4. The Role of Digital Literacy in Leadership

While it is often commonly assumed that younger individuals are naturally more adept than their elderly counterparts at using and learning digital tools, research suggests that it is not always the case. As a matter of fact, older individuals are not inherently less willing or able to become proficient with digital technologies. Many studies have demonstrated that most older adults have strong interest in digital technologies and actively seek opportunities to learn how to use them (Betts et al., 2019). Moreover, with appropriate training and support, older adults can achieve proficiency levels comparable to those of other age groups (see, e.g., Guo et al., 2008). Langset et al. (2018) contend that individuals’ baseline digital competence—which encompasses digital knowledge, skills, and literacy—and situational factors (e.g., whether digital technologies are readily accessible and necessary in one’s life or whether they can comfortably ask for support through their social network) are more important than their biological age in predicting their attitudes toward digital technologies (see also Daugherty et al., 2021). Interestingly, Barrie and colleagues point out that it is the societal and internalized ageism, rather than age itself, that influences one’s motivation to learn new digital tools and subsequent proficiency (Barrie et al., 2021). Together, these studies suggest that, while age may play some roles in shaping one’s digital competency, it is far from being the sole determinant. The relationship between age and the adaptability, trainability, and proficiency in digital technology is decidedly more complex than what the stereotype would have us believe.

Let me share my personal experience, which bears some relevance to this part of discussion. About two decades ago, I was a college student, juggling (barely successfully, my now-deceased advisor Todd would add) academics and a part-time job at an apparel store. The store was equipped with an inventory management system that resembled the one being discussed here. This system was integrated with the Point of Sale (POS) system and computerized to record and monitor inventories, triggering automatic reorders when stock levels ebbed. It worked perfectly well for perennial, all-season items, such as T-shirts, maintaining an optimal stock level throughout the year. It faltered, however, when dealing with seasonal items. For example, every year, as the chill of winter began to recede and the stockpile of down jackets dwindled—yes, precisely as you might anticipate—the system dutifully followed its programmed logic and kept replenishing the thermo-protective garment until the store manager manually interrupted it!

This ability of AI-driven DX to automate complex decision-making processes has some implications for the workforce (Jarrahi, 2018). While simple task automation may raise concerns about job displacement, the automation of complex tasks can free up employees’ time and enable them to focus on more creative and strategic aspects of their work (Selenko et al., 2022). This shift in responsibilities is argued to lead to a more fulfilling and productive, albeit challenging, work environment, where employees are able to utilize their skills and expertise to drive innovation, contribute to the organization’s success, and achieve their personal and professional goals.

Hook (2023) elucidates subtle yet enduring biases of AI through the discussion of the “lone banana problem.” In his experiment, Hook requested Midjourney, a generative AI system, to create visual images of a single banana. Intriguingly, regardless of phrasing of the prompt, the AI stubbornly kept producing images that depict two or more bananas. Hook posits that this perplexing result is a byproduct of the AI’s training data, which predominantly featured bananas in pairs or groups, making the system incapable of imaging a lone banana. This phenomenon, termed the “lone banana problem,” serves as a reminder of the importance of understanding the assumptions and biases inherent in AI technologies. While the depiction of single or multiple bananas in AI-generated images may seem trivial and inconsequential, it suggests that more significant and serious biases might be embedded within any AI algorithm. Those in leadership roles overseeing the use of AI systems should maintain acute awareness of such potential biases (for more discussions on biases in AI, see Marinucci et al., 2023; Parikh et al., 2019).

Uncertainty is typically engendered by one’s lack of knowledge or insufficient understanding about a situation, and humans have inherent needs to seek out missing information and fully comprehend their surrounding environment (Cosmides & Tooby, 2013).

See Chap. 3 for a discussion that compares and contrasts TFL with other leadership theories in the context of digital transformation.

It is worth noting that this unique feature of inspirational motivation could backfire and render negative ramifications if it is not exercised responsibly. To illustrate, leaders who set unrealistic expectations or make false promises can end up causing disillusionment and cynicism among followers (Tourish & Willmott, 2023). Moreover, leaders who emphasize inspirational motivation can also create a culture of overwork and burnout, where followers feel pressured to work unsustainably hard to achieve the vision (Hildenbrand et al., 2018; Salem, 2015).

Perhaps by the time this monograph finds its space in your bookshelf, some AI-based solutions may be available to help those who struggle to fix their speaker/microphone settings in every zoom meeting.

According to the most recent records as of the current study’s data collection, the service provider, Macromill, held the highest market share in Japan’s research industry (Japan Marketing Research Association, 2021).

In the screening process, respondent candidates were asked about their occupation, and only those who were identified as full-time white-collar employees were included in this study.

All respondents were Japanese citizens. Most of them were employees of Japanese companies, whereas a fraction (approx. 10%) of them were working at Japan offices of international/multinational companies.

Initially, 1,500 respondents were identified after the screening process, to whom the invitation for participation was sent. From this initial pool, 17 were removed by the data-fidelity checks detailed in Footnote 12. Another 92 dropped at wave-1 as the response to the for-supervisor and/or for-employee surveys was not returned. Additional 73 dropped at wave-2, yielding the final sample of 1,318 member–supervisor dyads who successfully completed both surveys at waves 1 and 2.

This study represents part of a larger research project; respondents and supervisors provided data not used in this study at both waves (the questions tapping the focal constructs examined in this study represented about one-third of the entire questionnaire at each wave).

McDonald’s ωs were estimated based on CFA factor loadings computed with Mplus 8.5. Computations were run based on Hayes and Coutts’s (2020) procedure. The confidence intervals were estimated using the bootstrapping process with 10,000 iterations.

The impact of the demographic variables was examined by running an alternative SEM. More specifically, the age and gender of both the respondents and their supervisors, whether the dyad was cross-sex, and the tenure of the dyad were entered as control variables in the alternative model. The results indicated that the statistical significance and direction of the effects among the main variables remained consistent. However, for the sake of brevity, the results of the analysis without the demographic variables are presented in this chapter. The details of the parameter estimates computed in the alternative model are available from the author upon request.

If the individuals can accurately assess the quality of the given support and make an independent decision about its adoption, that means they have a sufficiently deep understanding of the situation; thus, by definition, they should have little uncertainty in the first place.

An alternative interpretation is that both employees and supervisors might have had high digital literacy. They are adept at AI technologies, traversing the digital landscape with little uncertainty. Just like a video game where everyone is at the top level, supervisors belonging to this unique subpopulation may be uniformly so good at digital literacy. Such uniformity kills variance, the essential fuel for a variable to exhibit statistical significance in the null-hypothesis significance testing framework (Nickerson, 2000).

Yes, it is true that trains in Japan come and go literally within seconds of the scheduled time; similarly, if the departure or arrival of a flight of Japanese airline companies gets delayed even for a few minutes, an announcement containing humble apology remarks will be promptly issued. Punctuality serves as a measure to enhance predictability—should you have the experience of waiting for unpredictable buses in the bustling streets of New York or Jakarta City, you know what I am talking about.

In fact, supporting and training employees with low digital literacy is argued to constitute a fundamental core of cultural transformation in the context of DX (Kolade & Owoseni, 2022).