of sustainable competitive advantage in higher education institutions. Nonaka (1994) classified knowledge into two main types: tacit and explicit. A number of studies have compared and contrasted the characteristics of tacit and explicit knowledge. In this study, explicit knowledge refers to the academics’ course notes, teaching plan, course manuals, etc., while tacit knowledge refers to their teaching experience and pedagogical and research skills. Before commencing any knowledge sharing initiatives, organisations should identify the origin of knowledge. Knowledge location refers to the source of explicit and tacit knowledge residing in an organisation. Knowing the knowledge location enables employees to access and retrieve knowledge in a timely way (Chen, Li, Clark, & Dietrich, 2013), in turn, enhancing employees’ problem-solving ability and leading to higher organisational efficiency and effectiveness (Chen et al., 2013). Organisational knowledge can be found at two levels: (a) individual; and (b) organisational.
2.1 Individual level
At an individual level, academics, administration, service staff and students create and utilise knowledge in higher education institutions. Likewise, Abdel-Rahman and Ayman (2011) observe that most of the staff from the higher education sector perform the role of knowledge workers. Moreover, knowledge sharing among teachers, administrators, students and their parents is the key enabler of successful knowledge management implementation (Mawhinney, 2010; Sinha, Arora, & Mishra, 2012; Zhao, 2010). Resignation and retirement of academic staff are considered as a driver of knowledge loss in the higher education sector (Bhattacharjee, Shankar, & Gupta, 2015; Lin, Chang, & Tsai, 2016).
2.2 Organizational level
At the organisational level, knowledge is embedded in both intra-organisational networks and inter-organisational networks. Norms, values, cultures and relationships between organisational units are considered to be the knowledge found in an intra-organisational network (Zaied, 2012). In this study, knowledge at the organisational level refers to the (tacit) experience gained from programme development (or in the form of programme manual—explicit), collaborative research experience with university partners, consultancy services with business firms/government departments, internships, practicum and placements with employers. The next section reviews how knowledge can be shared within and between organisations.
3 ANALYSIS MODEL AND HYPOTHESES
3.1 Knowledge sharing
Knowledge sharing is one of the key processes in knowledge management (Foote & Halawi, 2018). Specifically, knowledge sharing is a process to exchange skills, experience and explicit and tacit knowledge among employees in an organisation. A number of terms, for instance, contribution, dissemination, distribution, transfer and socialisation, are used to describe the knowledge sharing process in a recent study. According to the knowledge sharing cycle proposed by Huysman and de Wit (2003), knowledge sharing can occur at both individual and organisational levels, and the shared knowledge can lead to knowledge creation or innovation. Mat and Razak (2011) emphasise that a good relationship between departments enhances the effectiveness of knowledge sharing. Knowledge sharing within an organisation also helps to improve the relationship between departments. Inter-departmental collaborative research and sharing of best practice among colleagues are the examples of knowledge residing in an intra-organisational network in higher education institutions. Some universities in Hong Kong adopted ‘Communities of Practice’ as an initiative for knowledge sharing. Under the higher education environment, an inter-organisational relationship is considered to be a connection with collaborated institutions (both local and overseas), industrial firms and students and parents.
3.2 Absorptive capacity
Cohen and Levinthal (1990, p. 128) defined absorptive capacity as the ‘ability of a firm to recognize the value of new, external information, assimilate it and apply it to the commercial ends’. Zahra and George (2002) recognise that absorptive capacity can be further divided into potential absorptive capacity and realised absorptive capacity. The former represents the acquisition and assimilation of knowledge, while the latter refers to the transformation and exploitation of knowledge. Liao, Fei, and Chen (2007) argue that knowledge sharing develops organisational absorptive capability—which ultimately improves innovation capability. Based on the empirical results in Taiwan’s knowledge-intensive industries, it is proven that knowledge sharing increases organisational absorptive capacity. In a recent study by Ali et al. (2018), a higher level of knowledge sharing is positively related to a higher level of absorptive capacity. Knowledge sharing is considered as a catalyst for exchanging tacit knowledge among project team members and it builds the absorptive capacity of the project team (Ali et al., 2018). This relationship is also confirmed in the study by Rafique et al. (2018) which explains that employees obtain external knowledge, share among them and thus increase the absorptive capacity of the firm. Therefore, this study proposes the following hypothesis:
Hypothesis 1.Knowledge sharing positively influences absorptive capacity in HKHEI.
