Abstract

Several studies have explored firm performance in the post-Covid-19 pandemic era. However, there is not much research to find reports divulging the complex relationship dynamics between business intelligence, organizational and network learning, customer value anticipation, and creative economy-based small-medium enterprises (SMEs) performance in developing countries. This study aims to uncover the complexity of those relationships. The quantitative data were collected from 313 creative economy-based SMEs in East Java, Indonesia. Using PLS-SEM, this study disclosed that business intelligence practices could not directly impact SMEs’ performance. Business intelligence will be crucial to SMEs’ performance with the support of organizational learning as a mediator. The finding also confirmed the presence of serial mediation of organizational learning and innovation in the relationship between business intelligence and SMEs’ performance. However, the role of network learning and innovation is also important, considering their relatively large direct impact on SMEs’ performance. The theoretical implications of this research broke the boundaries of strategic management theory in resource-based view and knowledge-based view in the latest era, where creative economy-based SMEs have been able to mobilize resources to carry out business intelligence to realize innovation and high performance. Further research is suggested to explore the role of business intelligence in promoting specific performance areas, such as marketing performance, financial performance, and human resource management. In addition, it is advisable to choose more specific research subjects, including those in the culinary subsector, and pay attention to other areas, e.g., the demographics of respondents in the model as a control variable.

Introduction

The performance of small-medium enterprises (SMEs) in the post-pandemic era of Covid-19 has been in the spotlight of many scientists, e.g. Ref. [1] highlighted the declining performance of 273 SMEs at the company level operating in commercial, manufacturing, service, or construction industries in certain geographical areas in Finland due to a competitive mindset contaminated by high-performance growth aspirations [2], examined the impact of identity-based entrepreneurial passion on the performance of 193 established SMEs in Ghana [3], identified the mediating role of green product innovation between green entrepreneurial orientation, green transformational leadership, and the performance of 384 manufacturing SMEs operating in Amman, Jordan [4], discussed the export performance of 329 SMEs in Vietnam and how they are developing their internal skills to face external challenges arising from the digitalization process and the COVID-19 crisis [5], analyzed the role of learning in an organization while measuring and managing the sustainable performance of 710 manufacturing sector SMEs operating in Laos [6], examined the impact of international resources on the sustainable performance of 380 Pakistani SMEs through the mediating role of green entrepreneurial orientation [7], tested the influence of organizational creativity and open innovation on the performance of 206 SMEs spread across several regions in Indonesia., and [8] explored how 247 Vietnamese SMEs can leverage information technology (IT) to overcome crises, exploit innovative opportunities, adapt to changing market conditions, and drive new competitive initiatives. This is due to the prominent role of creative economy-based SMEs as the drivers of economic growth in Indonesia and other countries [9]. East Java is no exception; one of the regions contributing 14.92% of Indonesia’s national gross domestic product (GDP) [10]. This province has much potential in the creative economy sector, which is supported by 113 startups [11,12] to face the turbulent business environment as a result of accelerating digital transformation [13] during the Covid-19 pandemic. The number of creative economy-based SMEs will continue to grow, even from the activities of homemakers who are familiar with digital literacy and the ease of doing business today [14].

Conclusion

This research has attempted to investigate the link between business intelligence, organizational learning, network learning, customer value anticipation (CVA), innovation, and creative economy-based SMEs performance in East Java of Indonesia. The findings indicate that business intelligence does not directly impact SMEs’ performance because the data generated from business intelligence practices needs to be integrated into knowledge that must be learned through organizational learning. If this is offset by CVA, it will give birth to innovations that ultimately impact SMEs’ performance. Hence, even though the impact of serial mediation network learning and innovation presence on the relationship between business intelligence and performance is not supported by field data in this study, network learning still need attention to improve SMEs’ performance. The theoretical implications of this research break the boundaries of strategic management theory resource-based view (RBV) and knowledge-based view (KBV) in the latest era, where creative economy-based SMEs in developing countries have been able to mobilize resources to carry out business intelligence, even though in a simple way, to realize Innovation and high performance.

The limitation of this research is that it has yet to explore the causes of the insignificant effect of network learning on innovation. Thus, future research can conduct qualitative studies to obtain more in-depth information regarding the relationship between network learning with Innovation. In addition, this research still uses economy creative-based SMEs as samples, while future research is recommended to involve specific samples, for example, in the culinary sector only, the sector with the highest number in East Java. Researchers can also pay attention to other provinces, such as West Java, which has the largest creative economy exports in Indonesia. Further scholars are also expected to explore the role of business intelligence in more specific performance areas, such as marketing performance, financial performance, and human resource management, and include the demographics of respondents in the model as a control variable. The data distribution in PLS-SEM analysis is only possible to assess statistical significance using the Bootstrapping technique.

