Sharpening Key Capabilities and Illuminating Integrative Ways Towards Business Model Innovation

doi:10.21203/rs.3.rs-1592866/v1

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Sharpening Key Capabilities and Illuminating Integrative Ways Towards Business Model Innovation

https://doi.org/10.21203/rs.3.rs-1592866/v1

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Firms strategically improve, promote and sustain competitive advantages through businesses model innovation (BMI). While present research largely focuses on the context of sustainability and BMI from different aspects of business, relatively few studies conduct in-depth analysis of what key capabilities a firm needs to achieve BMI. Meanwhile, although progress has been made in understanding the domain of BMI, present studies often relied solely on either innovation culture or innovation policy. Little is known about the effects of the combination of these two parameters on BMI outcomes. Therefore, this study firstly identifies a firm’s key capabilities that are required towards BMI. Additionally, to examine the compound effect on the indirect relationships between a firm’s key capabilities and BMI outcomes, this paper simultaneously adopts innovation culture as a mediator and innovation policy a moderator. A structured questionnaire was designed and 287 valid samples were collected from a population comprised of 948 listed companies on Taiwan Stock Exchange in 2020. Hypotheses were tested using the structural equation modeling (SEM) in SPSS AMOS for direct path analysis and Process Model 5 for indirect effects. The results show that three key capabilities, including Architecture, Reputation and Innovation, are crucial for a firm to accumulate over time in reaching BMI. The findings also suggest that internal factor of innovation culture and external factor of innovation policies go hand in hand to accelerate firms to reach BMI. The outcomes may assist public authorities and company managers with a useful guideline to make relevant policies and govern businesses.

Businesses Model Innovation

Innovation Culture

Innovation Policy

Key Capabilities-Architecture

Reputation and Innovation

In recent decades, businesses have experienced that their products may rapidly enter the decline stage (Bayus, 1994). An early survey conducted by the Economist Intelligence Unit found that more than 50% of business executives believe that business model innovation (BMI) rather than just product and service innovations may play a critical role if companies are to maintain competitiveness (Johnson et al., 2008). Indeed, after reviewing real cases, including Wal-Mart in retailing, Dell in PCs, and Ryanair in airlines, Chesbrough (2007) reached a conclusion that BMI could be a conceptualization, a process, an outcome, and performance, which is more significant than the initiatives of new product/service development (Foss & Saebi, 2017).

Business model (BM) can no longer be static and it has become a must for all firms to re-think, re-design and innovate BM to sustain competitiveness over time. While most companies searching for product/service innovation, others concentrate on different stages of the organizational process innovation (Frankenberger et al., 2013). The key drivers of innovation come from external stimulus that makes a firm inevitably adopt strategic approach, along with the internal capabilities that are critical for the firm to reach the goal (Lüftenegger et al., 2011). Several studies have revealed a firm’s dynamic capabilities that lead to BMI, and various aspects have been explored due to their latent characteristics, including entrepreneurial capabilities (Teece, 2007), leadership capabilities (Lessard et al., 2016), managerial capabilities (Adner and Helfat, 2003) and complementarity capabilities (Makri et al., 2010). The success of BMI heavily depends on a firm’s capabilities, including the internal team composition and the external innovation networks (Esau, 2020). To the extent, the former shows the inside-out capabilities to shape innovation culture, and the latter shows outside-in forces to develop innovation policies. The question of whether both internal and external factors are entwined needs to be researched.

To a large extent, some scholars attributed BMI as a necessary response to the external fact of the accelerating pace of the business environment and intense global competition (Johnson et al., 2008; Voelpel et al., 2004; Doz and Kosonen, 2010). Other researchers highlighted the internal dynamic capabilities of a firm to maintain competitiveness, such as the ability to sense the threats or opportunities in the competitive environment so as to develop BMI (Zott et al., 2011; Teece, 2007; Saebi and Foss, 2015). Despite a great attention to a variety of variables regarding BMI, another challenge viewpoint involves the macroeconomic matrix that may constrain and enable the capabilities of BMI. Therefore, an important question is of how innovation policies affects the capabilities of BMI.