3.3 Innovation capability
According to Hurley and Hult (1998), innovativeness refers to the degree of a firm’s openness to new ideas. In recent studies, innovation can also be defined as ‘successful implementation of creative ideas’ (Klijn & Tomic, 2010, p. 322) or ‘intentional introduction and application of new and improved ways of doing things’ (Kamasak & Bulutlar, 2010, p. 308). Some studies classified innovation into technical and non-technical innovations. The former one refers to product innovation, services innovation and process innovation, while the latter one includes innovation in managerial and marketing activities. Product/service innovation and process innovation are widely adopted in recent studies. Regarding the relationship between absorptive capacity and innovation capability, Kang and Lee (2017) conducted a study in the research and development department of an electronic company. The findings confirm that both potential absorptive capacity and realised absorptive capacity have a positive relationship with innovation behaviour. To generate creative ideas (a key step of innovation behaviour), it is critical for employees to access (potential absorptive capacity) and to use (realised absorptive capacity) external knowledge. The same finding is obtained by Curado et al. (2017) who argue that absorptive capacity ‘improves the speed, frequency and magnitude of innovation’ (p. 46). The following hypothesis thus is proposed:
Hypothesis 2.Absorptive capacity positively influences innovation capability in HKHEI.
A much-debated question is whether there is a direct relationship between knowledge sharing and innovation capability. Calantone, Cavusgil, and Zhao (2002) argue that intra-organizational knowledge sharing is a key element for enhancing firm innovativeness. In a tourism qualitative study, Hoarau and Kline (2014) develop a model to illustrate the importance of knowledge sharing practice in innovation outcomes. During the official trainings, meetings and informal activities, employees can share and exchange ideas. Meanwhile, employees interact with external stakeholders (vendors and clients) and obtain new ideas for innovation. The outcomes could be the improved product/service quality and efficient work processes. However, two recent quantitative studies disprove the direct positive relationship between knowledge sharing and innovation capability (Curado et al., 2017; Kang & Lee, 2017). Curado et al. (2017) add that knowledge sharing solely is not able to enhance the innovation and the shared knowledge must be absorbed by employees. To further examine this relationship, Hypothesis 3 is proposed:
Hypothesis 3.Knowledge sharing positively influences innovation capability in HKHEI.
3.4 The mediating role of absorptive capacity
The deliverable of the knowledge sharing process is shared knowledge. However, shared knowledge will not always be absorbed and implemented in the organisation. The absorptive capacity of the organisation plays an important role in knowledge creation and innovation capacity. Liao et al. (2007) proposed two arguments: (a) knowledge sharing improves innovation capability; and (b) knowledge sharing develops absorptive capacity, and then improves innovation capability. Only the second argument is supported by the data. This reveals that if knowledge is not reprocessed after sharing, then the shared knowledge is not enough to enhance a firm’s innovation capability. The study recommends that shared knowledge should be transformed through absorptive capacity. The mediating role of absorptive capacity is further confirmed in recent studies (Curado et al., 2017; Kang & Lee, 2017). In addition to knowledge sharing, knowledge must be absorbed and applied in order to formulate a firm’s competitive strategy (Soo, Tian, Teo, & Cordery, 2017). This study proposes Hypothesis 4:
Hypothesis 4.Absorptive capacity mediates the relationship between knowledge sharing and innovation capability in HKHEI.
3.5 Competitive advantage
Several recent studies defined competitive advantage as the core competency of an organisation that can lead to superior performance over competitors within the industry. With ‘valuable, scarce, inimitable and irreplaceable resources’ (Ren, Xie, & Krabbendam, 2009, p. 80), firms can derive competitive strategies (low-cost, differentiation and focus) to add value to their products and services. Human capital is one of these valuable resources (Bakir, Sofian, Hussin, & Othman, 2015). According to Marvel (2013), human capital refers to the knowledge, skills and experience owned by organisational members. Santos-Rodrigues, Dorrego, and Jardon (2010) confirmed that human capital has a significant impact on innovation, and this enables firms to gain competitive advantage. Furthermore, Aziz and Samad (2016) confirm that innovation has a positive impact on competitive advantage, and the study encouraged firms to invest in innovation so as to gain competitive advantage. A similar finding is obtained by Sulistyo and Siyamtinah (2016) who explain that high innovation capability will lead to superior performance and ultimately enhance competitive advantage. For example, a company with high innovation capability can develop new products which are difficult to be imitated by its competitors. This finding is comparable to those obtained in prior studies. Hypothesis 5 thus is proposed:
Hypothesis 5.Innovation capability positively influences competitive advantage in HKHEI.