Abstract

This study aims to investigate the intersection between organizational learning (OL) and knowledge management (KM) in the tourism and hospitality sector by conducting a bibliometric analysis. A total of 628 publications from 293 various sources and 1468 different authors were included in the analysis between 2001 and 2021. Open-source statistical R software was utilized to conduct the bibliometric analysis, specifically, the Bibliometrix R library and the Biblioshiny version. The VOSviewer program was also used to display the keyword co-occurrence network. The findings show that the implication of KM and OL has been widely recognized as critical learning facilitators and as strategic tools for gaining competitive advantage that recognizes opportunities and threats for possible alignment with environmental change. Organizational learning and KM are particularly relevant for sustainable tourism and hospitality solutions, cutting-edge technologies, and organizational service innovations. Four research clusters were identified that provide critical future research paths.

Introduction

Tourism and hospitality are important economic sectors in terms of overall contribution to GDP and employment (López-Bonilla & López-Bonilla, 2021). Although tourism and hospitality have been investigated from a variety of research perspectives (Harrison, 2007), knowledge management (KM) and organizational learning (OL) are relatively new to tourism studies (Ghaderi & Paraskevas, 2021). This late adoption originates from multiple constraints in the sector, such as the substantial presence of small and family businesses with limited managerial expertise, heterogeneity of supply, a large short-term and seasonal workforce, and a high rate of business and workforce turnover (Cooper, 2006; 2015; 2018). Nevertheless, there is a growing recognition of the importance of knowledge to the success and competitive advantage of enterprises (Paraskevas et al., 2013). The knowledge economy emerged in the 1990s (Cooper, 2015), where knowledge management and organizational learning was recognized as one of the most crucial resources and capabilities of firms to achieve competitive advantage (Eisenhardt & Martin, 2000; Grant, 1996). The knowledge economy differs from conventional economic theory because it is based upon innovation and intellectual capital to generate value, recognizing that knowledge is more than simply information: rather it is a resource to be valued and managed (OECD, 1996). Since the 1990s, knowledge as an intangible resource, and KM itself, has attracted the interest of business management researchers (Gaviria-Marin et al., 2019) and led to the emergence of learning organizations. These are firms that successfully implement and integrate KM and OL capabilities to achieve competitive advantage and superior performance (Pemberton & Stonehouse, 2000; Sun & Scott, 2003). In addition, KM has helped hospitality organizations to internationalize as this could enhance their learning competences related to organizational ambidexterity and positively influence performance (Ubeda-Garcia et al., 2021).

Discussion and conclusions

This study implemented bibliometric methods to investigate the intersection between OL and KM in tourism and hospitality research over two decades. From the results, several conclusions can be extracted. First, while these fields of research have been well-established in other disciplines, our findings point to the immaturity of the tourism, and hospitality field with fewer citations in tourism and hospitality up until a growth stage from 2017 onwards (almost 17% annually). In other words, research on KM and OL emerged relatively late in tourism and hospitality. This is likely explained by the growing understanding of the significant role of KM and OL in enhancing organizations’ performance and competitive advantage (Hassi, 2019), organizational innovation and sustainability, and crisis management practices. These topics have been at the frontline of much research, especially in the second decade of the Second, as discussed, social capital is highlighted as a key organizational success factor that is significantly attributed to the application of KM and OL through network creation and knowledge sharing (Kim & Shim, 2018; Paraskevas et al., 2013). Both OL and KM can boost social capital and improve human resource capabilities.

Abstract

     Transformative Organizational Change becomes more and more significant both practically and academically, especially in the context of organizational culture and learning. However computational modeling and a formalization of organizational change and learning processes are still largely unexplored. This paper aims to provide an adaptive network model of transformative organizational change and translate a selection of organizational learning and change processes into computationally modelled processes. Additionally, it sets out to connect the dynamic systems view of organizations to self-modelling network models. The creation of the model and the implemented mechanisms of organizational processes are based on extrapolations of an extensive literature study and grounded in related work in this field, and then applied to a specified hospital-related case scenario in the context of safety culture. The model was evaluated by running several simulations and variations thereof. The results of these were investigated by qualitative analysis and comparison to expected emergent behaviour based on related available academic literature. The simulations performed confirmed the occurrence of an organizational transformational change towards a constant learning culture by offering repeated and effective learning and changes to organizational processes. Observations about various interplays and effects of the mechanism have been made, and they exposed that acceptance of mistakes as a part of learning culture facilitates transformational change and may foster sustainable change in the long run. Further, the model confirmed that the self-modelling network model approach applies to a dynamic systems view of organizations and a systems perspective of organizational change. The created model offers the basis for the further creation of self-modelling network models within the field of transformative organizational change and the translated mechanisms of this model can further be extracted and reused in a forthcoming academic exploration of this field.

Introduction

     Organizational culture plays an important role in organizations’ success and failures (Johnson, Nguyen, Groth, Wang, & Ng, 2016), as organizational culture offers employees a framework they can apply to reality, which helps them to evaluate what is of significance for the organization and themselves, and what is irrelevant to the organization (Łukasik, 2018). Therefore to be successful in a change of strategy, e.g., towards more sustainability, a change in organizational culture is often inevitable (Bedford & Kucharska, 2021).

