Exploring Relationship between Digital Strategy & Business Performance: Regression Model

Over the last decade, we as a customer experienced a rapid change in the way we conduct business with companies. It is mainly due to the formulation and implementation of digital strategies. Banking and trading industry was the first to adapt to digital strategy, then followed by other sectors. Currently, experts suggest that though many sectors, especially in service, advanced in implementing the digital strategy, transport and logistics (TL industry) is way behind. Over the last two years, many digital experts realized this gap in TL industry and started to invest in aggregator or marketplace platforms to disrupt the industry. As the entry barrier is getting weaker (competition from outside the industry), TL industry started to relook at their internal business processes and rethink about digital strategies to stay competitive.

For this article, author-defined digital strategy as an end to end digitization of business process through implementing the latest technology, inducting digital talent and governed by visionary management. Basis literature review, it is must align (Teece, 2007) digital vision with talent, digitization of processes, sourcing technology and implementing good governance model (Sussan and Johnson, 2003). It is also critical for companies to improve continuously (Benner and Tushman, 2002; Anand et al., 2009) to stay competitive in the market as customer expectations are rising day by day (Kumar et al., 2008). It is easy to implement a process, but businesses are no longer competing with having a process in place (Teece, 2007 and Siemieniuch and Sinclair, 2002), they are completing on the ability to quickly re-engineer and implement as per changing market dynamics and customer expectations.

This motivated the author to verify if there is any evidence of a relationship between business performance (revenue) and digital strategy in TL industry.

Alternate Hypotheses (Ha): Digital Strategy will have a positive interaction effect on Business Performance.

 Building Model:

As a good practice, the researcher checked the five conditions of regression analysis before proceeding. Linearity, Independence of Error, Homoscedasticity, Multivariate normality, and non-multicollinearity. As per Q-Q Plots and box plot, data is approximately normally distributed with no outliers. The author decided to proceed with the multiple linear regression analysis.

Analysis: The author managed to build six models and presented the best model for publishing results. In this model, the author analyzed the relationship between Business Performance with Digital Vision Digital Process Talent and Digital Governance .  Where, BP is the dependent variable and  are the independent variable. Research is expecting BP to increase with the increase in clarity of digital vision, process digitization through induction of digital talent and good digital governance. The multiple linear regression model is given by the following equation.

The coefficients from represents regression coefficients  represents independent variables, while BP represents dependent variable.

Results and Interpretation

As per the analysis, the p-value <0.05 shows the model is significant and coefficient of determination  is 0.990 indicates the regression model explains 99% of the variability of business performance (BP) with Digital Vision Digital Process Talent  and Digital Governance .

The results of the p-value shows regression coefficients are statistically significant at 0.05 level of significance. So, the final multiple regression equation is as follows,

As author predicted, the equation (2) shows BP is influenced by the clarity of digital vision, process digitization through induction of digital talent and good digital governance.

Finally, the author checked if the residuals data for the above model is normally distributed. As per box plot of residual data, the data is approximately normally distributed with no outliers.

Conclusion and Limitations: The author established the relationship between business performance (dependent variable) and digital strategy (independent variable - digital vision, digital process talent, sourcing technology, and digital governance) using multiple linear regression analysis. However, this study got limitations as it is conducted with a limited sample.

• The researcher believes this study can be generalized by increasing sample size.  
• As this is a cross-sectional researcher, there is scope to extend this research through longitudinal study and including other companies and industries.
• Considered only revenue as a parameter for assessing business performance, going forward financial ratios can also be considered.
• Also, with regards to the independent variable, there is scope to include more variables like customer satisfaction, employee satisfaction and to name a few that will influence the business performance (dependent variable).

List of References:

• Anand, G., Ward, P.T., Tatikonda, M.V., & Schilling, D.A. (2009). Dynamic capabilities through continuous improvement infrastructure. Journal of Operations Management, 27, 444-61.
• Benner, M. J., and Tushman, M. (2002). Process management and technological innovation: a longitudinal study of the photography and paint industries. Administrative Science Quarterly, 47(4), 676-706.
• Siemieniuch, C., and Sinclair, M. (2002). On complexity, process ownership and organizational learning in manufacturing organizations, from an ergonomics perspective. Applied Ergonomics, 33, 449-62.
• Sussan, A. P., & Johnson, W. C. (2003). Strategic capabilities of business processes: looking for competitive advantage. Competitiveness Review, 13(2), 46-52.
• Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(8), 1319-50.
• Trkman, P. (2010). The critical success factors of business process management. International Journal of Information Management, 30(2), 125-34.