To fully evaluate the relationships among constructs, this study will also examine two other antecedents of competitive advantage (i.e. knowledge sharing and absorptive capacity). First, Almahamid, Awwad, and McAdams (2010) examine the impact of organisational agility and knowledge sharing on competitive advantage. The multiple regression result exhibits that knowledge sharing has a significant impact on competitive advantage. However, Almahamid et al. (2010) point out that this may only be applicable in the less-developed industrial sector. Waheed, Qureshi, Khan, and Hijazi (2013) proved the significant role of knowledge sharing in organizational performance (including competitive advantage and innovation). As the study combined competitive advantage and innovation as organisational performance, this direct relationship needs further examination.
Secondly, Delmas, Hoffmann, and Kuss (2011) confirm the positive relationship between absorptive capacity and competitive advantage using the data from chemical firms. However, Liao, Chen, Hu, Chung, and Yang (2017) conclude that no relationship is found between absorptive capacity and competitive advantage in the finance sector. This direct relationship remains debatable. This study proposes the followings:
Hypothesis 6.Knowledge sharing positively influences competitive advantage in HKHEI.
Hypothesis 7.Absorptive capacity positively influences competitive advantage in HKHEI.
Based on the above discussion, a conceptual model is developed in Figure 1.
4 RESEARCH METHODS
4.1 Sample and data collection
To address the research question, an empirical study was conducted in Hong Kong. A knowledge-intensive industry—higher education—was chosen in this study and the target population was the full-time academics from Hong Kong higher education institutions. The sampling frame was obtained from the publicly available staff directories on institutional websites. This study adopted a simple random sampling survey method, and questionnaires were sent to potential respondents by mail. A postage-paid return envelope was attached and academics were invited to complete and return the questionnaire directly to researchers. After two reminder emails, 172 questionnaires were received. Data screening and data cleaning processes were conducted and 166 questionnaires were complete and valid.
4.2 Measures
In this study, items used to operationalise the constructs were mainly adapted from previous studies and modified for use in the knowledge management context. Four items in the knowledge sharing construct were modified from those in the study by Lin (2007). Absorptive capacity measurements were adapted from Leal-Rodríguez, Roldán, Ariza-Montes, and Leal-Millán’s (2014) scale—two for potential absorptive capacity and two for realised absorptive capacity. Four items for innovation capability were adapted from Leal-Rodríguez et al. (2014) and Liao, Hsu, and To (2013). Lastly, the items to measure competitive advantage were adapted from Delmas et al. (2011). The measurement of all constructs uses a 7-point Likert scale (1 = strongly disagree; to 7 = strongly agree). All the questionnaire items are presented in Table 1.
| Constructs | Code | Items | Sources |
|---|---|---|---|
| Knowledge sharing (KS) | KS1 | When I have learned something new, I tell my colleagues about it | Lin (2007) |
| KS2 | When they have learned something new, my colleagues tell me about it | Lin (2007) | |
| KS3 | I share my knowledge with colleagues when they ask for it | Lin (2007) | |
| KS4 | Colleagues in my unit share knowledge with me when I ask them to | Lin (2007) | |
| Absorptive capacity (ACAP) | ACAP1 | In my unit, colleagues have frequent interactions with (unit) top management to acquire new knowledge | Leal-Rodríguez et al. (2014) |
| ACAP2 | In my unit, colleagues can always collect knowledge through informal means (e.g. lunches with colleagues, friends, chats with partners) | Leal-Rodríguez et al. (2014) | |
| ACAP3 | In my unit, colleagues share teaching and research experiences frequently | Leal-Rodríguez et al. (2014) | |
| ACAP4 | In my unit, colleagues have a clear division of roles and responsibilities | Leal-Rodríguez et al. (2014) | |
