The Role of Organizational Learning in Mediating the Relationship between Business Model Design and Innovation Performance

Zhang, Jia; Xu, Hui; Xiao, Xiangxia

doi:https://doi.org/10.1155/2022/1317646

Discrete Dynamics in Nature and Society

On this page

Abstract Introduction Experimental Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Model Optimization System Analysis of Business Operation Performance

View this Special Issue

Research Article | Open Access

Volume 2022 | Article ID 1317646 | https://doi.org/10.1155/2022/1317646

The Role of Organizational Learning in Mediating the Relationship between Business Model Design and Innovation Performance

Jia Zhang,¹Hui Xu,¹and Xiangxia Xiao¹

Academic Editor: Wen-Tsao Pan

Received01 Jul 2022

Accepted06 Sept 2022

Published24 Sept 2022

Abstract

Innovation in the business model (BM) may be irreversible and therefore entails more significant risk and ambiguity compared to product, service, or process innovation. This paper focused on exploring the role of organizational learning in mediating the relationship between business model design and innovation performance using hierarchical hybridized linear regression analysis (HHLRA). Furthermore, this paper shows that organizational learning is based on variables that mediate the relationship between training and performance and improve performance through its positive effect on organizational learning. Furthermore, the study examines the multi-dimensional and contingent progressive impact on the corporate performance of organizational training through innovative conjunctive roles. However, this study determines that both organizational learning capabilities (OLC) and organization innovativeness (OI) have a positive effect on organizational performance (OP). Besides, organizational innovativeness mediates the relationship between OLC and OI. Finally, the findings suggest that managers should improve organizational learning capabilities and improve innovation performance to enhance positive entrepreneurial orientation performance connections.

1. Introduction

The modern business environment has required enterprises to implement advanced business models to continue competitively in an uncertain and volatile market. In a competitive environment, organizations must modify themselves to meet the demands of the external and internal environment [1]. Business model design is central to the development of entrepreneurship and strategic business research [2]. Innovations can be preserved as drivers of profit, as drivers of growth, and as drivers of market value when considered. From the knowledge point of view, organizational learning is the progression of enhanced organizational behavior via the acquisition, exchange, and use of information [3]. Organizational learning, therefore, plays a significant role in the innovation of the business model. At the center of the innovation business model is the ability to offer customers valuable and innovative products or services that involve the redesign of operations, business processes, and value creation and transmission [4]. The key to the successful execution of these activities is organizational capacity [5]. Excellent integration capacity has considered an essential factor for companies to implement the innovation of their business model [6, 7].

Organizational learning improves product and process innovation by developing new abilities and unlearning old skills [8]. Change depends on the human capital of the organization. The creativity of employees contributes to the modernization of the company [9, 10]. When an organization offers its employees flexibility and opportunity, creativity is promoted. In this framework [11], human resource systems and practices play a crucial role through the provision of desired skills and the creativity of employees that is more innovative. Organizational learning offers a support system in which human resource management practices allow creative behavior [12–14]. The social and psychological experiences [15] of members of an organization produce multiple learning activities and ultimately lead to organizational change in performance [16]. That is, how new knowledge flows from the individual to the organizational level [17] and how existing knowledge flows [15] from the organization to the individual performance [18, 19]. Figure 1 shows the corporate learning and business model design.

In this paper, the hierarchical hybridized linear regression analysis (HHLRA) has been proposed to determine the role of organizational learning in mediating the relationship between business model design and innovation performance. This study, therefore, seeks to explore the mechanism for the functioning of resource integration in the field of organizational learning link between business model design. First, the proposed model considers mediating and moderating effects to illustrate how managers can guarantee that the BMI offers firms more benefits in terms of performance. Secondly, it helps practitioners align their BMI efforts with business strategy and day-to-day operations by providing a larger, more realistic view of what is happening in the company [20, 21].

The main contributions of the paper are as follows:(i)To propose hierarchical hybridized linear regression analysis (HHLRA) to explore the role of organizational learning in mediating the relationship between business model design and innovation performance.(ii)Designing the business model and questionnaire survey model to enhance organizational learning and innovation performance.(iii)The experimental results demonstrate the positive effects on the business model and innovation performance.

The remainder of the paper is discussed as follows: section 1 and section 2 discussed the introduction of business model design and existing methods. In section 3, hierarchical hybridized linear regression analysis (HHLRA) has been proposed to determine organizational learning in mediating the relationship between BM design and innovation performance. In section 4, the numerical results have been illustrated. Finally, section 5 concludes the research article.