     Organizational culture is especially important nowadays as is its constant improvement in the context of Health Care, as Covid 19 is putting further pressure on public healthcare systems (Ojogiwa & Qwabe, 2021). Especially constant learning culture in healthcare organizations is vital as it supports their innovation performance thanks to human capital development (Kucharska, 2022). Furthermore, communication and cooperation patterns between employees directly impact care for patients, so demanding circumstances lead to lower-quality patient outcomes (Johnson et al., 2016). Patient safety is naturally the healthcare system’s priority, hence many healthcare organisations have deeply embedded safety-oriented cultures. To prevent healthcare safety-related harms, the Institute of Medicine (IOM) recommends a culture of safety, understood as a constant improvement of patient care (Kohn et al., 2000).

Discussion & conclusion

     This paper was based on material from (Rass et al., 2022). The goal of this research was to further explore the field of transformational change, in the context of organizational learning and culture by computational modelling of organizational and individual processes. This specifically realizes itself in the objective to create an adaptive multi-order self-modelling network model that conceptualizes and approximates transformative organizational cultural change. The implemented mechanisms of organizational processes were based on an extensive literature study and grounded in related work in this field (Canbaloğlu et al., 2021), creating the described computational model of this study.

6.1. Evaluation of the computational model for the research focus

     To confirm the validity of the created computational model, a scenario and variations to it were created, enabling us to compare the models’ emergent behaviours. To further substantiate the model, the results of the variations of the scenario got compared to the base scenario, to gain knowledge about possible network effects (is there a better way to say something like “observe interplays and isolations of the mechanisms” again).

Abstract

     With intensifying competition and increasing dependence on external partners, open innovation has been becoming an inevitable and prevalent strategy to achieve long-term competitive advantage. Considerable research has investigated the benefit of open innovation, the mechanism through which open innovation translates into sustainable competitive advantage have yet been well examined. Drawing on the strategy-competence-competitive advantage framework and the knowledge-based view, this study examines the role of ambidextrous organizational learning in mediating the relationship between open innovation and sustainable competitive advantage as well as the moderating role of knowledge management capability. Based on data collected from 269 Chinese high-tech enterprises in 2021, we found that open innovation contributes to sustainable competitive advantage through enhancing organizational learning including both exploratory and exploitative learning as well as their balance. Moreover, knowledge management capability positively moderates the relationships of open innovation to exploration and exploitation as well as their combination. Research and practical implications are discussed.

Introduction

     As a result of globalization, technological complexity, increased competition and resource scarcity, organizations are changing and adopting a more open, cooperative approach to building their competitive advantage (Barrett et al., 2021; Lewis et al., 2010). Hence, open innovation has been becoming an important concept in both academic research and industrial practice; it refers to “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively” (Chesbrough, 2006). Compared with closed innovation, open innovation permits firms to explore outside knowledge and to externally exploit existing internal resources to gain a competitive edge (Drechsler and Natter, 2012; Popa et al., 2017). The more a business interacts with other organizations, the higher its chances of acquiring external ideas, capabilities, knowledge, technology and other intangibles, and the greater its chances of successful innovation (Greco et al., 2016). Moreover, the complementarity and collaboration of internal and external innovation resources will gradually improve enterprises’ innate innovation capability, helping them to ultimately form their own core competitiveness (Lichtenthaler, 2011).

Results

Hypothesis testing

     Table 4 shows the descriptive statistics and correlation analysis on all variables. Before the regression analysis, we mean-centered all variables to minimize multicollinearity. After centering, the results revealed that the VIF values of each model ranged from 1.029 to 3.579, and the tolerance was >0.100, implying no serious multicollinearity problem (Kock, 2015). We also analyzed the sample proportion; the sample proportion of balanced learning was 47.584 %, and the sample proportions of “high exploration-low exploitation” and “low exploration-high exploitation” of imbalanced learning were 26.022 % and 26.394 %, respectively, which met the requirements of polynomial regression (Shanock et al., 2010). The empirical regression results of this paper were depicted in Tables 5 and 6. Table 5 showed that open innovation had a significant, positive effect on sustainable competitive advantage (Model 9, β = 0.49, p < 0.001), supporting H1. In addition, open innovation had a significant, positive effect on exploratory learning (Model 1, β = 0.34, p < 0.001), exploitative learning (Model 3, β = 0.47, p < 0.001), OAB (Model 5, β = 0.25, p < 0.001), and OAC (Model 6, β = 0.17, p < 0.05) respectively. It can be seen from Table 6 that exploratory learning (Model 10, β = 0.51, p < 0.001), exploitative learning (Model 11, β = 0.64, p < 0.001), OAB (Model 14, β = 0.22, p < 0.001), and OAC (Model 13, β = 0.10, p < 0.05) had significant, positive effects on sustainable competitive advantage. Thus, we further tested the mediating effect via the SPSS Process V3.3 plugin performing Bootstrap 5000 repeated sampling to test whether the confidence interval at the 95 % level contains 0 (Hayes, 2017). The mediating effect value of exploitative learning was 0.25, and the confidence interval was [0.17, 0.32], thereby supporting H2a. The mediating effect value of exploratory learning was 0.13, and the confidence interval was [0.07, 0.19], thus supporting H2b. The mediating effect value of OAC was 0.01, and the confidence interval was [− 0.01, 0.05], which did not support H3a. The mediating effect value of OAB was 0.02, and the confidence interval was [0.01, 0.05], supporting H3b.