| Innovation capability (INNOCAP) | INNOCAP1 | The level of newness (novelty) of our programme/courses is very high | Leal-Rodríguez et al. (2014) |
| INNOCAP2 | In my unit, new programme/course developments are mimicked by competitors | Liao et al. (2013) | |
| INNOCAP3 | In my unit, the management can foresee potential market opportunities | Liao et al. (2013) | |
| INNOCAP4 | In my unit, the management always solves problems using new knowledge | Liao et al. (2013) | |
| Competitive advantage (CADV) | CADV1 | Overall, my unit has a better reputation than the same unit of competitors | Delmas et al. (2011) |
| CADV2 | My unit can always develop new and unique programmes | Delmas et al. (2011) | |
| CADV3 | My unit can always have better research performance than the same unit of competitors | Delmas et al. (2011) | |
| CADV4 | My unit can always have better relationships with industry than the same unit of competitors | Delmas et al. (2011) |
4.3 Methods
This study adopted the partial least square (PLS) technique—a variance-based structural equation modelling (SEM) method—to test the hypotheses in the conceptual model. According to Lowry and Gaskin (2014, p. 127), SEM is used to ‘model multiple independent variables and multiple dependent variables, chains of causal effects and indirect effects’. In addition, Hair Jr, Sarstedt, Hopkins, & Kuppelwieser (2014) explained that PLS-SEM can be adopted with non-normal data and small sample size. The minimal recommended sample size ranges from 30 to 100 cases (Wiedenfels, 2009). Therefore, the assumptions of SEM were met in this study. Apart from the descriptive statistics, the reflective measurement model and structural model were evaluated with SmartPLS 3.0 (Version 28). Reflective measurement model is used to examine the internal consistency, convergent validity and discriminant validity, while the coefficients of determination and path coefficients were evaluated in the structural model.
5 DATA ANALYSIS AND RESULTS
5.1 Demographics
The characteristics of the respondents in this study are tabulated in Table 2. In this sample, the majority was male (65%). The ages of respondents vary between 25 and 34 (15.7%), 35 and 44 (31.3%), 45 and 54 (35.5%), 55 and 64 (16.9%) and 65 or above (0.6%). In addition, more than 75 per cent of the respondents have doctorate degrees (PhD or professional doctorate) and nearly one forth obtained a master’s degree (MPhil or taught master).
| Frequency | Percent | |
|---|---|---|
| Age | ||
| 24 or below | 0 | 0.00 |
| 25–34 | 26 | 15.66 |
| 35–44 | 52 | 31.33 |
| 45–54 | 59 | 35.54 |
| 55–64 | 28 | 16.87 |
| 65 or above | 1 | 0.60 |
| Gender | ||
| Male | 106 | 65.03 |
| Female | 57 | 34.97 |
| Education level | ||
| Doctorate | 125 | 75.30 |
| Master | 39 | 23.50 |
| Bachelor | 2 | 1.20 |
5.2 Results
Table 3 summarised the reliability results of the measurement model. First, the composite reliabilities range from 0.85 to 0.90, which is greater than the acceptable threshold value 0.7 (Nunnally & Bernstein, 1994). The internal consistency reliability was acceptable in this study. To examine the convergent validity, both indicators’ outer loadings and average variance extracted (AVE) were assessed. Indicators with an outer loading below 0.4 should be removed from the scale (Hair, Ringle, & Sarstedt, 2011). From the results, all outer loadings are above this critical value. Secondly, according to Fornell and Larcker (1981), the acceptable AVE is 0.5. In this sample, the AVE is from 0.55 to 0.70. As a result, the convergent validity was established in this study.
| Constructs | Mean | SD | Items | Loadings | AVE | CR | Cronbach’s α |
|---|---|---|---|---|---|---|---|
| KS | 4.86 | 0.98 | KS1 | 0.55 | 0.59 | 0.85 | 0.77 |
| KS2 | 0.81 | ||||||
| KS3 | 0.82 | ||||||
| KS4 | 0.85 | ||||||
| ACAP | 4.30 | 1.12 | ACAP1 | 0.78 | 0.63 | 0.87 | 0.80 |
| ACAP2 | 0.83 | ||||||
| ACAP3 | 0.89 | ||||||
| ACAP4 | 0.65 | ||||||
| INNOCAP | 4.38 | 1.12 | INNOCAP1 | 0.79 | 0.55 | 0.83 | 0.74 |
| INNOCAP2 | 0.72 | ||||||
| INNOCAP3 | 0.70 | ||||||
| INNOCAP4 | 0.76 | ||||||
| CADV | 4.51 | 1.25 | CADV1 | 0.89 | 0.70 | 0.90 | 0.86 |
| CADV2 | 0.86 | ||||||
| CADV3 | 0.82 | ||||||
| CADV4 | 0.77 |
Note
- Remarks: Cut-off values for: CR: 0.7; AVE: 0.5; C-α: 0.7.