2. Conventional Survey and Its Impact

Zhu et al. [22] suggested structural equation modeling (SEM) for analyzing the supply chain integration value creation via handling interorganizational learning. Their findings verify the relationship between the integration of the supply chain and firm focal performance between organisationally based learning.

Zhu et al. [23] proposed the high-performance work system (HPWS) to determine the impacts of organizational learning and entrepreneurial orientation. The experimental outcomes show that, when organizational learning is stronger, the relationship between high-performance work systems and a firm’s performance is positive.

Lafuente et al. [24] introduced the sequential deductive triangulation analysis (SDTA) for discovering the role of organizational learning ability in knowledge-intensive business services companies. The findings show that organizational learning capacity has a positive effect on the performance of innovation.

Kafetzopoulos et al. [25] initialized the exploratory factor analysis monitored by confirmatory factor analysis and structural equation modeling (EFA-CFA-SEM) for analyzing the relationship between European foundation for quality management (EFQM) and business performance. This article accomplishes that the impact of excellence enablers on business performance, in part, mediates innovation performance.

Mikalef et al. [26] proposed the big data analytics capability (BDAC) for exploring the mediating roles of capabilities and the dynamic relationship between competitive performances. The outcomes demonstrate that a strong BDAC can support companies to build a competitive benefit using partially square structural equation models.

Yiu et al. [27] discussed the Service-Dominant Orientation (SDO) on the innovation performance of technology firms. The outcomes illustrated that SDO's relationship learning mediates external networks, innovation performance, knowledge-sharing, and innovation performance.

Morales et al. [28] proposed the moderated mediation model (MMM) for helping behaviors at work stimulate innovation in the organization. Furthermore, a higher level of support improves innovation and strengthens the positive link between intervention and innovation performance.

Based on the above discussion, there are certain differences in the research conclusions on the mechanism of business model innovation in enterprise performance. It is necessary to introduce relevant variables to deeply explore the mechanism of action between the two. Because the improvement of organizational learning ability will be affected by the change of the business model of the enterprise, the change of the business model of the enterprise will also have an impact on the original organizational learning ability. Based on this, in order to more objectively analyze the correlation between variables, the data in this paper are all from primary questionnaires. The sources mainly include individual distribution and collective distribution. The collective distribution method mainly uses data collection by outsourced third parties. In this paper, the role of organizational learning in mediating the relationship between business model design and innovation performance uses the Hierarchical Hybridized Linear regression analysis (HHLRA). OLC is a strategic tool for the improvement of OI and OP for competitive advantage following dynamic, rapid knowledge changes. Learning practice is considered to be a key factor in alleviating uncertainty and adopting external changes effectively to gain and maintain a competitive edge. These practices can be organized, analyzed, managed, and efficiently improved to correct management skills. Continuing and effective learning creates an innovative culture that pushes the organizational performance curve up. Innovation can lead an organization, which will finally lead to core competencies and competitive advantage, to grow and improve business processes. In the study variables, the development of organizational performance is important.

2.1. Hierarchical Hybridized Linear Regression Analysis (HHLRA)

In this paper, the hierarchical hybridized linear regression analysis (HHLRA) has been proposed to explore the role of organizational learning in mediating the relationship between business model design and innovation performance. Structurally, a business model as a routine has three main linked components: a value dimension, a trans active size, and a resource dimension. There are many moving elements within a business model, all of which must work together [29, 30]. The entire model must be in line with the strategy, culture, and resources of the organization. Figure 2 shows the conceptual model using the proposed HHLRA method.

Business model innovation is a complete change to an enterprise’s existing business, its current market environment and competitive landscape, and the creation and realization of brand-new value propositions based on the development of customer needs. The overall performance of the firm will be influenced by innovation. The orientation can have a positive impact on business performance, as change means a competitive advantage. Companies are interested in innovation and concentrate on activities that enhance their capability to do this. This ability drives companies to improve continually and thus improve their business performance.

2.1.1. Postulate 1

BMI has a direct impact on overall organizational performance.

Organizational learning is expressed by four constructs which contains corporate memory, information interpretation, knowledge acquisition, and information distribution. Organizational learning can be considered a dynamic process in which knowledge is gained, shared, and used to develop resources and capacity. The acquisition of experience refers to technical expertise to build or generate product knowledge, knowledge of management, and marketing knowledge. Sharing knowledge means spreading what has been learned. The use of expertise means integration of the experience and is, therefore, generalized to new situations and is broadly available.