More specifically, innovation policies in macroeconomic business environment are mainly shaped under the consideration of national resources and economic development (Borrás and Edquist, 2013), which are made to improve industry-science relationships (De Silva et al., 2018). Studies show that innovation policies may serve as a source of a firm’s distinctive capabilities and also account for a moderating role to accelerate the development between industry business capability and national economy, including the Taiwan government targeting the system-on-a-chip (SoC) industry (Shyu and Chiu, 2002), German's innovation policies for biotechnology (Kaiser and Prange, 2004), and entrepreneurship policy in EU facilitating to create innovative small- and medium-sized enterprises (SMEs) and in turn increase economic growth (Dahlstrand and Stevenson, 2010). In this respect, numerous researchers focus on the roles of antecedent and outcome variables play in BMI domain (Souto, 2015; Yunus et al., 2010; Witell and Löfgren, 2013; Schneider and Spieth, 2013; Karimi and Zhiping, 2016), while little literature about the moderators has been discussed.

In a nutshell, this paper aims to examine the key capabilities that a firm needs towards its BMI as this process is also indirectly influenced by innovation culture (i.e. mediator) and by innovation policy (i.e. moderator).

Capabilities of a firm are mainly manifested in several dimensions, including distinctive and hard-to-duplicate resources (Rumelt et al., 1991), assets, skills and accumulated knowledge (Leinard-Barton, 1992), and organizational processes (Day, 1994). Distinctive capabilities of a firm along with its resources and strategies are essential to lead to competitive advantage; however, the scale and effect vary for different types of companies (Lorenzo et al., 2018). As a great number of companies with key capabilities either lag behind in performance or go bankruptcy every year, more attention is needed in mediating or moderating factors. Little is discussed in this regard and therefore this paper addresses this research gap focusing on innovation culture and innovation policy. With sufficient and efficient policy supports from the governments, firms with strong innovation culture may effectively develop BMI in a dynamic and competitive manner over time.

Dependent Variable: BMI Outcomes

BMI can be “a matter of architectural or modular change associated with different degrees of novelty” (Foss and Saebi, 2017, p. 216). Firms staying with sustaining innovations improve products and services with various dimensions of performance, while others adopting disruptive innovations offer novel features that try to reach underserved customers (Markman and Waldron, 2014; Christensen et al., 2018). Both approaches of BMIs can be applied to the design of the business to create substantial value and competitive advantages. With rapid development of information technology, firms can further approach BMI by redefining models with new value propositions and effectively create and capture value for customers (Bock et al., 2012), including revenue model innovation (Spieth and Schneider, 2016; Osterwalder et al., 2010), market model innovation (Johnson, 2010), industry model innovation (Venkatraman and Henderson, 2008), technology-driven innovation (Chesbrough, 2010), and value architecture innovation (Spieth and Schneider, 2016).

BMI constantly takes place in response to changes in the competitive environment, and companies need diverse degrees or modes of implementation of BMIs in order to maintain competitive advantage from their competitors (Foss and Saebi, 2017; Andreini and Bettinelli, 2017; Winterhalter et al., 2014), such as offering novel products or services in appropriate time (Zhu et al., 2017), improving information technology infrastructure (Minatogawa, 2018), changing organizational inertia and leadership (Huang et al., 2013), building BMI culture and value chain creation (Eichen et al., 2015), developing strategic management (Gatautis et al., 2019; Andreini and Bettinelli, 2017), adapting to the external dynamic environment (Foss and Saebi, 2017), organizational process (Frankenberger et al., 2013), and promoting efficiency-centered BM designs (Wei et al., 2014). With various BMIs, firms often tend to convey signals as to their innovations to the market with patents (Hirshleifer et al., 2013). Patents, therefore, become an important non-financial indicator to evaluate BMI outcomes in this study.

Firms making strategic plans to innovate existing BMs sustain competitiveness and improve profits (Kapoor and Klueter, 2015), and BMIs are found to positively influence the business performance (Zott and Amit, 2007; Cucculelli and Bettinelli, 2015). Overall positive performance of a company is one of the most crucial factors in managing business, and might be adequately measured by primary or secondary data of its financial indicators (Venkatraman and Ramanujam, 1986). Among other things, Zott and Amit (2007) proposed that several dimensions of firm performance can be conceptualized as market value, market share, company profit, or sales growth. Consequently, BMI outcomes can be measured by the financial indicator of company profit.

