Assignment:
Please write 1200 words Group 1's analysis of all three case studies (provide feedback, question or critique the posted work, add your own observations and insights, and contribute/discuss in any other manner, but using substantive theoretical material and/or empirical evidence as your basis.)
- How do the barriers that prevent or hinder the applicability of artificial intelligence impact its use in an insurance company?
- Digital transformation toward data-driven decision-making
- Power as "Present-in-Actions" in mundane information systems work
1. Discuss how the "barriers," "challenges," and "power dynamics" respectively described in the three papers relate to the three dimensions of information systems in Figure 1.5 and Section 1.6 of the textbook. Need Assignment Help?
2. Discuss how the proposed "solutions," "mitigating actions," and "power-sensitive framework" respectively described in the three papers relate to the notions of "complementary assets" and "sociotechnical systems'" in Sections1.8 and 1.9 of the textbook.
3. What kinds of ethical issues/dilemmas - regardless of whether explicitly mentioned or not - might be relevant to the contexts of these three papers? How might they be addressed?
The barriers, challenges, and power dynamics described in paper 1 can be directly tied to the three dimensions of IS shown in the textbook. When it comes to the organizational dimension AI adoption can be limited by a lack of knowledge or a cultural resistance around AI and can ultimately create an asymmetrical power dynamic within an organization. Technologically, one of the biggest barriers and challenges a firm can face in AI implementation is if the data being fed to the AI is poor quality or if the AI that is being implemented within the firm has difficulty with or is unable to integrate with the systems already in place within a firm. Finally, when it comes to the managerial dimension, article 1 highlights that the operational execution and the overall strategic intent for this AI integration weren't aligned because these AI initiatives "have been born at the team level" (Edval da Silva Tavares, Leardini, & de Paula Pessoa, 2025) rather than as coordinated initiatives from leadership. Some of the biggest barriers and challenges highlighted in article 2 can be seen in the organizational dimension where firm leadership was faced with "fear of surveillance, balancing intuition and objectivity, and knowing how to leverage data-driven decision-making" (Müller, Zaggl, Svangaard, & Jakobsen, 2025) by their employees. This will lead to power dynamics within the firm as leadership will need to figure out how to manage these challenges to successfully implement the desired changes without driving away employees. When it comes to the technological dimension, this article showed that leadership struggled with practical integration and noted that organizations oftentimes lack clarity or understanding on the best ways to utilize data-driven decisionmaking. Finally, for the managerial dimension, not only did the firm's leadership need to navigate employee concerns, but they also needed to "motivate data use" and "develop digital competencies" (Müller, Zaggl, Svangaard, & Jakobsen, 2025) in order to successfully implement IS systems.
In article 3, the authors state that "power is endemic to IS work and an integral aspect of everyday IS practices." (Simeonova, Kelly, Karanasios, &Galliers, 2024). In an organizational context this can mean that the routine actions of IS managers and employees help to influence IS outcomes within a firm. For the technological dimension, the article highlights that the IS tools can also help to reinforce and mediate power through the use of "surveillance, monitoring, or control systems" (Simeonova, Kelly, Karanasios, &Galliers, 2024). These same systems can also be used to support collaboration and increase transparency within the firm and the work they are doing. From the perspective of the managerial dimension, the IS framework set up helps management to exercise their authority through resource control, tool access, as well as rule-setting. The power dynamic in the managerial dimension is further highlighted in this article when the author notes that power is "the rules and norms that direct behavior" (Simeonova, Kelly, Karanasios, &Galliers, 2024) as well as structures that dictate who is able to act on information.
Laudon and Laudon (2026) argue that information systems are most successful when organizations invest in complementary assets such as employee training, management support, and organizational change. They also emphasize a sociotechnical systems approach, which recognizes that technology and people must work together for an organization to achieve the full benefits of an information system. The three articles support these ideas in different ways. In the AI insurance study, Tavares, Leardini, and Pessoa (2025) found that the biggest barriers to AI adoption were organizational rather than technical. The company struggled with issues such as business IT alignment, employee knowledge, and innovation culture. The authors suggest improving these areas to help the organization get more value from AI, which reflects the idea of complementary assets. Likewise, Müller et al. (2025) found that organizations moving toward data-driven decision-making need more than just new technology. Their mitigating actions focus on developing digital skills, communicating the benefits of change, and encouraging employees to use data in their daily decisions. These actions show that leadership, culture, and employee engagement are just as important as the technology itself. The framework developed by Simeonova et al. (2024) highlights the importance of the people side of information systems. Their power-sensitive framework focuses on workplace relationships, everyday work practices, and how employees interact with technology. This suggests that organizations need effective communication and management practices in place if they want technology initiatives to succeed. All three articles also support the sociotechnical systems perspective. Tavares et al. (2025), Müller et al. (2025), and Simeonova et al. (2024) show that technology alone is not enough to create successful outcomes. Instead, organizations must make sure that their technology, employees, management practices, and culture work together. In that sense, all three papers reinforce Laudon and Laudon's (2026) argument that successful digital transformation requires both technical and organizational change.