Abstract

     Scholars have emphasised the importance of green settings in today’s business paradigms. Studies on green behaviour have produced a plethora of noteworthy discoveries, whether focused on financial success, individual capabilities, or development. However, despite significant growth in interest in green business practices, the relationship between individuals’ willingness and green competencies has received little attention. This article used the customised green competencies conceptual model to investigate how green skills influence organisational performance and their relationship with the willingness moment. This article developed an innovative human resource management approach to address these difficulties. A questionnaire was used to perform empirical statistical research with 516 respondents from Serbian universities. Different mathematical and statistical methodologies were used to analyse the results. The findings corroborate the suggested theoretical model, and they suggest that green competencies will influence people’s willingness to participate in green activities. This article gives new information on human behaviour and organisational effectiveness in a green atmosphere. It includes managerial and practical consequences and recommendations for businesses looking to improve their social responsibility and environmental sustainability

Introduction

     Rapid development as a result of technology and scientific developments and many social, health, and financial crises in recent decades and worldwide trends have had a significant impact on business and the environment [1,2]. In today’s global business climate, the only certainty is uncertainty. Many ongoing modern concerns have prompted changes in how organisations think about their objectives. Due to the ever-changing business environment in the age of globalisation, businesses face numerous challenges [3]. Recent difficulties and globalisation have a catalytic effect in all aspects of contemporary life and business [4], driving organisations to grow resiliently and sustainably. To preserve and acquire a competitive edge, organisations must adapt their business models to be more inclusive, pro-environmental, and innovative. Innovation for more significant influence entails developing innovative products or procedures to assist the organisations’ beneficiaries and effectively address critical challenges [5]. The growing need for sustainable techniques can be seen in all industries [6], and there has recently been a slew of studies on sustainability and financial performance [7].

Conclusions

     According to the findings, the aim of the paper was fulfilled. Natural green competencies, acquired green competencies, and attitudes toward green behaviour of an individual affect the willingness of the same individual. Furthermore, university students are involved in environmental protection, although their attitudes and understanding about environmental preservation vary. However, much more can be done to raise student understanding of this critical issue. In this regard, both students and universities should work harder to promote environmentally friendly behaviour and green competencies, leading to improved green performance.

      Green settings have become an essential part of the contemporary business environments, and it forces organisations to shift business paradigms to be more inclusive, proenvironmental, and innovative to maintain and gain a competitive edge. Furthermore, every forward-thinking organisation should consider incorporating sustainability into its processes to build future growth strategies. Green competencies of individuals are an essential part of this process. This paper endeavoured to examine how organisational performance is influenced by green competencies and willingness of individuals, and their interdependence and mutual relations. As a result, it can be concluded that a more inclusive and pro-environmental approach will have direct and indirect impacts on organisational growth, financial performance, and the environment. Accordingly, it means that embracing modern pro-environmental trends and nurturing green competencies benefit organisations in many ways.

Abstract

     Business model exists within business ecosystems, and stakeholders can exert a key influence on a firm’s business model innovation. Drawing insights from the business ecosystem perspective and resource-based view, this study examines how ties with stakeholders can affect a focal firm’s business model innovation and how the relationship is contingent upon the firm’s learning types. Analyses of 210 Chinese firms reveal that the relationship between intra-industry stakeholder ties and business model innovation is inverted U-shaped, while extra-industry stakeholder ties have a positive effect on business model innovation. The relationships between both intra-industry and extra-industry stakeholder ties and business model innovation are weakened by exploitative learning but strengthened by exploratory learning. Theoretical and practical implications are discussed.

Introduction

     Business model innovation (BMI), which involves holistic alterations to the structure and architecture of firms’ boundary-spanning activity systems for creating, delivering, and capturing value, has recently attracted considerable scholarly attention due to its ability to increase a firm’s competitive advantages (Bouncken and Fredrich 2016; Foss and Saebi, 2017). Nowadays, the locus of value creation and value capture has shifted to the business ecosystem (Adner and Kapoor, 2010) composed of interdependent stakeholders (e.g., customers, competitors, suppliers, social organizations, and other institutions) and the relationships between all stakeholders (Moore, 1993; Wei et al., 2017). Firms are relying increasingly on stakeholders in the business ecosystem to jointly create and capture value by redesigning their business models (Amit and Zott, 2015).

     An increasing number of scholars have realized that the business model, despite being often studied as a firm-centric concept, is an ecosystem-embedded construct (Amit and Zott, 2015; Frishammar and Parida, 2019). They acknowledge that BMI extends the dyadic relationships involving multiple ecosystem stakeholders (Sjödin et al., 2020). Hence, BMI is not only constrained by firms’ internal factors, but also affected by ecosystem-level factors, particularly stakeholders. Despite this, the literature exploring the antecedents of BMI has been guided mainly by the firm-centric view that focuses on the effects of firms’ internal factors (e.g., McDonald and Eisenhardt, 2020; Wei et al., 2017), leaving the role of stakeholders largely underexplored. However, given business model researchers’ growing interests in the business ecosystem, one intriguing question is what role ecosystem-level factors, particularly stakeholders who constitute the principal subjects of a business ecosystem (Lu et al., 2014), play in driving BMI? Unfortunately, the answer remains unclear.