Independent Variable: Key Capabilities of a firm towards BMI

Whether business successes or not is mainly determined by the match of a firm’s capabilities to the challenges in the business environment. Kay (1993) proposed three categories of distinctive capabilities that may help firms reach sustainability, including firm architecture, reputation, and innovation. This paper integrates these three variables as key capabilities of a firm to analyze their roles in approaching BMI.

Architecture

Architecture refers to a capability of a firm to develop stable and continuing relationships between itself and the desire of the parties involved. A firm with customer-focused and value-added architecture helps generate multiple benefits and manage risk. Henderson and Clark (1990) suggested that firms make significant change process in understanding the way to create the opportunities of mastering architectural innovation, a practice that changes the architecture of a product, a system, or entire industries (Garud et al., 2013). A recent research on BMIs for 15 years has reached a conclusion that firms through organizational change process (either new or innovative BMs) require capabilities such as leadership, technologies, value networks and learning mechanisms (Foss and Saebi, 2017).

Additionally, numerous researchers examined the positive outcomes of the organizational change process in different industries, including banking (Yunus et al., 2010), manufacturing (Witell and Löfgren, 2013), aviation (Schneider and Spieth, 2013), electric mobility (Abdelkafi et al., 2013), newspapers (Holm et al., 2013; Karimi and Zhiping, 2016), tourism (Souto, 2015), and energy (Richter, 2013). For most firms, the success in competitive markets depends largely on capabilities of change process in different aspects of architecture (Spieth and Schneider, 2016; Aiginger, 2006; Chang and Shih, 2004; Intarakumnerd et al., 2002; Sharif, 2006).

It appears to be difficult to highlight architecture due to its latent and broad characteristics, including a firm’s products, processes, governance, structure and information requirements. However, after studying architectural innovation more than 20 years, Henderson (2021) suggested that the key determinant to measure a firm’s architecture is its commitment to a shared purpose. A strong commitment of shared purpose plays a significant role in identifying new opportunities and accordingly builds strategic alignment to addresses them (O’Reilly and Tushman, 2011). Firms embracing a shared purpose can increase their ability to tackle architectural innovation effectively (Henderson, 2021), and in turn experience better performance (Gartenberg et al., 2016).

Based on the above literature review, the measured items of variable Architecture in this paper adopts Purpose-Clarity questions designed in a study that systematically constructs a measure of corporate purpose by aggregating four of the survey questions, including “Management has a clear view of where the organization is going and how to get there,” “I am given the resources and equipment to do my job,” “I feel good about the ways we contribute to the community,” and “I'm proud to tell others I work here.” (Gartenberg et al., 2016)

Reputation

Reputation helps firms to reduce information asymmetry and uncertainties (Boyd et al., 2010), which significantly affects normal business activities (Ecker et al., 2013). Managers holding value information can lead to better decisions (Connelly et al., 2011). Participants in the marketplace have different abilities to assess the information of inputs or outputs, and then transform it into knowledge. While knowledge of value-in-use of goods, services, or resources enables innovative outputs, reputation in this regard plays an essential role for helping firms trade value information with each other (Makadok, 2011).

Signaling theory is applied as well to explain the influence of information asymmetry between interacting parties with regard to reputation. Firms attempt to build a positive reputation over time as a signal in order to demonstrate their qualities so that they are likely to receive more value information throughout interorganizational ties (Park and Mezias, 2005; Gulati and Higgins, 2003). Given that strong signals of quality lead to the reduction of information asymmetry, firms with good reputation interacting with different entities may engage in any type of exchange relationship (Connelly et al., 2011). Good reputation may therefore help firms develop alliance networks (Ozmel et al., 2013) and participate in strategic alliances (Park et al., 2002). Consequently, companies with a better understanding of their resources can utilize the relationship advantage to accurately evaluate the basis of BMIs (Schmidt and Keil, 2013). Kraaijenbrink et al. (2010) indicted it is the reason why some firms outperform others.

