Exploring firm strategy using financial reports: performance impact of inward and outward relatedness with digitisation

ABSTRACT

A firm’s success critically hinges on its strategies in selecting its portfolio of products and services. In this paper, we study how differentiation and market alignment at the offering level impact firm performance. To that end, we mine firms’ 10-K filings to characterise the portfolio of offerings through the lens of outward relatedness, inward relatedness, and digitisation. We define outward relatedness as a measure of alignment of firm offerings within its market space, inward relatedness as a measure of differentiation of firm offerings with its own past offerings, and digitisation as a measure of the firm’s focus on IT. We find that markets react positively to firms that operate with high levels of outward relatedness, low levels of inward relatedness and high levels of digitisation. However, we find that highly digitised firms do not have to conform to peers’ offerings. Digitisation enables these firms to differentiate by internally diversifying their offerings. Interestingly, our results show that only firms already highly digitised benefit from further digitisation.

ACCEPTING EDITOR:

• Ojelanki Ngwenyama

ASSOCIATE EDITOR:

• Kweku-Muata Osei-Bryson

KEYWORDS:

• Firm portfolio
• Ten-K filing
• IT-intensive
• performance
• outward relatedness
• inward relatedness
• digitisation

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. We elaborate on how we operationalise this measure in Section 4.2.

2. Others have referred to this concept as digitalisation defining it as “the practice of taking processes, content or objects that used to be primarily physical or analog and transforming them to be primarily digital. The effect (…), is to make processes more tailorable and malleable”, and efficiency gains (Fichman et al., 2014). We do not make such distinction.

3. Our definition of digitisation does not distinguish between the type of IT uses, such as for automation of firm’s processes or for firm’s supply chain processes. We note however that such distinction is not feasible for analyses at large scale number of firms like ours, as such information is not readily disclosed by firms and often is only available sporadically for some firms.

4. Details available at https://www.census.gov/eos/www/napcs/napcstable.html and http://whatis.techtarget.com/glossary/IT-Management, and http://www.gartner.com/it-glossary/.

5. We believe our approach outperforms theirs which is susceptible to synonyms issue as they simply rely on identifying the set of words commonly used by firms.

6. Note that while we also tried to minimise Hansen’s J statistic, this metric does not correct for the degrees of freedom in the model.

7. The magnitude is computed as the product of the regression coefficient and the mean of the corresponding variable. The resulting value represents the average impact of the regressor on the outcome variable.

8. An interesting extension would be to combine Granger causality analysis with PVAR which can further help strengthen causal identification. We thank an anonymous reviewer for this helpful comment.

Additional information

Funding

This work was partially supported by the Social Sciences and Humanities Research Council of Canada [30-2013-000562].