The Brazilian insurance company case brings up some serious ethical questions that are worth examining even though the paper does not always address them directly. The most pressing of these is information rights and privacy. The textbook defines privacy as the claim of individuals to be left alone, free from surveillance or interference from other individuals or organizations, and notes that information technology makes that invasion cheap and effective (Laudon, Laudon, & Traver, 2026). When the company uses open data sources and AI to track customer health behaviors and spending patterns for fraud detection and premium pricing, most customers have no real awareness that this is happening or how much it influences decisions about their coverage (Tavares, Leardini, & Pessoa, 2025). There is also an accountability problem. The textbook points out that most AI systems operate as a black box, making it difficult to hold anyone responsible when automated decisions harm a customer (Laudon, Laudon, & Traver, 2026). The paper reinforces this by acknowledging that liability for AI caused harm remains legally unsettled in many places (Tavares, Leardini, & Pessoa, 2025). On top of that, the paper flags algorithmic bias as a real risk, noting that AI models can perpetuate existing social and cultural biases in their decision making, which in a healthcare insurance context raises serious fairness concerns (Tavares, Leardini, & Pessoa, 2025). To address these issues the company should prioritize transparent data disclosure, implement a human-AI balance where complex or sensitive cases are handled by people rather than automatedsystems, and conduct regular bias audits. The EU AI Act discussed in Chapter 4 offers a useful model here, requiring developers of high risk AI systems to take reasonable steps to protect consumers from algorithmic discrimination (Laudon, Laudon, & Traver, 2026). The Smukfest case raises ethical concerns around how guest data is collected and used during the festival's shift toward data driven decision making. Guests wear smart wristbands that track their movements, purchases, and behavioral patterns throughout the event, often without a full understanding of what that data is being used for (Müller, Zaggl, Svangaard, & Jakobsen, 2025). This is a clear challenge to information rights and privacy as the textbook defines it (Laudon, Laudon, & Traver, 2026). There is also an accountability gap worth noting. The paper admits that the roles of business leaders in managing data driven transformation are not well understood, which means guest data is being collected without clear governance or protection in place (Müller et al., 2025). What makes this worse is that the paper openly states Smukfest's digitalization is partly driven by leadership wanting to appear innovative rather than genuinely serving guests (Müller et al., 2025). That means guests are carrying the privacy risk while the organization captures the competitive benefit, which is exactly the kind of quality of life concern Chapter 4 raises around IS and everyday life (Laudon, Laudon, & Traver, 2026). To fix this Smukfest should give guests a real opt-in choice about data collection, limit what they collect to only what is operationally necessary, and refocus their data strategy around delivering a better guest experience rather than building a competitive profile.
The third paper raises ethical issues that feel the most personal because they affect how people experience their jobs every day. The paper describes workplaces where managers use IS tools to record employee emails and phone calls, block social media access, and track how long workers spend on every website, all in the name of maintaining control rather than improving performance (Simeonova, Kelly, Karanasios, &Galliers, 2024). The textbook is clear that privacy rights extend to the workplace and that digital surveillance of employees is a growing concern as information technology makes it easier to implement (Laudon, Laudon, & Traver, 2026). When workers are expected to follow script-like behavioral guidelines and have every communication monitored, it takes away the autonomy that is essential to a healthy work environment and can have real mental health consequences that these organizations do not appear to have considered. This is a direct quality of life issue as described in Chapter 4 (Laudon, Laudon, & Traver, 2026). However the paper's Case 2 shows that IS controls do not have to work this way. In that case the same kind of controls paradoxically led to employee empowerment and better knowledge sharing with a hospital network (Simeonova et al., 2024), which suggests the goal should be redesigning IS governance around enabling workers rather than surveilling them. Organizations should be transparent about what is being monitored and why, shift accountability toward outcomes rather than policing every interaction, and regularly check how their IS practices are affecting employee wellbeing.
Laudon, Laudon, and Traver. Management Information Systems: Managing the Digital Firm, 18th Edition, Pearson, ©2026. ISBN-13: 9780138344245.
Edval da, S. T., Leardini, M., & Marcelo Schneck de, P. P. (2025). How do the barriers that prevent or hinder the applicability of artificial intelligence impact its use in an insurance company: Revista de administracao e inovacao. Innovation & Management Review, 22(1), 13-46.
Sune Dueholm Müller, Zaggl, M., Svangaard, R., & Jakobsen, A. M. (2025). Digital transformation toward data-driven decision-making: Theorizing action strategies in response to transformation challenges. Communications of the Association for Information Systems, 56, 961-999.
Simeonova, B., Kelly, P. R., Karanasios, S., &Galliers, R. D. (2024). Power as "Present-inactions" in mundane information systems work. Journal of the Association for Information Systems, 25(4), 867-889.