Analysis and results

Results

     The descriptive statistics and correlation analysis results for all variables, including the mean values, standard deviations, and correlation coefficients, are presented in Table 3. To assess multicollinearity, we calculated the variance inflation factors (VIFs). The maximal VIF value in all of the models was 2.309, below the cutoff of 10. To further minimize multicollinearity, we mean-centered the independent and moderating variables before generating interaction terms (Aiken and West, 1991). We adopted multivariate regression analysis and the moderated method (Baron and Kenny, 1986) to test our hypotheses. The steps performed to test the hypotheses are detailed in Table 4.

     H1 predicted an inverted U-shaped relationship between intraindustry stakeholder ties and BMI. We followed the suggestions of Haans et al. (2016), who pointed out that tests of an inverted U-shaped relationship should meet the following conditions: a) coefficients must be significant and of the expected sign, b) the slope of the curve must be sufficiently steep at both ends of the data range, and c) the turning point of the curve must be located well within the data range. First, Model 3 shows that intra-industry stakeholder ties have a positive and statistically significant effect on BMI (β = 0.105, p < 0.05) and that the squared term of intra-industry stakeholder ties have a statistically significant negative effect on BMI (β = − 0.171, p < 0.01). Second, we split the data in half based on the turning point, computed as 0.307. We ran two multivariate regression analyses on the split data sets. The coefficient estimate of intra-industry stakeholder ties for the values less than or equal to 0.307 was positive (β = 0.149, p < 0.05) and the coefficient estimate for the values greater than 0.307 was negative (β = − 0.238, p < 0.1), indicating that both the positive and negative slopes of the curve were statistically significant (Chuang et al., 2018). Third, we plotted the inverted U-shaped relationship as shown in Fig. 2. The turning point of 0.307 fell well within the data range of the centered intra-industry stakeholder ties (− 1.712 to 1.287). Thus, the conditions were satisfied, supporting H1.

Abstract

     This paper explores the role that IT capability (IT-c) plays in firm innovation performance through the channel of organisational learning (OL) in Kenya. It frames OL in two dimensions: explorative and exploitative OL. The former entails seeking new knowledge, which mainly exists outside the firm’s competence. The latter is an activity or process that builds on existing competence and knowledge in the firm. Using mediation analysis of 481 firms drawn from the World Bank Enterprise Survey (2013) and Innovation Follow-up Survey 2014, it demonstrates that IT-c has a significant effect on innovation performance of firms (capability to simultaneously improve products, processes, organising and market development). It finds that the mediative role of OL in the relationship between IT-c and innovation performance is realised mainly through explorative learning, whilst enabling the firm exploit existing (in-house) knowledge base. The paper puts forward some managerial, policy and further research suggestions.

Introduction

     Information technology (IT)Footnote1 is changing how businesses are conducted and holds promise for firms. The deployment of IT in business is evident in the shift in business activities towards its use to create value in new ways for the firm and customers (Gobble, 2018). Whilst recognition of the adoption of IT amongst firms is not an issue, the fundamental questions arise in the empirical establishment of the business value of IT and the mechanisms through which it is realised in firms (Kuusisto, 2015). The business value or performance effect of IT refers to its benefit(s) for firms as may be captured in various ways, for example improvement in product development and process improvement, return on asset or any economic added value for the firm that derives from IT (Bharadwaj, 2000). In the context of this study, the business value of IT in focus is the contribution of IT to “product, process, organisational and marketing improvement”, for which this study applies the term innovation performance.

     Earlier studies provide evidence for the performance effect of IT (Bharadwaj, 2000; Wade & Hulland, 2004). However, it is insufficient to identify its direct impact on firms, without the complementary resources that account for the impact (Brynjolfsson & Hitt, 2000). Lee et al. (2014) support this position. They argue that there is evidence for the effect of IT on firms, but “we still have limited understanding about the process through which IT contributes to business value” (p. 111). This is rooted in the thinking that IT has little or no significance for firms if not integrated with firms’ resources and processes. An established firm process that accounts for differences in firms’ performance is organisational learning (OL).Footnote2 Accordingly, Tippins and Sohi (2003) argue that OL could be a missing link in the business value of IT. OL is important because it is a source of competitive advantage. Firms that learn are able to gather information about the market and tend to be more flexible hence can respond to changes in the business environment (Jiménez-Jiménez & Sanz-valle, 2011). Such firms are also able to build on their existing knowledge and modify internal practices to improve product development, process and find new markets. This positions OL as a key mediator of the impact of IT on firms.

Conclusions and Reflections

     This study sets out to explore the role that IT capability (IT-c) plays in firm innovation performance through the channel of organisational learning (OL) in Kenya. It establishes that IT-c has a significant effect on innovation performance of firms, enhancing their capability to simultaneously improve products, processes, organising and market development. This effect is mainly realised through the OL orientation of firms. It finds that the mediative role of OL in the relationship between IT-c and innovation performance is realised mainly through explorative learning in form of research-oriented and experimentation-oriented learning whilst enabling the firm exploit existing knowledge. The study has managerial and policy implications.