Companies continue to conduct organizational learning to search and refine areas for improvement, incrementally improve existing revenue models and value creation models, etc. Enhance the content of enterprise innovation, speed up the process of realizing the commercialization of knowledge and market demand, and then stimulate enterprises to reflect and improve their business models, and ultimately ensure the ability of enterprises to innovate business models.

The essential values of a market culture depend on a rational view that highlights competitiveness, productivity, and a precise setting of objectives. Market culture prefers leaders who are very happy to encourage their underlings to attain quick response, and their goals to guarantee modest success are highlighted. A fundamental reason for maintaining a balanced method is the survival of scarce resource competition by organizations. The competitive market culture promotes a tension between the transfer of learning from individual to group and organization and vice versa. Those who work in a market-oriented culture would desire to participate in learning activities that provide categorical knowledge and monitor the learning procedure carefully to improve knowledge production reliability.

2.1.2. Postulate 2

Organizational learning has a positive effect on business model innovation, while Market Culture positively impacts organizational learning activities.

By building values and recognition of opportunities, BMI can provide opportunities, both in times of rapid economic growth and economic downturn, via the ongoing transformation of the industry, to exploit a market that its competitors are unable to tackle and thereby increase the revenue growth of companies. As an illustration, organizations can create additional access to resources, quickly or in conjunction with new opportunities to integrate services, through the adoption of new collaboration models.

BMI is required to make organizational efforts. To enable companies to renovate their BM, they need to detect new technology and market needs and be open-minded and innovative. This BMI process increases its organizational capacity once the firm starts to explore, design, test, and implement its BMI. Culture is defined as the expression of norms, values, and beliefs that boost business performance at the end of the day. When specific attitudes are adapted to the organizational culture, the consequences of conduct are expanded to compensate for the circumstances, groups, and individuals of the company. A culture that supports the implementation of a strategic attempt is not easy to imitate, as, in our BMI research, it can lead to a competitive, sustainable advantage. The ability to search for opportunities is another organizational capacity that can mediate BMI and its performance. BMI is a consequence of innovation at the BM Company. BMI can, therefore, boost the change of people within the company in different ways, including sharing the whole business idea, building the capacity to search for opportunities, and creating genuine value propositions. Besides, organizational learning is a critical organization, through which information and information and knowledge can be processed and which can alter the organization’s attributes, behaviors, capabilities, and performance.

2.1.3. Postulate 3

Organizational capabilities have a positive impact on BMI.

BM Experimentation resources involve budgeting, human capacity, and time spent in support of BM experimental practices by a company. The innovative method in the BM company includes activities related to (a) how to modify the logic of the company, i. e. radically or incrementally, (b) the order in which modifications are made to parts of the company, and (c) (virtual) thinking vs. real experimentation. These activities can be carried out in the context of a BM team, for example, in the context of management work, or through the hiring of external consultants on a financial plan. Let’s preassume that resources for a particular task enable more activity to be undertaken.

2.1.4. Postulate 4

BM experimentation resource allocation has a direct impact on BMI.

In Figure 3 shows the business model innovation structure to improve a firm’s performance, involving moderating and mediating variables. The production function of the regional innovation output is built from several factors that are multiplicatively associated as follows:

As shown in (1) where to be regional factors are reviewed in the logistic regression analysis, and X indicates the innovation output. C denotes a constant and to are the coefficients the strengths of every factor influence.

The major attraction of this method is that it can be calculated with an ordinary, square least regression using the following logarithm:

The advantage of this type of function is the elasticity of coefficients. The relationship between regional factors and innovative output must be log-linear. When applying the log-linear production of knowledge, it is implicitly assumed that no innovations are to be expected in a region with a lot of enterprises and public research, not organizations’ employees. Because regional factors are connected multiply in the nontransformed version, a value of null in one infers that no new output is predictable.

This issue is overcome by supposing an ordinary linear relationship with additionally linked regional factors, which is determined as follows:

As inferred from (3) where is the m regional factors reviewed in this analysis, and X indicates the innovation output. V is the statistical error term with the characteristics, c is a constant and to is the regression coefficient that indicates the strength of every factor's influence. The relationship between regional and regional innovation factors is calculated twice for each sector. The first is to apply the standard model of linear regression, and the second is the log-linear model. It is tested that the expectations for their application are fulfilled to check which model is best suited.