It is a costly and risky activity for a firm in attempt to establish good relationships with outsiders. For the costly part, each actor concerns about the potential costs such as the possibility that the actual benefits/cots are lower/higher than expected, and the opportunity cost of relationships with others. For the risky part, a firm faces uncertainty regarding the qualities and intentions of the potential partners whom it will share a tie with. A reputation signal is therefore useful for actors with minimum cost to scrutinize the targeted firms. Likewise, firms may leverage the reputation they have built to develop an effective coalition with prestigious actors who attest to their quality (Plummer et al., 2016), which may form an intangible asset both in value and contribution toward BMIs.

The desire and willingness of one firm to develop collaborative relationship with the other party is largely due to its reputation that might signal trustworthiness and provide invaluable information (Nielsen, 2004). The former results in the reduction of the alliance transaction cost such as the continued search and bonding expenses (Hennart, 1993; Hoelz and Bataglia, 2021), and the latter has often regarded as critical reason why some firms outperform others (Kraaijenbrink et al., 2010). From this perspective, firms have a better understanding of their resources and knowledge advantage to correctly assess their ways towards BMI (Schmidt and Keil, 2013). In this paper, therefore, the measured items of variable Reputation are built upon the strategic partnership; that is, the number of business partners a firm works together.

Innovation

Innovation may refer to a process that involves the invention or the execution of new techniques, methods and practices that can lead to new products, services and procedures (Utterback, 1971), as well as a way of delivering business value, maintaining competitive advantages and improving a firm’s performance (Damanpour, 1991). In recent years, the subject of innovation capability has attracted considerable researchers since it has become a vital element for a firm to main competitiveness (Samson et al., 2017; Chang et al., 2017). Due to its complex patterns and dynamic characteristics, numerous studies specifically focus on innovation capability from the perspective of knowledge management, indicating knowledge is a valuable strategic resource and the essence of the innovation process (Romijn and Albaladejo, 2002; Kogut and Zander, 1992; Lawson and Samson, 2001; Ngo and O’Cass, 2013; Quintane et al., 2011; Weber and Heidenreich, 2018).

Innovation capability is highly related to knowledge-based activities (Terziovski, 2010; Subramaniam and Youndt, 2005; Dougherty, 1992), as knowledge is the main source for innovation and business advancements (Block et al., 2013; Johannessen et al., 1999). As one of the critical capabilities to reinforce or extend a firm’s competitiveness, knowledge-based activities become essential sources to stimulate the business innovation process and therefore contribute to a rapid pace of advancement in innovation (Powell and Snellman, 2004; Subramaniam and Youndt, 2005). Those intellectual capital and intangible assets are the keys to competitive advantage in the knowledge economy environment. Nevertheless, the benefits of innovation are not long obtained by the innovating firms because competitors duplicate it unless a support policy or standards can be established.

Researchers proposed that the measured indicators of the innovation capability can be the R&D expenditures, patent propensity, and the degree of organization of R&D activities (Herrera et al, 2010). Other empirical studies mainly adopted patents as knowledge-based outcome measures of innovation (Jansen et al., 2006; Brockington, 1996; Willigan, 2001; Schilling and Phelps, 2007), reflected in the intensity and quality of patents registrations (Lopes and Martins, 2006). Patent registration is a fair basis that can capture and measure this intangible asset (Willigan, 2001; Johnstone et al., 2010). The invention covered by a patent should be deemed capable of industrial application. Usually, patents are included in one of the following categories: product, process, machine, composition of matters, software, and articles of manufacture. Therefore, the measured items of variable Innovation in this paper are mainly built upon the dimension of patents that a targeted firm has accumulated in the past decade in their business field.

Based on the above literature review of key capabilities composed of three components (i.e. Architecture, Reputation and Innovation), this paper proposes

Hypothesis 1

(H1). Key Capabilities have significant effects on BMI Outcomes.

The Mediating Variable: Innovation Culture

In spite of diverse perceptions of BMs that can be adopted, numerous firms are failing in innovation most of the time (Geissdoerfer et al., 2018). A major concern is associated with innovation culture. Although some researchers asserted that firms with strong corporate cultures would promote overall performance (Owen et al., 2001), others emphasize the importance of enhancing innovation culture that embraces innovative activities, motivates open communication of opinions and generates new or improved products, services or processes to achieve continuous innovation (Brettel and Cleven, 2011; Martín-de Castro et al., 2013).