Fornell-Larcker criterion and heterotrait-monotrait (HTMT) ratio of correlations were adopted to access the discriminant validity. Table 4 presents the results regarding discriminant validity of four measure scales. The bold figures in the matrix diagonals, representing the square roots of the AVEs, are greater in all cases than the off-diagonal elements in their corresponding row and column. Besides, all HTMT values were below the threshold of 0.85. Discriminant validity was supported in this research.
| Constructs | KS | ACAP | INNOCAP | CADV |
|---|---|---|---|---|
| KS | 0.77 | 0.71 | 0.54 | 0.57 |
| ACAP | 0.60 | 0.79 | 0.79 | 0.72 |
| INNOCAP | 0.47 | 0.65 | 0.74 | 0.82 |
| CADV | 0.49 | 0.60 | 0.69 | 0.84 |
Notes
- Boldface numbers on the diagonal are the square root of the average variance extracted; Values below the diagonal elements are the correlations between the constructs; Values above the diagonal elements are the HTMT values.
Figure 2 exhibits the results of the structural model. Knowledge sharing showed a positive influence on absorptive capacity (Hypothesis 1: β = 0.595; p < .01), and Hypothesis 1 is supported. Secondly, a positive association between absorptive capacity and innovation capability was proven (Hypothesis 2: β = 0.572; p < .01). Thus, Hypothesis 2 is supported. However, the impact of knowledge sharing on innovation capability is insignificant; Hypothesis 3 is not supported. Based on the results from Hypothesis 1 to Hypothesis 3, absorptive capacity fully mediates the relationship between knowledge sharing and innovation capability (Hair, Hult, Ringle, & Sarstedt 2017). Thus, Hypothesis 4 is supported. In addition, the linkage between innovation capability and competitive advantage is significant (Hypothesis 4: β = 0.500; p < .01). Hypothesis 5 is also supported. Lastly, both knowledge sharing and absorptive capacity were not the significant predictors of competitive advantage. Therefore, Hypothesis 6 and 7 are not supported. Table 5 summarises the evaluation result of the structural model.
| Hypotheses | Path coefficients | Conclusion |
|---|---|---|
| Hypothesis 1: Knowledge sharing positively influences absorptive capacity in HKHEI | 0.595* | Supported |
| Hypothesis 2: Absorptive capacity positively influences innovation capability in HKHEI | 0.572* | Supported |
| Hypothesis 3: Knowledge sharing positively influences innovation capability in HKHEI | 0.131 | Not supported |
| Hypothesis 4: Absorptive capacity mediates the relationship between knowledge sharing and innovation capability in HKHEI | 0.340* | Supported |
| Hypothesis 5: Innovation capability positively influences competitive advantage in HKHEI | 0.500* | Supported |
| Hypothesis 6: Knowledge sharing positively influences competitive advantage in HKHEI | 0.134 | Not supported |
| Hypothesis 7: Absorptive capacity positively influences competitive advantage in HKHEI | 0.197 | Not supported |
- * Significant at the .01 level (2-tailed).
6 DISCUSSION AND IMPLICATIONS
6.1 Discussion of findings
To address the two research questions in this study, an empirical survey was used to test the hypotheses in the context of the HKHEI. The positive associations between knowledge sharing and absorptive capacity and between absorptive capacity and innovative capability are confirmed. However, the linkage between knowledge sharing and innovative capability is not significant. These findings are in accord with recent studies indicating that absorptive capacity acts as the mediator between knowledge sharing and innovative capability (Curado et al., 2017; Kang & Lee, 2017; Liao et al., 2007). As explained by Curado et al. (2017), knowledge sharing alone is not enough for the organisation to become innovative. After sharing, knowledge must be recognised, absorbed and used so as to develop innovative solutions for organisations. Hansen (2015) highlighted the importance of innovation in higher education. After the knowledge sharing process, it is also crucial for faculty members to recognise the value of shared knowledge, to integrate (Rundquist, 2012) and to transform (Liao et al., 2007) the knowledge into innovative competence in universities. This study confirms the mediator role of absorptive capacity between knowledge sharing and innovative capability, and points out that those academics have to absorb and integrate the shared knowledge so as to influence the innovative capability in their units.