Some of the regional factors contain 0 values, which avoids taking the logarithm, and it is essential for the log-linear model. The total geographical factors are thus transformed as follows:

In our study, regional factors are theoretically determined to produce coherent, theoretical significance for each of the first components. Organizational learning decentralizes the decision-making process and supports to development of creative and innovative behavior. Organizational learning, therefore, affects the innovation skills of the company. Regulatory learning activities are fundamental organizational capacities to achieve corporate success and improve performance in business model innovation. This paper has demonstrated the theoretically positive results of organizational learning performance as a result of the independence between transfers of learning and knowledge accumulation at various organizational levels.

3. Experimental Results and Discussion

The data has been analyzed statistically to test the above postulates. First, standard deviation (SD), intercorrelation validity, and reliability have been calculated. The correlation analysis has been utilized to observe the relationship between various types of culture, innovation performance, BMI, and organizational learning. Table 1 shows the significant correlation that exists between variables, which can further determine the relationships between them through the proposed hierarchical hybridized linear regression analysis (HHLRA).

Figure 4 shows the innovation performance in business model innovation. The study explores the mechanism of effects inherent to the change between OL and BM, consequently contributing to the theoretical development of business model innovation by introducing resources integration capacity into the conceptual framework. This shows that it is wise to improve the ability to integrate resources through organizational learning if the company wants to introduce an innovation in business models. When an enterprise has a good organizational learning ability, it can use this ability to identify external market opportunities in a timely and accurate manner, effectively use the internal resources of the enterprise, control the risks existing outside the enterprise, and improve the efficiency of enterprise organizational reform and value creation, which is conducive to appropriately innovating existing business models and exploring business models suitable for its own development. Companies should actively encourage employees to obtain information and knowledge from clients, partners, competitors, and benchmarking firms and promote cross-company knowledge flows. The capacity to integrate resources is helpful to absorb the knowledge performance gained from the organization's learning and thus encourage innovation in business models, as shown in Figure 5.

Market culture prefers leaders who are very happy to encourage their underlings to attain quick response, and their goals to guarantee modest success are highlighted. A fundamental reason for maintaining a balanced method is the survival of scarce resource competition by organizations. The competitive market culture promotes a tension between the transfer of learning from individual to group and organization and vice versa, as shown in Table 2, based on an innovative balanced performance ratio.

In Table 2 shows the organization's innovation performance using the proposed HHLRA method. The development and operation of a new BM may have a more powerful effect on company performance in a more dynamic setting. Regulatory changes, on the contrary, can affect the performance of continuing BM innovations both within and outside the industry.

The structural model outcomes reveal that the resources of business model experiments have positive effects and relationships with the business model as well as with innovation performance. Besides, a mediation test has been conducted to analyze if the relationship between BM’s experimental resources and the strategic implementation practices of the BM has mediated the company's overall performance. The outcomes have shown that business model experimentation mediates the relationship between business model plan practice and the global firm's performance partially. Table 3 shows the postulate test and statistical results.

This paper specifies the multidimensional learning features of an organization and has verified their leading role in increasing organizational performance. Firstly, learning at multiple levels is the main capacity for organizations to address changing environments. The organization, therefore, requires to progress workroom learning tools like shared practices, mentorship programs, and workplace training that can foster learning via close relationships and promote the development of the learning tool. The research indicates that firms with a single type of culture cannot encourage innovation and education. Firms need to guarantee they have a mix of cultures that promote creativity, teamwork, and risk becoming capable of less structured and courteous policies.

4. Conclusion

This paper presents the hierarchical hybridized linear regression analysis (HHLRA) to explore the role of organizational learning in mediating the relationship between business model design and innovation performance. Firstly, business model design has a significant positive impact on the overall innovation performance; that is, the stronger the business model design capability, the better the innovation performance. This conclusion is consistent with the null hypothesis. Secondly, the improvement of business model design ability has a significant positive impact on organizational learning. Finally, the impact of business model design on innovation performance can be achieved through the mediating role of organizational learning capabilities. This paper opens the “black box” between business model and enterprise performance clarifies the mechanism of action and realization path, further enriches the relevant theoretical research on business model design, organizational learning, and innovation performance, provides an idea for follow up research and provides a theoretical basis and management support for how to quickly improve the performance of late developing enterprises. Regulatory learning companies tend to obtain certain kinds of knowledge from customers, competitors, partners, and employees. Organizations can update the knowledge system and bridge the inertia that forms the basis of innovation. Organizational learning, therefore, contributes to change in the BM.