Innovation culture is the foundation upon which all successful innovation must be built. As a major benefit of innovation is to lead firms to higher productivity (Maradana et al, 2019), some economic literature highlights the growth-enhancing role of innovation by emphasizing the different aspects of firms’ innovative activities that are determinants of divergence in innovation process (Verspagen, 1995; Abreu et al., 2010). To improve innovation culture, Dostie (2018) suggested that firms invest in human capital, particular on-the-job personnel training. Likewise, a cross-country report (Fialho et al., 2019) also showed that both non-formal and informal job-related training for innovation highly influence firms’ innovation activities and in turn lead to significant productivity to firms themselves. The provision of employee learning is an indication of a learning organization that being much more active in deploying innovation (Dobni, 2008).

Measures that need to be taken by both governments and companies to promote innovation activities are to increase spending on R&D coupling with adequate laws and regulations as well as investment in education (Glaeser and Hausman, 2019). At government level, policies regarding innovation efforts are crucial to generate firms’ BMIs, which will be explained as the moderator in next section. At firm level, a key element to enhance innovation culture towards BMI lies in accessing sources of knowledge and facilitate interactive learning in innovation (Moreno and Suriñach, 2014). This goal can be achieved through intentional relations, including (1) research collaborations across firms and institutions, (2) along with independent investment in R&D activities and (3) personnel training (Dostie, 2018; McCann and Ortega-Argilés, 2011). Questionnaire items are designed associated with these three indicators in this study and based on the above literature review, this paper proposes

Hypothesis 2 (H2). Innovation Culture mediates the relationship between

Key Capabilities and BMI Outcomes.

The Moderating Variable: Innovation Policy

The stability of macroeconomic environment has led to the development of different aspects of competitiveness for individual firms and in turn the nation as a whole, while microeconomic foundations of competitiveness need to consider the set of institutions, market structures and policies (Solleiro and Castañon, 2005). Consequently, governments use policy instruments as tools to influence firms’ innovation processes in order to achieve ultimate objectives such as economic growth (Borrás and Edquist, 2013).

Policy instruments to sustain the innovation system give the firms the possibility of accessing the capacities and expertise of other entities with the purpose of innovating (Westmore, 2014). Multiple policy instruments combined into mixes are necessary to support and promote the corporate innovation system, including financial incentives such as direct financial support measures (Kochenkova et al., 2016), regulatory measures such as offering incentives for companies by improving legislation on intellectual property (IP) (Meissner and Carayannis, 2017), supply-side support measures such as creating a favorable environment for generating new knowledge and developing network in innovation (Edler and Fagerberg, 2017), and demand-side promotion measures such as supporting development of innovation infrastructure to transfer the technologies and knowledge into the real sector of the economy (De Silva et al., 2018).

Innovation has been considered a key determinant of economic growth at a macro level (Schumpeter, 1911; Hausman and Johnston, 2014; Coad et al., 2016). Most of the existing studies have supported a positive relationship between innovation activities and economic growth both directly and indirectly (Hasan & Tucci, 2010; Agenor & Neanidis, 2015; Maradana et al., 2019; Akinwale et al., 2020; Mohamed et al., 2022). However, while generating BMIs can bring long-term benefits for both firms and an entire country, it may cause short-term disruption and therefore lead to resistance of incumbent interest parties to change. To maintain the sustainable development of innovation environment, the government's intervention becomes a necessity (Navarro et al, 2009; Diyamett, 2021). Consequently, sound innovation policies play a critical role for the promotion of firms’ BMIs, particularly in spending on R&D which needs be prioritized in national budgets (Veugelers, 2016), helping embedded ties in the development of networks across multiple sectors (McCann & Ortega-Argilés, 2011), and others such as laws of property rights, patent protections, contracts, and various incentives to invest (Broughel & Thierer, 2019).

The assessment of innovation policy effectiveness and its impact on a firm’s key capabilities is a complicated task. From a macro perspective, Edler et al. (2016) proposed three dimensions need to be considered, including an industrial specificity, institutional conditions and economic development in the country. From a micro perspective, potential determinants for a firm to adopt innovation policy support are its size, age, market structure, technological opportunities, and financial performance (Vlasova, 2019), while Simachev et al. (2017) focusing on direct funding and tax incentives. The positive effects of innovation policies on firm-level capabilities could be observed in several aspects, including successful cooperation with R&D organizations and universities, improvement of business competitiveness, market expansion, cooperation links, innovation processes, and better results of innovation activities (Roud & Vlasova, 2020).

