Our computer-aided analysis investigates a company’s outlook to determine its readiness for the future.
We refer to this as big text analytics.
We've created an algorithm that analyzes more than 70 public news sources from the last decade. These include BBC, Financial Times, New York Times, corporate press releases and various trade magazines. The app we created specifically for this purpose talks to these media sources through an API (Application Programming Interface). We brought in hundreds of thousands of articles.
The goal of this is to determine how analysts describe companies and how the companies describe themselves. It is a similar approach to sentiment analysis, but on a larger scale and over a longer period of time.
In order to attain high accuracy in our findings, we are using a state-of-the-art transformer model that Vaswani et al. first released in 2017 while he was employed as a staff research scientist at Google Brain. This model can be used for various natural language processing tasks and has been trained on an extensive 3,000 million words.
Here is the entire process: To start, we pre-process news articles by feeding them into a transformer model. With this model, we can convert anaphoric expressions into company names. In simple terms, we replace any ambiguous pronouns in articles with the appropriate company names. This helps us to focus only on the text that is relevant to the companies in question.
Second, we utilize the transformer model to identify negation in the sentence. This will ultimately prevent us from over-inflating our results. As an example, "company A is not moving fast enough." The negation, or rather, the word "not", needs to be classified as a negative against our construct "fast-moving."
We are now ready to keep a tally of how often certain words and phrases appear in a positive light. For many years, academic, peer-reviewed journals have developed public dictionaries; that is, a list of words associated with a specific construct. This approach follows a long history of research from the academic management literature. In our earlier example the construct was "fast-moving."
What's new with our A.I. approach is that we can unpack and analyze more data than ever before. We compare the scores of different companies in each calendar quarter to ensure a fair baseline, as all companies face similar circumstances during any given point in time.
Questions we asked in our research include:
- Which companies learn more quickly and efficiently?
- Which companies focus on opportunities with immediate benefits rather than developing new capabilities that may not have an immediate payoff?
- Which companies are more committed to adopting digital trends?
Looking at the data, we can see that there are companies who consistently perform better than their peers over long periods of time. We also see quarters where some companies move up in the rankings while others move down. Such observations help us understand why and how some companies are better prepared for the future than others.
Companies which are focused on exploring “identify new opportunities” and follow “processes of concerted variation, planned experimentation, and play”. These companies focus their innovation activities on “technological innovation aimed at entering new product-market domains” (Uotila et al., 2009; Allison, McKenny, & Short, 2014; March, 1991; Raisch & Birkinshaw, 2008; Baum, Li, and Usher, 2000; Gupta, Smith, and Shalley, 2006). Exploring is the opposing construct to exploiting.
Companies high in exploration are often mentioned in connection with words like searching, variation, exploration, risk-seeking, experimentation, playing, flexibility, discover, etc. This also includes variations of the words, like the corresponding nouns.
Some management scholars have investigated how exploration relates to other of our constructs in textual analysis. For instance, Kammerlander et al. (2014) suggests positive correlation between exploration & promotion. Similarly, Tuncdogan, Van den Bosch, and Volberda (2015) explain that exploration is more correlated with promotion than exploration is with prevention.
Companies which focus on exploiting “seize existing opportunities”, and focus on processes of “local search, experiential refinement, and selection and reuse of existing routines”. These companies’ activity focuses on “improving existing product-market domains” (Uotila et al., 2009; Allison, McKenny, & Short, 2014; March, 1991; Raisch & Birkinshaw, 2008; Baum, Li, and Usher, 2000; Gupta, Smith, and Shalley, 2006). Exploiting is the opposing construct to exploring.
Companies high in exploitation are often mentioned in connection with words like refining, choosing, exploiting, production, efficiency, selection, implementation, execution, etc. This also includes variations of the words, like the corresponding nouns.
Which type of scholarly literature investigates exploitation?
The most prominent literature that focuses on exploitation discusses the so-called exploration-exploitation dilemma, which states that companies struggle to face an inherent trade-off of exploitation and exploration. As such, only “ambidextrous” organizations, which are rare, manage to overcome that hurdle (O’Reilly & Tushman: The ambidextrous organization, 2004; O’Reilly & Tushman: Ambidexterity as a dynamic capability, 2008)
Learning orientation is a behavior derived from the literature on organizational learning and describes the motivation, ability, and executional quality of learning in an organization. Learning orientation concerns the whole organization and not only individuals. It also measures the dissemination of the relevant organizational knowledge throughout the organization.
Companies high in learning orientation are often mentioned in connection with words like analyse, evaluate, discuss, exchange, fail, identify, interpret, induce, infer, learn. This also includes variations of the words, like the corresponding nouns.
Many management scholars have researched organizational learning orientation. For example, Calantone, Cavusgil, and Zhao (2002) investigated the link between learning orientation, firm innovation capability, and firm performance. They find that learning orientation is a key factor to organizational competitive advantage. Further, Sujan, Harish, Weitz, and Kumar (1994) find that learning orientation is important for effective marketing and sales staff. Finally, Baker and Sinkula (1999) suggest that learning orientation and market orientation contribute to good organizational performance.
Digital orientation is an organization’s guiding principle to pursue digital technology-enabled opportunities to achieve competitive advantage. It encompasses the dimensions of digital technology scope, digital capabilities, digital ecosystem coordination, and digital architecture configuration.
Companies high in digital orientation are often mentioned in connection with words from the four dimensions mentioned above. These include algorithm, compute, API, developer, digital, functionality, IoT, open source, virtual, etc. This also includes variations of the words, like the corresponding nouns.
Our conceptualization and use of digital orientation stems from Kindermann et al. (2020), who are the first scholars to propose a digital dictionary for automated textual analysis. These scholars conceptualize digital orientation as a distinct type of strategic orientation, i.e., an orientation which “reflects the firm’s philosophy of how to conduct business through a deeply rooted set of values and beliefs that guides the firm’s attempt to achieve superior performance (Gatignon & Xuereb, 1997)”.