Based on the above discussion, thus firms with higher organizational learning can improve the performance of innovation. The benefit of utilizing the proposed HHLRA method offers an accepted mathematical method for analysis postulates in a generalizable way. This article suggests that business model innovation practices deliberating and trying out modifications in the business model positively impact firm performance. Therefore, organizational learning fully mediates the relationship between corporate innovation and business model design. Business model innovation is the innovation of a system, and activities consist of value acquisition and value creation.

5. Research Implications

Organizational learning is the driving force for enterprises to move forward. In today's era, enterprises are faced with continuous changes in the environment and the continuous updating of knowledge. The development of a knowledge economy requires enterprises to learn continuously and effectively in order to obtain sustainable competitive advantages. An organizational learning mechanism is an effective way for enterprises to communicate, communicate and cooperate with the external environment. To establish an organizational learning mechanism, firstly, the management should take the lead and put learning and innovation in the first place; secondly, it is necessary to improve the incentive mechanism to encourage employees to actively learn; and finally, to improve the enterprise training system and create a good organizational learning atmosphere for the enterprise.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the Philosophy and Social Science Youth Program of Hubei Provincial Department of Education (20Q033).

References

R. Rauter, D. Globocnik, E. Perl-Vorbach, and R. J. Baumgartner, “Open innovation and its effects on economic and sustainability innovation performance,” Journal of Innovation & Knowledge, vol. 4, no. 4, pp. 226–233, 2019.
View at: Publisher Site | Google Scholar
A. B. Abdallah, S. E. Dahiyat, and Y Matsui, “Lean management and innovation performance,” Management Research Review, 2019.
View at: Google Scholar
S. Collinson and Y. Liu, “Recombination for innovation: performance outcomes from international partnerships in China,” R & D Management, vol. 49, no. 1, pp. 46–63, 2019.
View at: Publisher Site | Google Scholar
Q. Shi, H.-K. Lam, Bo Xiao, and S.-H. Tsai, “Adaptive PID controller based on Q-learning algorithm,” CAAI Transactions on Intelligence Technology, December, vol. 3, no. 4, pp. 235–244, 2018.
View at: Publisher Site | Google Scholar
X. Zhu, Z. Xiao, M. C. Dong, and J. Gu, “The fit between firms’ open innovation and business model for new product development speed: a contingent perspective,” Technovation, vol. 86-87, pp. 75–85, 2019.
View at: Publisher Site | Google Scholar
N. Salim, M. N. Ab Rahman, and D. Abd Wahab, “A systematic literature review of internal capabilities for enhancing eco-innovation performance of manufacturing firms,” Journal of Cleaner Production, vol. 209, pp. 1445–1460, 2019.
View at: Publisher Site | Google Scholar
Gao, S. Guo, X. Liu, G. Manogaran, N. Chilamkurti, and S. Kadry, “Simulation analysis of supply chain risk management system based on IoT information platform,” Enterprise Information Systems, vol. 14, pp. 1–25, 2019.
View at: Google Scholar
P. Adams, I. M. Bodas Freitas, and R. Fontana, “Strategic orientation, innovation performance and the moderating influence of marketing management,” Journal of Business Research, vol. 97, pp. 129–140, 2019.
View at: Publisher Site | Google Scholar
C. Yu, Z. Zhang, and Y. Liu, “Understanding new ventures’ business model design in the digital era: an empirical study in China,” Computers in Human Behavior, vol. 95, pp. 238–251, 2019.
View at: Publisher Site | Google Scholar
S. Dhote, C. Vichoray, R. Pais et al., “Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce,” Electronic Commerce Research, vol. 20, 2019.
View at: Publisher Site | Google Scholar
S. Kobarg, J. Stumpf-Wollersheim, and I. M. Welpe, “More is not always better: effects of collaboration breadth and depth on radical and incremental innovation performance at the project level,” Research Policy, vol. 48, no. 1, pp. 1–10, 2019.
View at: Publisher Site | Google Scholar
M. Loon and R. Chik, “Efficiency-centered, innovation-enabling business models of high tech SMEs: evidence from Hong Kong,” Asia Pacific Journal of Management, vol. 36, no. 1, pp. 87–111, 2019.
View at: Publisher Site | Google Scholar