For assessing the effectiveness of innovation policies, Marino et al. (2016) suggested to collect information on both the adoption of government support and the effect on innovation activities. Consequently, two questions in this regard are: (1) Did your firm adopt any measure of innovation policy support for innovation activities in 2010–2019? (2) Did your firm improve its capabilities toward BMIs and thus have effects on innovation performance few years after adoption of innovation policy support? Questionnaire items are designed associated with these two questions in this study and based on the above literature review, this paper proposes

Hypothesis 3

(H3). Innovation Policy moderates the relationship between Key

Capabilities and BMI Outcomes

This paper is to examine the relationship between the key capabilities of a firm and BMI outcomes, which is indirectly influenced by innovation culture (mediator) and innovation policy (moderator). The conceptual model is presented in Fig. 1.

Literature review and variable discussion were carried out in order to operationalize the constructs and build the questionnaire. The instrument finalized six constructs with total 18 items. Five-point Likert’s scale was utilized, ranking from 1 (strongly disagree) to 5 (strongly agree). Samples were randomly selected on the basis of a population comprised of 948 listed companies on Taiwan Stock Exchange (TWSE) in 2020. The structured questionnaire was distributed via email to the position “manager” of the targeted companies and this study collected adequate respondents until the minimum requirement of 278 sample size was met (Krejcie & Morgan, 1970). Initially, the questionnaire was distributed to 500 companies through emails obtained from the TWSE. Finally, the total number of valid respondents was 287.

The hypothesized model of this study uses structural equation modeling (SEM) techniques involving the analyses of both the measurement model and the structural model. For measurement model, reliability and validity are examined through several criteria, including adequacy (i.e. KMO-MSA > 0.5 and Bartlett's Test), reliability (i.e. Cronbach's alpha > 0.7 and CR > 0.7), and construct validity (i.e. AVE > 0.5) (Hair et al., 2011). For structural model, the path analysis assesses the direct effect of key capabilities on BMI outcome by using SPSS AMOS, and two indirect effects (i.e. mediating and moderating) are examined by using PROCESS Template Model 5 (Hayes, 2017). Criteria regarding confirmatory factor analysis (CFA) will be reviewed, including model fit (Chi-Square/df < 3) and fit indices (i.e. CFI > 0.9; RMSEA < 0.08; and AGFI > 0.90) (Schumacker & Lomax, 2016).

Measurement Model

Reliability and Validity. Table 1 shows that factor loading of all the questionnaire items by using principal component extraction are higher than 0.7 (0.743 ~ 0.972). All Cronbach’s alphas of the questionnaire items are higher than 0.8 (0.781 ~ 0.903). The value of KMO (0.827) and Bartlett’s Test (p < 0.001) also indicate that the factor analysis is useful. The paper fairly concludes that all items of the questionnaire show high degree of internal consistency and their factors are appropriate to be used for further analysis.

Table 1

Summary of the Measurement Model
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.				.797
Bartlett's Test of Sphericity Approx. Chi-Square				2075.021
Df				153
Sig.				.000
Construct		Factor
Independent Variables:	Item	1	2	3	4	5	6
Key Capabilities:
Architecture (AT) Reputation (RT) Innovation (IV)	AT1		.847
	AT2		.814
	AT3		.903
	RT1			.976
	RT2			.774
	RT3			.755
	IV1					.811
	IV2					.741
	IV3					.949
Dependent Variable
BMI Outcome (BMIO)	BMIO1						.770
	BMIO2						.851
	BMIO3						.868
Mediating variable
Innovation Culture (IC)	IC1				.883
	IC2				.812
	IC3				.799
Moderating Variable
Innovation Policy (IP)	IP1 .864
	IP2	.865
	IP3 .842
	Cronbach’s α	0.816	0.817	0.817	0.786	0.793	0.781
	AVE	0.721	0.726	0.701	0.677	0.702	0.681
	CR	0.886	0.888	0.874	0.862	0.875	0.865
Source: Developed by the author
Structural Model

Direct Effect. CFA shows that Chi-square/df (1.846), CFI (0.959), RMSEA (0.054), and AGFI (0.908) are indicating overall good fit of the model to the data. The path model in Fig. 2 illustrates that overall direct effect of Key Capabilities, including Architecture (0.166, p < 0.05), Reputation (0.034, p < 0.05), and Innovation (0.182, p < 0.05), on BMI Outcome exist. Therefore, hypothesis H1 is supported.