     Top management who make decisions on investment in IT such as software acquisition, website development and other shared platforms and IT skills should think of integration with internal processes. IT-related investment and activities must take into account the competence and practices that encourage research, staff training and re-using knowledge for innovation. The business value of IT lies in its capability to enhance these activities and practices. Investing in IT infrastructure whilst keeping organisational culture that discourages new ideas can weaken the expected outcome.

1- Introduction

2- Theoretical framework and hypotheses

3- Data and methods

4- Results

5- Discussion

References

Abstract

We investigate how human beliefs are associated with the absorption of specialist knowledge that is required to produce cyber-security. We ground our theorizing in the knowledge-based view of the firm and transaction-cost economics. We test our hypotheses with a sample of 262 members of an information-sharing and analysis center who share sensitive information related to cyber-security. Our findings suggest that resource belief, usefulness belief, and reciprocity belief are all positively associated with knowledge absorption, whereas reward belief is not. The implications of these findings for practitioners and future research are discussed.

Introduction

For both public and private organizations, effective cyber-security is required to prevent business interruption and thus to ensure operational continuity (Fransen, Smulders, & Kerkdijk, 2015; Furnell & Clarke, 2012; Gordon, Loeb, & Zhou, 2016; Luiijf & Klaver, 2015; Skopik, Settanni, & Fiedler, 2016; Tounsi & Rais, 2018). The production of such cyber-security is a knowledge-intensive task (Ben-Asher & Gonzalez, 2015; Jakobson, 2011). Despite the fact that hardware and software components required for this defense are relatively homogeneous and readily available at low cost or even for free (Anderson, 2001; Hofmann & Ramaj, 2011), highly specialist knowledge is required to combine and deploy these components effectively for organizational defense — for instance, by designing resilient systems architectures and implementing them efficiently (Etzioni, 2011; Lee, Bagheri, & Kao, 2015). Hence, cyber-security is a complex capability that is not readily created by the purchasing of technological components; rather, it is the skilled knowledge of how to organize and orchestrate these components that creates the actual defense (Anderson, 2001; Hofmann & Ramaj, 2011; Solms & Niekerk, 2013). Furthermore, due to the swift technological evolution and short technology life-cycles of these components, knowledge required to produce cyber-security becomes obsolete (Casas et al., 2017; Chen, Chiang, & Storey, 2012; Mahmood & Afzal, 2013; Wang et al., 2014).

1- Introduction

2- Adoption of e-government innovations: a review of the historical development of e-government

3- Conceptual framework of a knowledge vacuum in e-government innovation

4- A case of attempted e-government system innovation

5- Discussion

6- Conclusion

References

Abstract

In spite of attempting to implement e-government innovations to enhance efficiency in public organizations for several decades, e-government innovation has often not met the expectations of citizens, legislatures, or the organizations. Although a wide range of causes from poor planning to improper implementation have been suggested for explanation of failures, it is still challenging to theoretically construct an explanation of what the overarching dynamic is behind those causes. To further develop the understanding of the conditions of unrealized benefits of e-government innovation, we propose a conceptual framework of a knowledge vacuum, which is an organizational condition in which excessive exploration and organizational inertia interact to create a vicious cycle of low performance. We first review the history of e-government and factors that affect the success and failure of e-government innovation. Next, we develop the conceptual framework, and apply the concept to review an e-government innovation failure case for an illustrative purpose. We conclude by discussing theoretical and practical implications of the conceptual framework and its limitations in understanding the current state of e-government innovations.

Introduction

The public sector has been attempting for several decades to implement e-government innovations in order to improve the effectiveness, efficiency, responsiveness and creativity in public organizations (Chen & Perry, 2003; Coursey & Norris, 2008; Moon, 2002; Wood, Bernt, & Ting, 2009). However, there are many cases of low performance and failures, and many examples of e-government implementation that have not met the expectations of citizens, legislatures, or the organizations (Anthopoulos, Reddick, Giannakidou, & Mavridis, 2016; Askim, Christensen, Fimreite, & Laegreid, 2010; Holmes, 2005; Morgeson III & Mithas, 2009; Moynihan & Lavertu, 2012). When looking at the history of e-government innovations, scholars find that they have not been a well-planned, coherent movement; instead, they are rather fragmented attempts (Dawes, 2008) driven by different factors such as technological innovations, political pressure, or even precedent failures. New technologies as well as innovative information, management and communication approaches seem to provide optimism, but until recently scholars still remain cautious. Causes of these broken promises have been investigated by scholars, from the planning to the implementation stage across the domains of technology, management, regulation, and environment (Gil-Garcia & Pardo, 2005). Although the literature suggests which factors may help explain e-government success and failure, it is still necessary to design a comprehensive theoretical framework through which we can more systematically understand the relationship among causes of e-government success and failure. One venue to develop a theoretical framework is to elucidate the dynamic nature of e-government innovations through the learning perspective. Innovation research has been closely related to the organizational learning literature (Borins, 2001; Brown & Brudney, 2003; Cohen & Levinthal, 1990; Moynihan & Lavertu, 2012; Salge, 2010). Recent policy implementation research has also paid increasing attention to the role of organizational and social policy learning to better understand the dynamic of policy decisions, implementation and evaluation (Bennett & Howlett, 1992; Dunlop, 2017; Hall, 1993; Howlett, Ramesh, & Perl, 2009; May, 1992; Moyson, Scholten, & Weible, 2017). Both literatures emphasize that learning lies at the center of the dynamic aspect of the causes of innovation success and failure.