The relationship between innovative capability and competitive advantage is positive and significant in this study. The result is consistent with data obtained in recent studies (Aziz & Samad, 2016; Chen, Lin, & Chang, 2009; Sulistyo & Siyamtinah, 2016). Chen et al. (2009, p. 157) concluded that ‘the more investments in innovation performance, the better is the competitive advantage’. Similarly, Hana (2013) explains that knowledge residing in individuals helps to generate new innovative ideas, which ultimately enhance the competitive advantage of organisations. In order to gain competitive advantage, universities can incorporate innovative elements in the topics suggested by Ehlers and Schneckenberg (2010), including lifelong learning, ICT adoption at all levels, ubiquitous learning, affordable education, collaborative learning, diversity, international and new forms and patterns. Ehlers and Schneckenberg (2010, p. 4) further add that ‘innovation will be at the heart of excellence and the origin for all new approaches to lead change in higher education organizations’.
The launch of open educational resources is an example of innovation, and universities can increase their reputation and earn mutual beneficial partnerships with industry and society through sharing their resources (Ehlers & Schneckenberg, 2010). Furthermore, Powell and Yuan (2013) highlight that openness is a key element in driving innovation and, in turn, in gaining competitive advantage in the higher education sector. Openness incorporates the components of open curriculum, open learning, open assessment and open platform. For instance, a number of higher education institutions in Hong Kong—for example, the Chinese University of Hong Kong (CUHK), the University of Hong Kong (HKU) and the Hong Kong University of Science and Technology (HKUST)—have started to develop a series of Massive Open Online Courses (MOOCs) via different online education platforms (e.g. edX and Coursera).
Apart from openness, the theory of stakeholders is discussed. In contemporary organisations, Hana (2013) found that the three largest impulses for innovation are large customers, suppliers and employees. In the domain of higher education, the impulses for innovation come from students, faculty members, parents, employers, alumni and professional entities. In order to gain competitive advantage through innovation, the study suggests grouping specialists from both internal and external environments so as to generate new ideas. The implications for stakeholders will be discussed in the following section. Lastly, a number of studies confirm the relationship between capabilities, innovation and competitive advantage (Schilke, 2014). With the concept of a resource-based view, Barreto (2010) summarises that organisations should bundle their resources and capabilities so as to develop non-substitutable solutions. By introducing high imitation costs to competitors, an organisation can sustain its competitive advantage in the market. To sum up, the openness concept, stakeholders and resource/capability are key innovative capability components in enhancing competitive advantage.
6.2 Implications for practitioners
This study also presents several practical implications for both the leaders and administrators in the Hong Kong higher education sector and the policymakers in the region. The relationship between knowledge sharing and innovative capability was proven to be mediated by absorptive capacity. This is consistent with another recent study by Curado et al. (2017) which advised practitioners to spend more resources on knowledge sharing and absorptive capacity so as to influence innovative capability in organisations. Higher education leaders should develop different initiatives to enhance the absorptive capacity of faculty members. As suggested by Jambekar and Pelc (2006), organisational learning is one of the strategic ways to enhance absorptive capacity. To enhance the absorptive capacity in higher education institutions, management should establish a learning organisation environment, including shared vision, trust and a team learning culture (Qureshi & Evans, 2013). Some studies also agree that technology can act as a tool to support absorptive capacity enhancement (Peltola & Mäkinen, 2014). Otherwise, institutions could not benefit from the shared knowledge among academics.
Apart from higher education leaders, policymakers can contribute at two levels (i.e. local and regional). At local level, the Education Bureau of the Government of Hong Kong (EDB) organised knowledge management activities for teachers and principals from primary and secondary schools. However, limited knowledge sharing activities are organised at tertiary level. As different research funding schemes and study subsidy schemes are designed for publicly-funded and self-financing higher education institutions, policymakers can assist the knowledge sharing process between them, in terms of the design of academic programmes and research experience. With the development of Guangdong, Hong Kong and Macao Greater Bay Area, the Constitutional and Mainland Affairs Bureau of the Government of Hong Kong (CMAB) developed 12 policy areas, including education (https://www.bayarea.gov.hk/en/opportunities/education.html). Some initiatives include the cooperation among higher education institutions of Guangdong, Hong Kong and Macao, the set-up of laboratories and research centres and the commercialisation of scientific research outcomes. By identifying the strengths of each institution in Hong Kong, policymakers can facilitate the matching of collaborative institutions from other regions, thus speeding up the knowledge sharing process.