Indirect Effect. Table 2 shows the results of partial relationship analysis. For mediating effect, the output has statistical significance so KeyCapa was a positive and statistically significant predictor of the mediator InnoCul (β = 0.1757, SE = 0.0681, p < 0.001, 95% CI= [0.5007, 0.7687]). Therefore, hypothesis H2 is supported.

Table 2

**Output of the PROCESS macro for the Hypothesized Model**
	coeff	se	p	LLCI	ULCI
KeyCapa × InnoCul	.6347	.0681	.0000	.5007	.7687
KeyCapa × InnoPol	.1757	.0765		.0251	.3263

Source: Developed by the author

For moderating effect, the interaction between Key Capability and Innovation Policy was statistically significant (β = 0.6347, SE = 0.0765, 95% CI= [0.0251, 0.3263]) since the null of 0 does not fall between the lower and upper limit of the 95% confidence interval, inferring that Innovation Policy significantly moderates the indirect effect of Key Capability on BMI Outcome. Furthermore, Fig. 3 shows regression slopes of the relationship between KeyCapa and BMIO at 3 points along the scale of the InnoPol. The slope appears to be increasing as it moves from one Standard Deviation below the mean (-1 SD) on the InnoPol to one Standard Deviation above the mean (+ 1 SD) on the InnoPol, suggesting that the higher the level of implementation of innovation policy, the greater the effect of a firm’s Key Capabilities on BMI Outcome. The outcomes support H3.

The outcome is threefold. First of all, this paper identifies that BMI outcome is substantially affected by a firm’s three internal capabilities, including Architecture, Reputation and Innovation. Next, Innovation Culture plays a mediating effect on the relationships between the Key Capabilities and BMI outcomes. Finally, the effect of three Key Capabilities on BMI outcomes is conditional to the level of the moderating variable Innovation Policy. In other words, both input and output effects of Innovation Policy are critical in determining the degree of a firm’s innovative activities.

Theoretical Implications

Progress has been made in understanding the critical factors to maintain a firm’s competitive advantages, mainly concentrating on strategic analysis of the value chain (Zott et al., 2011). Nevertheless, recent research show that BMI is perhaps a more important variable than traditional views of product/service innovation to affect positive change in business (Chesbrough, 2007; Foss & Saebi, 2017). Adding to this current stream of research with the variable BMI that may give rise to outstanding competitive advantage, this paper bridges a research gap by identifying three key capabilities as the fundamental characteristics of a firm to begin with the incentive towards BMI.

Furthermore, in attempting for companies to build critical capability and thus affect strategic flexibility on the way to BMI, researchers take into consideration of either innovation culture or innovation policy as an antecedent, mediator, or moderator, respectively. Nevertheless, numerous firms in reality around the world are still failing in BMI most of the time even if they have either developed innovation culture or received supports from innovation policy. Little has been discussed regarding the compound effects on BMI by combining the internal factor of innovation culture and the external driver of innovation policy. Therefore, this study fills the gap of which these two approaches need to develop parallelly in order to accelerate the success of BMI.

Practical Implications

This study identifies innovation culture as a mediating factor to highly impact BMI outcome. At firm level, leaders have a responsibility to establish the beliefs of the company, to improve employee engagement, to create innovative environment and atmosphere, and to reinforce behaviors that reflect those values. Productive leadership may shape strong corporate culture spreading throughout the workplace. A strong corporate culture significantly influences the thinking and behavioral styles of members, and therefore is able to promote overall performance (Owen et al., 2001). In addition to just an overall strong corporate culture, top management should develop adequate technical policy to specifically foster a culture of innovation that lead to more radical process innovation. A fine study focusing on the concept of organizational creativity indicated that organizational innovation can take place by individuals working together as members know of innovation culture supportive of creative behavior such as creativity training programs (Woodman et al., 1993). When innovation culture supports risk-taking behavior, individual creative performance will be increased.