Knowledge entrepreneurship is increasingly becoming important in driving innovation for high levels of competitiveness. The purpose of this study was to investigate the relationship between Organization Learning (OL) and Innovation Output (IO) for improved performance in manufacturing firms in Kenya. The theoretical underpinnings on this study are the Schumpeter’s (1934) innovation theory of and the Gleick (1987) complexity theory. The methodology used was mixed method research because it provides a more holistic understanding of a thematic area. The research design that was used is cross-sectional design because it allows for making observations on different characteristics that exist within a group at a particular time. The target population was manufacturing firms across the country. Multi-stage sampling strategy was used to sample 303 respondents from 101 firms. Primary and secondary data were used to collect both qualitative and quantitative data. The questionnaire, interview schedule and a checklist of key informants were used to collect data. Content validity was used to ascertain the credibility of the research procedure and internal consistency technique was used to test for reliability. Correlation and linear regression were used to determine the relationship between OL and IO. Work disruptions were avoided by making prior arrangements and appointments. The findings indicate that OL has a significant influence on IO. It is recommended that lifelong learning, management support and risk tolerance should be encouraged to improve creativity. High creativity is important in raising the capacity to integrate internal and external knowledge for greater levels of IO. Further research should be carried out to find how customers and suppliers information can be utilized to enriched OL.

Introduction

Innovation utilizes knowledge which is important in raising creativity and capacity development for enterprise prosperity. Many countries have developed their National Innovation Systems (NIS) and have a comprehensive innovation policy framework, but most firms have not leveraged on these opportunities to raise their Innovation Output (IO). This has been contributed by the disjointed relationship between research institutions and industry. The situation has been brought about by multiplicity of new institutions that have become a barrier to knowledge sharing and thus firms are shying away from intense collaboration with research institutions and universities which has led to declining knowledge absorption, creation and diffusion which are key components of innovation performance (Cornell University, INSEAD & WIPO, 2015). The situation can be addressed by rallying firms to develop their knowledge capacities by focusing on Organization Learning (OL) for greater IO.

Introduction

Literature review

Hypotheses development

Research methodology

Data analysis

Results

Discussion

Theoretical implication

Practical implication

Limitations and suggestions

Conclusion

References

Abstract

Purpose: The objective of this study is to see the mediating effect of innovative culture and organizational learning between leadership styles and organizational performance in Malaysian SMEs. Design/methodology/approach: Questionnaires were used to collect data from owners/managers and 950 questionnaires distributed by using postal and email strategy. Out of 950, only 409 questionnaires returned back, 25 questionnaires have missing values and eliminated from a sample, and only 384 questionnaires were used for final analysis.
Findings: Findings reveal that leadership styles have a significant influence on organizational learning, innovative culture, and organizational performance. Innovative culture and organizational learning have a significant influence on organizational performance. Moreover, innovative culture and organizational learning significantly mediate between leadership styles and organizational performance.
Practical implications: In practical term, the current study contributes for managers/ owners in SMEs to focus leadership styles with innovative culture and organizational learning in their decision making if they want to enhance their organizational performance.
Research originality: This study is one of the pioneer studies that test leadership styles at third-order with the combination of two mediating variables innovative culture and organizational learning with organizational performance.

Introduction

Effective leadership plays an important role to determine the success or failure of any kind of organization (Tourish, 2014). In the last few decades, leadership is the most studied area in business as well as in industry. Moreover, leadership has become a compulsory part for organizations (Bryman, 2007; Brymer & Gray, 2006). Despite the wider quantity of studies on leadership, there has been scant research done on small and medium-size enterprises (SMEs) (Franco & Matos, 2015). A highly competitive market and rapidly changing strategies enhance the importance of leadership in attaining a competitive edge over their competitor through leaders to enhance organizational performance (Jaramillo, Mulki, & Marshall, 2005). The primary purpose of organizations is to focus financial efficiency but also considers their intangible resources and leadership to maintain competitive advantage (Wang, Chich-Jen, & Mei-Ling, 2010). Literature demonstrates that Malaysian SMEs faces issues regarding leadership styles that reduce organization performance (Aziz, Abdullah, Tajudin, & Mahmood, 2013; Hashim, Ahmad, & Zakaria, 2012; Rahim, Zainal Abidin, Mohtar, & Ramli, 2015). Prior studies suggested that effective leadership can help to improve the organizational performance in that situation where organizations face a lot of new issues and challenges (Franco & Matos, 2015; McGrath, Mac Grath, & MacMillan, 2000). Leadership styles have an influence on both individual and organizational performance (Wang et al., 2010; Windsor, 2009). Sometimes firms do not identify the ineffective or effective leaders until time passed and organizations suffer (Nazarian, Soares, & Lottermoser, 2017). Leadership styles are a vital element that explains organizational performance. Hence, this study focuses on leadership styles to determine SMEs performance.

