A project by IMD’s Center for Future Readiness
A.I. Text Analysis
A project by IMD’s Center for Future Readiness
How does artificial intelligence help us understand companies better than ever before?
Our NLP-based analysis investigates a company’s outlook to determine its readiness for the future.
We have created an algorithm that analyzes more than 70 public news sources from 2010 onward. These include BBC, Financial Times, New York Times, corporate press releases and various trade magazines. The workflow we created specifically for this purpose fetches media sources through an API (Application Programming Interface). We brought in hundreds of thousands of articles.
Our goal of this is to quantify analyst description of companies’ strategic aspect as well as quantify companies’ self-description of their strategic aspect.This approach is akin to sentiment analysis, but instead of determining discrete outputs (positive, neutral or negative sentiment) we derive continuous measures of strategic thematics, commonly referred to as constructs in the academic literature.
In order to attain high accuracy in our findings, we are leveraging the state-of-the-art transformer architecture that Vaswani et al. first released in 2017. 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 pretrained 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”.
To extract quantifiable insights on strategic topics such as digital orientation, decision speed, etc., we derive continuous measures based on counts of the words defining them. The composition of these subjects is established using academic peer-reviewed definitions. Our 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.
Decision speed, also known as regulatory mode locomotion is a cognitive and behavioral orientation that describes a proactive and action-oriented mindset. It is based on regulatory mode theory, which focuses on two primary modes of regulation: locomotion and assessment. Companies with a locomotion-oriented regulatory mode exhibit traits such as decisiveness, self-confidence, and persistence. They actively engage with their environment, embrace change, and take risks to overcome obstacles and accomplish objectives.
Companies high in decision speed are often mentioned in connection with words like fast, dynamic, motion, change, etc. This also includes variations of the words, like the corresponding nouns.
Our conceptualization and use of decision speed, aka regulatory mode locomotion stems from Higgins, Kruglanski, Pierro (2003). In their influential paper, they provided compelling evidence highlighting the contrasting management styles associated with locomotion and assessment orientations. The locomotion orientation is marked by a keen emphasis on goal pursuit, progress, and forward movement.
Companies explore by identifying new opportunities. This refers to “processes of concerted variation, planned experimentation, and play”. The innovation activity focuses on technological innovation aimed at entering new product-market domains. (Baum, Li, and Usher, 2000; Benner and Tushman, 2002; He and Wong, 2004). Exploring is the opposite construct to exploiting.
Companies high in exploration are often mentioned in connection with words like searching, risk-seeking, exploring, flexibility, experimentation, discover, playing, etc. This also includes variation of the words, like corresponding nouns.
The most prominent literature that focuses on exploration 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)
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.
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)”.