On top of the innovation culture, BMI outcome indeed is the complex product of the creative outputs of the firm’s contextual influences, including the cross-level interactions among individual, group, and organizational characteristics (Woodman et al., 1993). Studies from traditional perspectives showed that reward systems, especially monetary reward, are the most common and useful ways to enhance an individual’s creativity (Amabile, 1983; Abbey & Dickson, 1983). When creativity becomes the major concern in the domain of innovation culture, future studies may explore more topics such as what types of leadership create efficient innovation culture, and what personality traits of employees generate more individual creative performance.

Furthermore, this study recognizes innovation policies as a substantial moderating factor to influence BMI outcome. The result delivers a significant signal to policy makers to further evaluate the effective use of a nation’s resources in terms of the government budget, social circumstances and the quality of the business environment to generate opportunities for the creation of innovations. At government level, while financial support such as R&D tax incentives, direct funding and subsidies from governments, and patent rights are found to be successful (Westmore, 2014; Simachev et al., 2017), innovation policies without excessive incentives or sufficient funding support may deactivate a firm’s motivation towards BMI. Since the amount of the budget plan largely depends on a nation’s economic development, further studies can focus on the questions of innovation policies largely weigh on which variable of macroeconomics (e.g. GDP), and what proportion of government budget on innovation policies is optimal.

Moreover, non-financial supports from the governments such as the intermediary organizations may play an important role to connect firms with different valuable partners (Tavani et al., 2018). Collaborative innovation involves multiple participants, including universities, research institutions and enterprises, to enhance innovation performance (Teece, 1986; Persaud, 2005). As evidence shows that collaborative innovation networks improve a firm’s innovation capability (Davis & Eisenhardt, 2011), the obstacle of finding various value partners may significantly increase transaction costs and therefore reduce innovation incentives. Further research can take an in-depth look of what variables in making innovation policies should be taken into consideration to effectively bridge the gap among these participants.

Several limitations need to be concerned in this study. First, samples in this study collected from the firms listed on the Stock Exchange may have drawbacks. A recent research examining the effects of firms in the initial public offerings (IPOs) market on innovation shows that some firms going public may lead to a huge decline in BMI due largely to external factors such as the stock market pressures and market volatility, while others staying private (or unable to go public) are more ambitious with innovative activities (Bernstein, 2015). Therefore, future research could expand the population by taking into account of non-IPO firms with similar size, age and capital. In addition, perceptions of innovation range from incremental to disruptive types. Highly innovative firms may choose different paths to BMI but their outcomes may not be measurable such as corporate social responsibility (Chandy & Tellis, 1998). In this respect, small- and medium-sized enterprises may be an appropriate targeted group to do further research. Finally, the proposed model was only tested in Taiwan Stock Exchange, a market with a high ratio of technology companies. Future research adopts markets in different countries with diverse proportions of industries may lead to various outcomes.

Businesses Model Innovation (BMI)

Structural Equation Modeling (SEM)

Business Model (BM)

System-on-a-Chip (SoC)

Intellectual Property (IP)

Taiwan Stock Exchange (TWSE)

CC contributed to the design and implementation of the research, to analyze and interpret the results and to write the whole manuscript. All authors read and approved the final manuscript.

-Availability of data and materials: Upon request

-Competing interests: Not applicable

-Funding: Not applicable

-Authors' contributions: Sole author completes this paper.

-Acknowledgements: Not applicable

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Sharpening Key Capabilities and Illuminating Integrative Ways Towards Business Model Innovation

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Abstract

Figures

Introduction

Theoretical Background And Hypotheses

Dependent Variable: BMI Outcomes

Independent Variable: Key Capabilities of a firm towards BMI

Architecture

Reputation

Innovation

The Mediating Variable: Innovation Culture

The Moderating Variable: Innovation Policy

Research Design And Method

Result And Analysis

Conclusion

Theoretical Implications

Practical Implications

Abbreviations

Declarations

References

Status:

Version 1