H. Rydehell, A. Isaksson, and H. Löfsten, “Business networks and localization effects for new Swedish technology-based firms’ innovation performance,” The Journal of Technology Transfer, vol. 44, no. 5, pp. 1547–1576, 2019.
View at: Publisher Site | Google Scholar
C. Rupietta and U. Backes-Gellner, “Combining knowledge stock and knowledge flow to generate superior incremental innovation performance—evidence from Swiss manufacturing,” Journal of Business Research, vol. 94, pp. 209–222, 2019.
View at: Publisher Site | Google Scholar
V. Ramadani, R. D. Hisrich, H. Abazi-Alili, L. P. Dana, L. Panthi, and L. Abazi-Bexheti, “Product innovation and firm performance in transition economies: a multi-stage estimation approach,” Technological Forecasting and Social Change, vol. 140, pp. 271–280, 2019.
View at: Publisher Site | Google Scholar
M. Hamelink and R. Opdenakker, “How business model innovation affects firm performance in the energy storage market,” Renewable Energy, vol. 131, pp. 120–127, 2019.
View at: Publisher Site | Google Scholar
S. Cheah and Y. P. Ho, “Coworking and sustainable business model innovation in young firms,” Sustainability, vol. 11, no. 10, p. 2959, 2019.
View at: Publisher Site | Google Scholar
M. Bashir and R. Verma, “Internal factors & consequences of business model innovation,” Management Decision, vol. 57, 2019.
View at: Google Scholar
H. Wang, Y. Zhao, B. Dang, P. Han, and X. Shi, “Network centrality and innovation performance: the role of formal and informal institutions in emerging economies,” Journal of Business & Industrial Marketing, vol. 34, no. 6, pp. 1388–1400, 2019.
View at: Publisher Site | Google Scholar
H. Bouwman, S. Nikou, and M. de Reuver, “Digitalization, business models, and SMEs: how do business model innovation practices improve performance of digitalizing SMEs?” Telecommunications Policy, vol. 43, no. 9, Article ID 101828, 2019.
View at: Publisher Site | Google Scholar
E. Erna, S. Surachman, S. Sunaryo, and A. Djajuli, “Integration between radical innovation and incremental innovation to expedite supply chain performance through collaboration and open-innovation: a case study of Indonesian logistic companies,” Uncertain Supply Chain Management, vol. 7, no. 2, pp. 191–202, 2019.
View at: Publisher Site | Google Scholar
Y. Snihur and J. Wiklund, “Searching for innovation: product, process, and business model innovations and search behavior in established firms,” Long Range Planning, vol. 52, no. 3, pp. 305–325, 2019.
View at: Publisher Site | Google Scholar
Q. Zhu, H. Krikke, and M. C. Caniels, “Supply chain integration: value creation through managing inter-organizational learning,” International Journal of Operations & Production Management, vol. 38, no. 1, pp. 211–229, 2018.
View at: Publisher Site | Google Scholar
C. Zhu, A. Liu, and G. Chen, “High-performance work systems and corporate performance: the influence of entrepreneurial orientation and organizational learning,” Frontiers of Business Research in China, vol. 12, no. 1, p. 4, 2018.
View at: Publisher Site | Google Scholar
E. Lafuente, A. Solano, J. C. Leiva, and R. Mora-Esquivel, “Determinants of innovation performance: exploring the role of organisational learning capability in knowledge-intensive business services (KIBS) firms,” Academia. Revista Latinoamericana de Administración, vol. 32, no. 1, pp. 40–62, 2019.
View at: Publisher Site | Google Scholar
D. Kafetzopoulos, K. Gotzamani, and D. Skalkos, “The relationship between EFQM enablers and business performance,” Journal of Manufacturing Technology Management, vol. 30, no. 4, pp. 684–706, 2019.
View at: Publisher Site | Google Scholar
P. Mikalef, J. Krogstie, I. O. Pappas, and P. Pavlou, “Exploring the relationship between big data analytics capability and competitive performance: the mediating roles of dynamic and operational capabilities,” Information & Management, vol. 57, no. 2, Article ID 103169, 2020.
View at: Publisher Site | Google Scholar
H. L. Yiu, E. W. Ngai, and C. F. Lei, “Impact of service‐dominant orientation on the innovation performance of technology firms: roles of knowledge sharing and relationship learning,” Decision Sciences, vol. 51, 2019.
View at: Google Scholar
M. Samih, “Mostafa. “Imputing missing values using cumulative linear regression”,” CAAI Transactions on Intelligence Technology, vol. 4, no. 3, pp. 182–200, 2019.
View at: Google Scholar
V. P. Y. Morales, A. Pan, and U. Ali, “How helping behaviours at work stimulate innovation in the organization: evidence from a moderated-mediation model,” Innovation-Organization & Management, vol. 22, 2019.
View at: Google Scholar

Copyright

Copyright © 2022 Jia Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies