The value of data and technology for corporations

By
Dax Patel
-
0
2323
Read the full article as a PDFDownload

Abstract

In this review paper, we look at the impact different technologies have had in recent years on corporations. From elementary implementations like digital data storage to more sophisticated applications like machines that “learn” from the data that pass through them, the introduction and popularization of computer-based technologies have had a big impact on corporations, which forms the major theme of this paper. We explore a variety of applications: from simple ones like digital work to more complex ones like big-data analytics. The aim of this paper is to introduce the most impactful results of technological advancements, especially those that have fared well commercially.

Introduction

With the rise of the internet and technologies that work over the internet, we have seen a shift in the paradigm of the way in which work is done. Teams now routinely communicate and coordinate via e-mail, instant messaging, and web-conferencing applications without ever meeting in person1. The possibility of working virtually is moving from the realm of science fiction to the everyday reality of the workplace2, which is something that we especially witnessed during the COVID-19 pandemic. Even before the pandemic, dedicated online communities like Nomad List provided a convenient way to become a digital ‘nomad’, someone who does only digital work and is therefore free to work from anywhere he/she desires. This trend of digital nomadism started to appear in the 2000s, when IT companies started accepting remote working arrangements for software developers. In the 2000s, Internet and mobile data speeds increased substantially while costs decreased, leading to wider usage and the development of new complementary technologies. ‘Social’ technologies like Flickr, LinkedIn, and Facebook, along with popularization of video conferencing played a big role in facilitating people to work remotely3.

Technology not only enables employees to collaborate regardless of their physical location, but it is also a great way for employers to explore new avenues of business that were previously just a faraway concept. There exist data-intensive technologies that are shaping how organizations evaluate and coordinate activities across boundaries, thus providing companies with new tools for conceiving and resolving problems that arise within partnerships4. Research has examined how data-intensive technologies create objectified representations of organizational processes and qualities5 and thus, act as “technologies of accounting”6 that make previously concealed characteristics visible and commensurate them into discreet metrics7’8. By gaining insight into the intricacies of organizational changes and their effects by thorough data analysis, a company may gain an unprecedented understanding about itself, which is termed as ‘Data-Induced Rationality’. ‘Data mining’—powered by Information Technology—acts as a reliable and efficient way to help businesses make informed decisions by sifting through sets of relevant information in search of key patterns.

Other technologies with a potential to help corporations include Big-Data Analytics (BDA) — which refers to finding meaningful patterns and data points from a large collection of data that is typically unstructured —and Artificial Intelligence (AI)—a highly capable and complex technology that aims to simulate human intelligence9. Weak AI is based on a variety of technologies that are able to achieve fragments of the simulation of human intelligence, such as face recognition. To better understand how AI differs from more traditional technology, it is useful to understand one commonly used component of AI, namely machine learning9, which is the ability of computers to adjust their behavior based on the data to which they are exposed10. Both BDA and AI aim to provide corporations with a new perspective that helps corporations see new ways to utilize their own expertise, like by showing them meaningful ways to diversify their offerings, helping them see patterns in consumer behavior to better predict sales in the near future, and such related applications.

The reasons for choosing these topics with the field of technology lie in the profound impact that these technologies have on corporations. Digital work opens a business up to potential employees who could never be hired when businesses required physical presence of every employee. While Digital Work may help create a better workforce, Information Technologies go beyond that by utilizing data-intensive technologies to optimize the efficiency of the work done in a corporation. Data-induced rationality refers to the mindset of managing organizational relationships on the basis of data, which is often formed when special units are formed within a corporation whose primary purpose is to monitor interactions as transfer of data within the said corporation. Big Data Analytics (BDA) is the pillar on which Information Technologies and Data-Induced Rationality function. Big Data Analytics are particularly impactful when we consider that many corporations have already had great success with them, like Coca-Cola launching a successful BDA-based customer retention program in 2015. Artificial Intelligence is a product of data analysis, often conducted on a scale large enough to give the machine an ‘intelligence’ whereby it behaves intelligently by implementing—and learning from—actions that have successful in the past. Such a technology has broad commercial applications, such as personal assistants like Siri and Cortana, self-driving ability in Tesla cars, and the Movie/TV recommendation engine in Netflix.

Digital Work

Digitization involves the creation of computer-based representations of physical phenomena2 like the symbolic meeting room that Zoom creates to emulate a real-world meeting room. Digital work gets great success in those industries where having access to physical objects is relatively unimportant for tasks that involve operating on representations that have no referents. Research conducted by Forbes in 2020 elucidates the benefits of digital work for a corporation:

  1. Productivity — Teleworkers are about 35-40% more productive than their office counterparts, and have measured an increase of at least 4.4% in output.
  2. Performance — With stronger autonomy via location independence, workers produce results with 40% fewer quality defects.
  3. Engagement — Higher productivity and performance combine to create stronger engagement, or in other words, 41% lower absenteeism. 
  4. Retention — 54% of employees say they would change jobs for one that offered them more flexibility, which results in an average of 12% turnover reduction after a remote work agreement is offered. 
  5. Profitability — Organizations save an average of $11,000 per year per part-time telecommuter, or 21% higher profitability.

For a real-life successful application of digital work, consider the Câmara Municipal de Cascais, which governs the town of Cascais in Portugal. In 2017, they created a connected and mobile-enabled workplace to transform the paper-based, siloed organization into an agile digital workspace. The organization aimed to improve employee mobility and productivity by enabling workers to manage their tasks with documents, email, and online collaboration software available from any device. All employees were migrated to Microsoft Exchange Online, connecting an entirely new segment of field workers to corporate email services. Without any training provided, employees started using the new company intranet tool to create plans, assign tasks and set due dates, improving collaboration by 20% and completing group initiatives about 7% faster11

Digital work, however tempting it may seem, is not always the best way to move forward practically. Outsourcing, which is a popular way of bringing down costs of development, is a great example of this. Work is generally outsourced to developing countries where labor is still comparatively cheaper and workforce is educated enough to do the work without much hassle. The success of this arrangement is dependent on many factors like access to proper resources at the work site, seamless communication between the managers, and adequate previous exposure to kind of work at hand. There have been instances of projects failing because the workforce or their managers were not well-acquainted with the fundamental bases of the work that the project demanded.

Digital work, however tempting it may seem, is not always the best way to move forward practically. Outsourcing, which is a popular way of bringing down costs of development, is a great example of this. Work is generally outsourced to developing countries where labor is still comparatively cheaper and workforce is educated enough to do the work without much hassle. The success of this arrangement is dependent on many factors like access to proper resources at the work site, seamless communication between the managers, and adequate previous exposure to kind of work at hand. There have been instances of projects failing because the workforce or their managers were not well-acquainted with the fundamental bases of the work that the project demanded.

A simple yet elegant solution to most of these problems is to keep the information regarding the project flowing both ways. Communication is key for teams to represent their views and helps other teams understand the new ideas or reservations they might have. Managing expectations also plays a role in the satisfaction of employees, managers, and customers. Understanding that digital work cannot completely replace traditional work in many industries is key to a good relationship between companies, employees and their clients. Apart from that, a supportive company culture that makes employees feel safe to communicate about what is hindering their productivity and the willingness of managers to solve the said issue goes a long way in making sure that the quality of digital work is on par with expectations.

Information Technologies

Over the last few decades, we have seen a marked growth in the sheer number of companies that are harnessing data-intensive information technologies to gain a competitive advantage in the business landscape. Even the companies whose main offerings are not tech-related are adopting data-driven technologies because they have widespread implications for the management of interorganizational collaboration12’13’14’15. Digital data allow organizations to establish knowledge-sharing routines for accessing partner capabilities and to create increasingly effective formal governance mechanisms16’17’13’18.

Digital technologies often mediate collaboration processes, shaping knowledge exchange13 and forming the basis for coordination19’20’18. Information systems can automate information exchange and coordination to facilitate traditional interorganizational routines, such as ordering, logistics, and interconnected production processes, making them more effective and reliable21’18. Digital technologies are commonly applied to facilitate governance, enabling more efficient monitoring22, reducing coordination and transaction costs23, and facilitating the creation of more complete contracts24’25.

In addition to the aforementioned efficiency-focused applications, information systems can also be designed to capture and analyze data more effectively for the purpose of identifying hidden patterns, trends, and opportunities within broader relationships to facilitate sensemaking and innovation21’18. Through predictive and prescriptive learning algorithms that accommodate diverse metrics and prescribe pragmatic solutions26, organizations may be able to create more encompassing solutions that effectively alleviate or overcome conflicting concerns27’28. Another way of looking at the evolution of IT is to focus on the specific contributions of technological inventions and advances to the industry’s key growth driver: digitization and the resulting growth in the amount of digital data created, shared, and consumed. Connecting people in a vast and distributed network of computers not only increased the amount of data generated but also led to numerous new ways of getting value out of it, unleashing many new enterprise applications and a new passion for “data mining”—focused analysis of a particular kind of data point.
The main benefits of Information Technology to a business are:

Improved Business Agility — Technology solutions allow small businesses to remain agile and quick to respond to change within the markets. Integration of various tech leads to increased collaboration among teams leading to better product development.

2. Improved Staff Coordination and Collaboration — Software products such as Asana and G suite improve collaboration among staff members. VOIP systems, conference calls, and telepresence software allow employees to interact remotely from any part of the world.

3. Automation and Productivity — Tapping into the benefits of high-speed internet and automation software allows for better handling of vital tasks. Automation tools can enhance digital presence and engagement with your customers.

4. Increased Revenue Streams — The creation of e-commerce stores enables sales teams to target a broader customer base. In 2019, consumers spent over $601.75 billion with U.S. online merchants, up 14% when compared to 2018.

5. Better Storage Solutions — IT infrastructure modernization enables businesses to drop outdated legacy systems for cloud storage solutions. Cloud storage systems are reliable, allowing for restricted access to business information from any place in the globe.

6. Financial Savings — Communication solutions such as video conferencing and VOIP enable businesses to save on travel costs and accommodation. Cloud services reduce data storage costs. Automation reduces the need for surplus staff, saving on labor costs.

7. Improved Data Security — Tech support for small businesses can help create encryptions and firewalls that enhance data security and safeguard sensitive data.

8. Better Customer Experience — Intuitive web designs can help streamline operations on the first point of contact with potential clients. Automated tools can enable customers to book appointments and consultations.

According to a 2020 report by the US Bureau of Labor Statistics, Employment of computer and information technology occupations is projected to grow 11% from 2019 to 2029, much faster than the average for all occupations. These occupations are projected to add about 531,200 new jobs, with companies looking to fill their ranks with specialists in cloud computing, collating and management of business information, and cybersecurity. SWZD reports that the biggest driver for IT budget increases of small businesses in 2021 is the need to upgrade IT infrastructure (57%), followed by increased security concerns (38%), employee growth (32%), business revenue increases (26%), increased priority on IT projects (26%), and change in operations due to COVID-19 (26%). Meanwhile, the major IT budget drivers for the largest enterprises in 2021 are the increased priority on IT projects (67%), need to upgrade IT infrastructure (53%), changes in operations due to COVID-19 (49%), increased security concerns (47%), and supporting the remote workforce during the pandemic (42%).

Figure 1 | Expected IT Budget Breakdown as a percentage of total IT budget YoY.
Source: SWZD29
Figure 2 | Expected IT Budget Breakdown as a percentage of total IT budget by Company Size
Source: SWZD29

Information Technologies have their own drawbacks too. If a business has been traditionally run—using paper to store business information—for a very long time, it is a very time-consuming and labor-intensive procedure to digitalize all the stored data. Further than that, the exchange of rich data on business processes can accentuate risks of unintended knowledge spillovers and asymmetric interdependencies. For example, although managers may pursue short-term benefits from an alliance, the choice to ignore long-term considerations can lead to asymmetric dependence on the partner or the failure of the collaborative relationship4. With regard to inevitable knowledge spillover between two different firms entering a partnership, some literature suggests that it is in the firm’s interests to endeavor to capitalize on incoming spillovers from a collaborative relationship, and attempt to limit the amount of outgoing knowledge, spilled out to the partner firm30, while some research recommends firms establish stringent protective measures to shut off their proprietary knowledge from partners31.

Due to the recent global supply chain disruptions and chip shortage, the IT industry is finding it a challenge to consistently innovate. Businesses in North America are finding it incredibly difficult to navigate a market filled with supply chain issues, increases in product costs and limited product availability.

Figure 3 | Anticipated IT challenges in North America and Europe
Source: SWZD29

Data-Induced Rationality

In Data-Induced Rationality, the use of the word “rationality” seeks to convey the encompassing effect of this technology that goes beyond mere affordances32. The concept of data-induced rationality can add to the existing understanding of virtual and data-based work, algorithmic evaluation, and technologies of accounting in organizations by elaborating how sensor data and their algorithmic analysis can become deeply entangled with human understandings33. The extensive real-time transfer of sensor data helps focus attention to the aspects of routines that can be captured and represented with digital data.

Although digital data flows only “capture” reality imperfectly along predefined dimensions, the combination of extensive data flows and specifically defined goals means that an organizational unit can believe digital data flows to fully represent all the relevant aspects of its task domain. In such cases, digital indicators do not merely direct employee attention7 but capture it in a way that makes complementary human observation appear unnecessary. This is to say that data are not merely a virtual approximation to be verified by the reality as in the case of simulations2 but in a sense appear as “hyperreal”—the dashboards and analyses are conceived as more accurate depictions of the operations than can be created through human observation and conversations.

In recent case studies, it has been found that companies considered such data-intensive collaboration to be fairly successful, and the technologies appeared to deliver many of the intended benefits4. In their case companies, top management believed that the digital units should be more innovative and more customer oriented than other units, goals that they associated with greater independence. In effect, this compartmentalization seems to be driven by management’s recognition of digital activities as distinctively innovative34. Further, analyses reveal that all digital service units were driven by a strong data-induced rationality, a shared normative understanding of what information ought to be attended to, how it should be processed, and how the conclusion should be enacted4.

Big-Data Analytics

With managers increasingly considering “big-data analytics” (BDA) to be a strategic resource for gaining a competitive advantage35’36 it becomes important to define what exactly BDA is. “Big data” refers to an exponential rise in the volume, variety, velocity, and veracity of data collection37’38 and “Analytics” refers to the data science methods applied to big data (statistical, predictive, and cognitive) to draw insights and drive evidence-based management decisions39.

BDA is different than other technological advancements in that this complicates traditional relationships between information technology (IT) vendors and clients40, in part because managers outside of an organization’s IT business unit increasingly approach vendors to launch BDA projects of their own41’42’43. Traditional IT vendor-client relationships are based on a service-dominant logic, emphasizing service provision to capture value by implementing prepackaged technology44. The dynamics of BDA suggest that organizations may need to rethink these service relationships to develop a different logic for innovation that relies on learning and experimentation as potential value creation mechanisms45. With BDA, neither organization fully envisions the end goal because BDA is a generative technology that evolves based on the organizations’ resources and understanding of BDA’s application46. The client needs to learn business applications of BDA, while the vendor needs to learn how to apply BDA to clients in diverse industries to help create value with BDA.

Exploring an alternative trajectory—as in the case of BDA—begins with a contextual trigger47, such as unsatisfactory performance48 or a desire to design an improved solution49. Changing a trajectory requires developing new knowledge and constant negotiation between groups shaping a technology50’51. Because alternative trajectories differ from the beliefs of the established trajectory, introduce new technology artifacts, and lack proven routines, pursuing new innovation trajectories is a constant process of learning to overcome knowledge and experience gaps51.

To pursue a new trajectory, however, organizations engage in exploratory learning while lacking experience and facing uncertainty52. Interorganizational learning is considered most effective when organizations engage in learning that is interactive53, collaborative54, and characterized by strong interorganizational ties55. Organizations attempting BDA innovation alone face heightened uncertainty because the vast unstructured data are beyond the purview of traditional data mining resources56’57’58’59.

Exploration requires experimentation to overcome the status quo experience established60. Thus, by exploring new trajectories, an organization risks reintroducing liabilities of newness61, reverting to the initial phases of the learning curve wherein performance is likely to be negative and may not trend positive for some time depending on the pace at which experience accumulates62. Financially, the exploration phase is a bit unclear since we see this in a 2022 case study: although the data do not reveal the analysis company’s exact expenses for working with its client for multiple years, the analysis company incurred significant sunk costs (i.e., sales and executive trips, human resources allocated for projects) to pursue a potential client in a new industry. An indication of the analysis company’s high up-front cost of collective experimentation is their offer of “innovation dollars” to share costs with its client and help close the negotiation on the first innovation lab contract63. This leads us to conclude that setting up a BDA framework not only presents a big learning curve to obtain desired results, but also an uncertainty in what it costs to set up that framework.

To combat the high costs of setting up a BDA framework, a variety of proprietary technologies have been invented to act as a ready-to-use platform that reduces costs for the corporation implementing a new framework by providing Big Data Analytics as a service. One of the biggest providers of BDA as a service is Amazon, which provides ‘Amazon S3’ as a data lake to store data, ‘AWS Glue’ as a data processor, ‘Amazon Redshift’ as an online analytical processing station, which then finally ‘Amazon QuickSight’ as a Visualization tool. Because the corporation can simply use the resources provided by a company like Amazon, implementing BDA becomes cost-effective and hassle-free. Another benefit for a corporation to choose a platform provided by a cloud-computing firm is that there are rarely ever any technical problems that can compromise the regular business of the said corporation. Regardless, BDA remains a growing field with immense future scope.

Figure 4 | Revenue from Big Data and Business Analytics worldwide
Source: IDC Worldwide Semiannual Big Data and Analytics Spending Guide29

Artificial Intelligence

Artificial Intelligence is a result of data analysis on a large scale. When huge chunks of data are analyzed by humans and the results fed to specialized software, the machine “learns” from the data—hence the term “machine learning”—and powers the metaphorical brain behind artificial intelligence. Contemporary AI solutions are predicated on machine learning algorithms with a voracious appetite for data, despite a history of diverse approaches and vision64. AI producers are companies, start-ups and research labs that use machine learning to develop applications. AI production needs human help not only to design cutting-edge algorithms (highly qualified engineers and computer scientists) but also at a much more basic level: to produce, enrich and curate data65. This is because the right data are not always available or accessible, and when they are, they often lack suitable annotations, and need human intervention before they can be used. Technological progress has not eliminated the need for micro-tasking, but transformed it, integrating humans and computers more tightly.

Well-publicized prospective literature emphasizes potential job losses in a world that thrives on data and automation66’67’68. While some scientific research corroborates these predictions69, other studies highlight historical dynamics of complementarity rather than substitution between human labor and machinery70’71, with more complex outcomes such as polarization between high-skilled and low-skilled workers72. Irrespective of all this, the global market for human-powered data services for AI is growing65.

Figure 5 | Use Cases for Artificial Intelligence
Source: SWZD29

In addition to AI, worldwide revenues for the artificial intelligence market, including software, hardware, and services, are estimated to grow 15.2% year over year in 2021 to $341.8 billion, according to a recent release of the International Data Corporation and further, it is also predicted that forecast to accelerate further in 2022 with 18.8% growth and remain on track to break the $500 billion mark by 2024.

There exist platforms which enable client companies to access workforce on demand, at a fraction of the cost of salaried staff, and usually with quicker turnaround times. They advertise themselves to clients as AI-service vendors, and to workers as providers of online earnings opportunities. Under pressure to perform, companies may find it cheaper to just leave aside cutting-edge technology, fragment the work into micro-tasks, and sub-contract them to low-paid workers through such platforms.

Amazon’s Mechanical Turk is a great example of such a platform that manages human contributions to AI preparation, verification and impersonation, but there are also some vendor companies that hire employees—typically in emerging countries—for the same purpose73. Moreover, the relatively recent need for widespread verification is not a temporary need but a recurrent one. As the sales of AI-based tools increase and affect a more diverse range of users, there will be a growing need to ensure that outputs meet expectations. Social media bots, at the heart of widespread reports of digital influence operations during major elections, are in fact mostly human-assisted and rely on similar systems to recruit and remunerate online workers74.

Conclusion

We have looked at some technologies that have been crucial in shaping the innovative practices and workforce that we see today, as well as leading the transition to the twenty first century by introducing practices that were practically unheard of just a few decades ago. In this day and age, however, we find these to be an integral part of any company trying to give itself an edge in a super competitive landscape. Big-Data Analytics and Artificial are not merely tools made to assist corporations, but also huge industries in themselves. The fact remains, however, that companies are continuously leveraging these technologies to help business better understand its customers or—better yet—the business itself.

  1. Maznevski, Martha L and Chudoba, Katherine M. 2000. “Bridging space over time: Global virtual team dynamics and effectiveness.” Organization science 473-492. [↩]
  2. Bailey, Diane E and Leonardi, Paul M and Barley, Stephen R. 2012. “The lure of the virtual.” Organization Science 1485-1504. [↩] [↩] [↩]
  3. Schlagwein, Daniel. 2018. “The History of Digital Nomadism.” [↩]
  4. Cepa, Katharina and Schildt, Henri. 2022. “Data-Induced Rationality and Unitary Spaces in Interfirm Collaboration.” Organization Science. [↩] [↩] [↩] [↩]
  5. Zuboff, Shoshana. 1988. In the age of the smart machine: The future of work and power. Basic Books, Inc. [↩]
  6. Miller, Peter and Power, Michael. 2013. “Accounting, organizing, and economizing: Connecting accounting research and organization theory.” Academy of Management Annals 557-605. [↩]
  7. Orlikowski, Wanda J and Scott, Susan V. 2014. “What happens when evaluation goes online? Exploring apparatuses of valuation in the travel sector.” Organization Science 868-891. [↩] [↩]
  8. Espeland, Wendy Nelson and Stevens, Mitchell L. 2008. “A sociology of quantification.” European Journal of Sociology/Archives Européennes de Sociologie 401-436. [↩]
  9. Glikson, Ella and Woolley, Anita Williams. 2020. “Human trust in artificial intelligence: Review of empirical research.” Academy of Management Annals 627-660. [↩] [↩]
  10. Samuel, Arthur L. 1959. “Machine learning.” The Technology Review 42-45. [↩]
  11. Zayed, Mohammad. 2018. 3 Great Examples of the Modern Digital Workplace in 2017. 11 January. Accessed October 25, 2022. https://www.linkedin.com/pulse/3-great-examples-modern-digital-workplace-2017-mohammed-zayed/. [↩]
  12. Boland Jr, Richard J and Lyytinen, Kalle and Yoo, Youngjin. 2007. “Wakes of innovation in project networks: The case of digital 3-D representations in architecture, engineering, and construction.” Organization science 631-647. [↩]
  13. Malhotra, Arvind and Gosain, Sanjay and El Sawy, Omar A. 2007. “Leveraging standard electronic business interfaces to enable adaptive supply chain partnerships.” Information Systems Research 260-279 [↩] [↩] [↩]
  14. Zammuto, Raymond F and Griffith, Terri L and Majchrzak, Ann and Dougherty, Deborah J and Faraj, Samer. 2007. “Information technology and the changing fabric of organization.” Organization science 749-762. [↩]
  15. Lee, Jaegul and Berente, Nicholas. 2012. “Digital innovation and the division of innovative labor: Digital controls in the automotive industry.” Organization Science 1428-1447. [↩]
  16. Hitt, Lorin M. 1999. “Information technology and firm boundaries: Evidence from panel data.” Information Systems Research 134-149 [↩]
  17. Baker, George P and Hubbard, Thomas N. 2004. “Contractibility and asset ownership: On-board computers and governance in US trucking.” The Quarterly Journal of Economics 1443-1479 [↩]
  18. Im, Ghiyoung and Rai, Arun. 2014. “IT-enabled coordination for ambidextrous interorganizational relationships.” Information Systems Research 72-92. [↩] [↩] [↩] [↩]
  19. Jonsson, Katrin and Holmström, Jonny and Lyytinen, Kalle. 2009. “Turn to the material: Remote diagnostics systems and new forms of boundary-spanning.” Information and organization 233-252 [↩]
  20. Jonsson, Katrin and Mathiassen, Lars and Holmström, Jonny. 2018. “Representation and mediation in digitalized work: Evidence from maintenance of mining machinery.” Journal of Information Technology 216-232 [↩]
  21. Im, Ghiyoung and Rai, Arun. 2008. “Knowledge sharing ambidexterity in long-term interorganizational relationships.” Management Science 1281-1296 [↩] [↩]
  22. Baker, George P and Hubbard, Thomas N. 2003. “Make versus buy in trucking: Asset ownership, job design, and information.” American Economic Review 551-572. [↩]
  23. Hitt, Lorin M. 1999. “Information technology and firm boundaries: Evidence from panel data.” Information Systems Research 134-149. [↩]
  24. Malone, Thomas W and Yates, Joanne and Benjamin, Robert I. 1987. “Electronic markets and electronic hierarchies.” Communications of the ACM 484-497 [↩]
  25. Aral, Sinan and Bakos, Yannis and Brynjolfsson, Erik. 2018. “Information technology, repeated contracts, and the number of suppliers.” Management Science 592-612. [↩]
  26. Faraj, Samer and Pachidi, Stella and Sayegh, Karla. 2018. “Working and organizing in the age of the learning algorithm.” Information and Organization 62-70. [↩]
  27. Pache, Anne-Claire and Santos, Filipe. 2013. “Inside the hybrid organization: Selective coupling as a response to competing institutional logics.” Academy of management journal 972-1001 [↩]
  28. Perkmann, Markus and McKelvey, Maureen and Phillips, Nelson. 2019. “Protecting scientists from Gordon Gekko: How organizations use hybrid spaces to engage with multiple institutional logics.” Organization Science 298-318. [↩]
  29. n.d. Big data and analytics: definitions, value, trends and applications. Accessed November 9, 2022. https://www.i-scoop.eu/big-data-action-value-context/. [↩] [↩] [↩] [↩] [↩]
  30. Belderbos, Rene and Carree, Martin and Lokshin, Boris. 2004. “Cooperative R&D and firm performance.” Research policy 1477-1492. [↩]
  31. Sammarra, Alessia and Biggiero, Lucio. 2008. “Heterogeneity and specificity of Inter-Firm knowledge flows in innovation networks.” Journal of management studies 800-829. [↩]
  32. Cabantous, Laure and Gond, Jean-Pascal and Johnson-Cramer, Michael. 2010. “Decision theory as practice: Crafting rationality in organizations.” Organization Studies 1531-1566. [↩]
  33. Orlikowski, Wanda J and Scott, Susan V. 2015. “Exploring material-discursive practices.” Journal of management studies 697-705. [↩]
  34. Yoo, Youngjin and Henfridsson, Ola and Lyytinen, Kalle. 2010. “Research commentary—the new organizing logic of digital innovation: an agenda for information systems research.” Information systems research 724-735. [↩]
  35. McAfee, Andrew and Brynjolfsson, Erik and Davenport, Thomas H and Patil, DJ and Barton, Dominic. 2012. “Big data: the management revolution.” Harvard business review 60-68 [↩]
  36. Ringel, M and Grassl, F and Baeza, R and Kennedy, D and Spira, M and Manly, J. 2019. “The Most Innovative Companies 2019: The rise of AI, platforms, and ecosystems.” Boston Consulting Group. [↩]
  37. Gerloff, Christian and Cleophas, Catherine. 2017. “Excavating the treasure of IoT data: An architecture to empower rapid data analytics for predictive maintenance of connected vehicles.” [↩]