The business model canvas (BMC) and the lean start-up manifesto (LSM) have been changing both the entrepreneurial education and, on the practical side, the mindset in setting up innovative ventures since the burst of the dot-com bubble. However, few empirical insights on the business model implementation patterns that distinguish between digital and non-digital innovative ventures exist. Connecting practical management tools to network theory as well as to the theory of organizational learning, this paper investigates evolution patterns of digital and non-digital business models out of the deal flow of an innovation intermediary. For this purpose, a multi-dimensional quantitative content analysis research design is applied to 242 ventures’ business plans. The measured strength of transaction relations to customers, suppliers, people, and financiers has been combined with performance indicators of the sampled ventures. The results indicate that in order to succeed, digital ventures iterate their business on the market early and search for investment afterwards. Contrariwise, nondigital ventures already need financial investments in the early stages to set up a product ready to be tested on the market. In both groups we found strong evidence that specific evolutionary patterns relate to higher rates of success.

Introduction

Digitalization has been the driver of economic change, with ups and downs in all industries, since the end of the last century. The development of new options in information technology allowed newcomers to challenge existing value chains with innovative and often disruptive business models. In the days when the dot-com bubble burst, a serious backlash was directed at the digitalization of the economy, due to misdirected investments into unproven business models (Ayres and Williams, 2004; Drori et al., 2009; Min et al., 2008). As a consequence, it seems that the business model as a management tool suddenly went out of fashion, as judged by the publication statistics of Google Scholar and other, similar research (Al-Debei and Avison, 2010; Magretta, 2002). Initiated once again by the publication of Osterwalder’s Business Model Canvas (BMC) and Eric Ries’ Lean Startup Manifesto (LSM) at the end of the first decade of the 21st century, a new way of thinking in the field of digital entrepreneurship diffused into practice (Chesbrough and Rosenbloom, 2002; Frederiksen and Brem, 2017; Osterwalder and Pigneur, 2011; Ries, 2011). In the combination of both approaches, Steve Blank recommended that the business model evolves based on assumption testing to become a better mechanism of resource combination. He recommends developing and testing hypotheses behind each building block of the BMC in an iterative process while implementing the business (Blank, 2013; Blank and Dorf, 2012).

1- Introduction

2- Theoretical background and hypotheses

3- Partnering experience and contractual complexity

4- Methods

5- Measures

6- Results

7- Discussion

8- Conclusions

References

Abstract

This research investigates the learning of inter-organizational contract design in greater depth. Two types of learning, i.e. learning from all past partnerships and learning from one specific partner, are distinguished in terms of their influence on the complexity of three different functions of the contract, namely control, coordination, and adaptation. Contract design capability and interorganizational routines are employed as mediators to explain the two types of learning respectively. Empirical tests using data from the Chinese construction industry reveal that there are significant indirect effects between partner-specific experience and contractual coordination, and between general partnership experience and all the three functions of the contract. This research contributes to the literature by providing more nuanced conclusions regarding the contract learning issue.

Introduction

It has long been acknowledged that firms can develop various capabilities through experience for superior performance (Kale & Singh, 2007; Levitt & March, 1988; Sampson, 2005; Zollo, Reuer, & Singh, 2002). Whilst the strategic management and organizational literature mainly focus on how firms accumulate and leverage know-how and enhance alliance capability to achieve success, less research has examined learning related to contract design in interorganizational relationships compared with the extensive literature on organizational learning regarding technical knowledge and skills (Lumineau, Fréchet, & Puthod, 2011). The contract serves as a formal governance mechanism and plays an important role in controlling deviant behavior, mitigating potential transaction hazards, and ensuring the realization of organizational performance (Lu, Zhang, & Zhang, 2016; Luo, 2002; Mellewigt, Madhok, & Weibel, 2007; Poppo & Zenger, 2002; Wang, Chen, Wang, & Tang, 2016). Considering that firms tend to absorb prior partnering experience to facilitate contract design (Argyres & Mayer, 2007), this research aims to investigate such processes in more depth from an organizational learning perspective. The extant literature on contract design learning has investigated the impact of firm’s prior partnering experience on contractual complexity, but the scholars seem to have suggested more than straightforward conclusions. Specifically, some studies take the view that prior partnering experience will lower the costs of contracting through learning, leading parties to draft more complex subsequent contracts (Mayer & Argyres, 2004; Mellewigt, Decker, & Eckhard, 2012; Ryall & Sampson, 2009; Xing, Mayer, & Xie, 2015), while some others focus on the reduction of behavior uncertainty and the development of trust through repeated collaboration, drawing a conclusion that subsequent contracts tend to be less complex (Ariño & Reuer, 2005; Reuer & Arino, 2007).