The Influence of National and Corporate Culture on Innovation Ambidexterity in the Telecom Industry
Keywords:management, ambidexterity, culture, innovation, organization, big data, small data, artificial neural network, transformation, change
Purpose: Telecom firms are examples of multinational corporations (MNC) that face cut-throat competition in today’s rapidly changing business industry. Competitive pressures together with geographical dispersion makes MNCs dependent on management of both ambidextrous capabilities (the management of both incremental and radical innovations in relation to exploitative and explorative behaviors) and culture. An example of such a management process is to manage the influence of an inherent national culture with the development of a goal-orientated corporate culture in relation to innovative capabilities. Thus, this study is designed to increase understanding of the relative and simultaneous contribution of national and corporate culture on innovation ambidexterity.
Design/methodology/approach: After an extensive literature review, a theoretical model of how factors of national and corporate culture contribute to exploitative and explorative behaviors of innovation ambidexterity, was assembled. National culture attributes were derived from Hofstede’s work. The corporate culture attributes were selected after review of literature and engagement with practitioners from the telecom field. The model constituted the basis for a cross sectional randomized survey to be distributed within purposely selected sites of the telecom firm across Europe and Asia. The survey was sent to 156 employees, including leaders and employees. A total of 96 (41 leaders, such as middle, project and product managers, and 55 employees located in Sweden, Italy and China) returned the survey. The main dataset consisted of the survey responses, including leaders and employees (cohort 1, N=96). The first subset consisted of only employees (N=55, cohort 2) whereas the next subset (cohort 3) consisted of only leaders (N=41). Another subset consisted of both leaders and employees at the Swedish national site with 28 samples (N=28, cohort 4). The next data subset consisted of both leaders and employees at Italian national (N=31, cohort 5). The final subset consisted of both leaders and employees at the Chinese national site (N=26, cohort 6).
The data was analyzed with a PLS-SEM configuration as well as with a machine learning algorithm, multilayer perceptron (MPA) of the class of feedforward artificial neural network (ANN), to predict explorative and exploitative behaviors.
Findings: The main hypothesis, stating that explorative behaviors predict innovation ambidexterity to a larger extend the purely exploitative ones, was confirmed in the main dataset and in the five subsets. Moreover, the outcome of the PLS-SEM analysis showed how “creative instability” (internal corporate culture attribute) played a major role (with a path coefficient value of 0.426 (p-value 0.002) in PLS-SEM managerial chart for cohort 1) in generating explorative behaviors and decreasing the exploitative ones. The indirect effects analysis provided indications on how creative instability could be a mediator of innovation ambidexterity in the main datasets and in the subsets. Creative instability not only appeared to be positively linked to explorative innovation (cohort 1: path coefficient 0.426 with p-value 0.002; cohort 2: path coefficient 0.480 with p-value 0.002; cohort 5 path coefficient 0.989 with p-value 0.006), but also appeared as a possible mediator of innovation ambidexterity more than a direct contributor when analyzing the entire dataset (cohort 1) and on four out of six other datasets.
When analyzing cohort 1, apart from the major finding on creative instability, boundary spanning (internal corporate culture attribute) was found to decrease exploitative behaviors with a path coefficient value of 0.264 (p-value 0.002). From a national culture point of view, power distance was found to increase exploitative behaviors of innovation (path coefficient 0.354, p-value 0.009). In addition, gender diversity was found to increase explorative behaviors identified through the path coefficient value of 0.242 (p-value 0.017) for cohort 2. It was found to decrease exploitative behaviors identified through the negative path coefficient value of 0.178 for cohort 3, however, the p-value was 0.08. Lastly, statistically significant relationships were found for the remaining attributes of national and corporate culture when analyzing the five subsets separately to reduce heterogeneity.
In cohort 1, explorative innovation was found to be positively linked to innovation ambidexterity (path coefficient 0.661, p-value 0.000). The latent variable of explorative innovation accounted for 63.1% of variability (R-square adjusted), and exploitative innovation of 55.7%, forming the innovation ambidexterity variable that was captured at 42.7%.
Conclusions/implications: The findings lend theoretical as well as empirical support to previous research on the role of explorative and exploitative behaviors on innovation ambidexterity with explorative behaviors relating positively to innovation behavior. However, the findings also move beyond simplified explanations of innovative behavior by addressing the underpinnings of explorative behaviors as well identifying the relative influence of national and corporate culture attributes. Consequently, the influence of culture (national and corporate) on innovative behaviors may be asymmetrical across sites in the MNC. The findings also aid in providing insights for the company`s executives, to be used strategically, in terms of how various KPIs may be linked to the type and amount of innovation as well as to gain counter intuitive insights on human capital.
We believe that this conceptual model and the proposed methodology, including predictions can be extended to incumbent firms of the telecom sector. The PLS-SEM model can be personalized with moderating and mediating factors based on the outcomes of the initial rounds of analyses. Moreover, our findings and methodology are also indicative of that companies may refine their Big Data Mining efforts by means of Small Data (such as data granular from questionnaires sampled for this research) psychometrics techniques. This means that an unexploited reservoir of strategic information remains available for corporate decision making as well as for pointing out direction for change management.
Keywords: management, ambidexterity, culture, innovation, organization, big data, small data, artificial neural network, transformation, change.
Link to the full research:
DiVA, id: diva2:1608592
Note for the Program Chair: This submission has been extracted for the first time from the MBA thesis work of Chiara Isola and Divya Peddireddy which was supervised by Prof. Martin Svensson at BTH, Sweden. Thesis Publication Date: 7th Oct. 2021.