At the organisational level, higher education management is highly recommended to promote knowledge sharing among different units in universities and with external entities. Synergy could then be achieved through the collaboration between colleges/schools/departments. As a result, the overall teaching and research quality can be increased. In the same vein, strengthening the relationships with government, business firms and university partners can also lead to greater knowledge sharing and ultimately enhance the innovative capability of institutions. For instance, colleges/schools/departments can encourage employers and business firms to share the performances of the graduates of their institutions and the shared knowledge helps to streamline the design of academic programmes.
In addition, the positive association between innovative capability and competitive advantage is confirmed in this research. In order to compete with local and global players, higher education leaders should bear in mind that innovation is an essential element in future higher education development. As advocated by Powell and Yuan (2013), higher education management can incorporate the concept of openness—including open curriculum, open learning, open assessment and open platform—in their practices.
Although the focus of this study is on the higher education faculty members, this research attempts to point out the implications for the stakeholders. Aligned with the theory of stakeholders mentioned before, a quality cycle, including alumni, industries, government authorities and quality assurance agencies, can be formed in higher education. Lamichhane and Sharma (2010, p. 63) provide a view that ‘both university and industry are benefited and create synergy in improving performance, bring about innovation and change through collaboration’. Universities and industries can collaborate in three areas: (a) curriculum; (b) consultancy services; and (c) research projects. Collaborative relationships enable universities to foster knowledge diffusion, increasing research outputs, patenting the innovation and finally enhancing their competitive advantage (Hamdan et al., 2011). Industries with limited research and development budgets can definitely benefit from this symbiotic relationship (Hamdan et al., 2011). In addition, industries are given the opportunities to select appropriate candidates as their employees. A win-win situation can be achieved through this mutually beneficial relationship (Lamichhane & Sharma, 2010). Furthermore, mentoring is proven as an effective way to facilitate knowledge creation, knowledge sharing and building intellectual capital (Karkoulian, Halawi, & McCarthy, 2008). Alumni can take the opportunity to join the mentorship programme or similar activities so as to provide feedback to faculty members and guidance to current students.
7 LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH
Despite the theoretical and practical implications, this study is subject to several limitations. First, the generalisability of this study is limited because the sample was drawn from eight Hong Kong higher education institutions only. Moreover, this is a single-industry analysis. The attributes of these institutions may be different from organisations in other industries and countries. Moreover, the intensity of competition varies among different industries and countries. Hence, the research framework should be further validated using samples from different sectors and regions. Further testing would yield a more robust result. Secondly, the respondents in this study were the faculty members of higher education institutions in Hong Kong. Tan, Wong, Lam, Ooi, and Ng (2010) confirm that the role of administrative staff is also significant in the knowledge sharing process. In the future, researchers could compare the role between academics and administrative staff in the knowledge sharing process. Thirdly, this study adopted a cross-sectional survey research design. Data were collected from academics at one point in time and the relationships tested represent a snapshot in time. As suggested in numerous knowledge management studies (Liao & Wu, 2010), this limitation can be overcome by collecting longitudinal data. Fourthly, the sample size in this study is 166 and this met the minimum requirement of PLS-SEM analysis. As a rule of thumb, the larger the sample size, the better the statistical power. The findings in this study should be further verified with a larger sample size; thus, a higher statistical power will lead to greater generalizability.
Fifthly, as there is a significant growth in the private higher education sector in Hong Kong, the results in this study may not be generalised to these institutions. The organisational culture may vary between public and private higher education sectors. As discussed in recent knowledge management studies (Lee, Shiue, & Chen, 2016), organisational culture plays a crucial role in knowledge sharing behaviour. Future researchers could conduct a comparative study to examine the impacts of organisational culture antecedents on knowledge sharing behaviour between public and private higher education institutions. Sixthly, this study did not consider all the antecedents of knowledge sharing behaviour. For instance, Hau, Kim, Lee, and Kim (2013) utilised the social capital theory to develop the social capital construct (including social ties, social trust and social goals) in a knowledge management study. The empirical result showed that a significant positive relationship was identified between social capital and knowledge sharing. The role of social capital in knowledge management domains has also been widely investigated recently (Akhavan & Mahdi Hosseini, 2016). Finally, apart from the social capital variable, future researchers could also incorporate another antecedent of knowledge sharing, knowledge governance (Ali et al., 2018), which has been proven in other studies.