  38. Tonidandel, Scott and King, Eden B and Cortina, Jose M. 2018. “Big data methods: Leveraging modern data analytic techniques to build organizational science.” Organizational Research Methods 525-547. [↩]
  39. Kiron, David and Prentice, Pamela Kirk and Ferguson, Renee Boucher. 2014. “The analytics mandate.” MIT Sloan management review 1. [↩]
  40. Topi, Heikki and Tucker, Allen. 2014. Computing handbook: Information systems and information technology. CRC Press. [↩]
  41. Furstenau, Daniel and Rothe, Hannes and Sandner, Matthias. 2017. “Shadow systems, risk, and shifting power relations in organizations.” Communications of the Association for Information Systems 3 [↩]
  42. Haffke, Ingmar and Kalgovas, Bradley and Benlian, Alexander. 2017. “Options for Transforming the IT Function Using Bimodal IT.” MIS quarterly Executive [↩]
  43. Woods, Viveca. 2015. Gartner Survey Shows More Than 75 Percent of Companies Are Investing or Planning to Invest in Big Data in the Next Two Years. 16 September. Accessed August 2022. https://www.gartner.com/en/newsroom/press-releases/2015-09-16-gartner-survey-shows-more-than-75-percent-of-companies-are-investing-or-planning-to-invest-in-big-data-in-the-next-two-years. [↩]
  44. Vargo, Stephen L and Lusch, Robert F. 2014. “Evolving to a new dominant logic for marketing.” The service-dominant logic of marketing. Routledge. [↩]
  45. Grover, Varun and Chiang, Roger HL and Liang, Ting-Peng and Zhang, Dongsong. 2018. “Creating strategic business value from big data analytics: A research framework.” Journal of management information systems 388-423. [↩]
  46. Wang, Shan and Yeoh, William and Richards, Gregory and Wong, Siew Fan and Chang, Younghoon. 2019. “Harnessing business analytics value through organizational absorptive capacity.” Information & Management 103-152. [↩]
  47. Henfridsson, Ola and Yoo, Youngjin. 2014. “The liminality of trajectory shifts in institutional entrepreneurship.” Organization Science 932-950. [↩]
  48. Greve, Henrich R. 1998. “Performance, aspirations, and risky organizational change.” Administrative Science Quarterly 58-86. [↩]
  49. Boland, Richard and Collopy, Fred. 2004. Managing as designing. Stanford business books Stanford, CA. [↩]
  50. Dosi, Giovanni. 1982. “Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change.” Research policy 147-162 [↩]
  51. Garud, Raghu and Rappa, Michael A. 1994. “A socio-cognitive model of technology evolution: The case of cochlear implants.” Organization science 344-362. [↩] [↩]
  52. Andries, Petra and Debackere, Koenraad and Van Looy, Bart. 2013. “Simultaneous experimentation as a learning strategy: Business model development under uncertainty.” Strategic entrepreneurship journal 288-310. [↩]
  53. Lane, Peter J and Lubatkin, Michael. 1998. “Relative absorptive capacity and interorganizational learning.” Strategic management journal 461-477. [↩]
  54. Larsson, Rikard and Bengtsson, Lars and Henriksson, Kristina and Sparks, Judith. 1998. “The interorganizational learning dilemma: Collective knowledge development in strategic alliances.” Organization science 285-305. [↩]
  55. Baum, Joel AC and Ingram, Paul. 1998. “Survival-enhancing learning in the Manhattan hotel industry.” Management science 996-1016. [↩]
  56. Baars, Henning and Kemper, Hans-George. 2008. “Management support with structured and unstructured data—an integrated business intelligence framework.” Information systems management 132-148 [↩]
  57. Constantiou, Ioanna D and Kallinikos, Jannis. 2015. “New games, new rules: big data and the changing context of strategy.” Journal of Information Technology 44-57 [↩]
  58. Loebbecke, Claudia and Picot, Arnold. 2015. “Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda.” The Journal of Strategic Information Systems 149-157 [↩]
  59. Yoo, Youngjin. 2015. “It is not about size: a further thought on big data.” Journal of Information Technology 63-65. [↩]
  60. Levitt, Barbara and March, James G. 1988. “Organizational learning.” Annual review of sociology 319-340. [↩]
  61. Haveman, Heather A. 1992. “Between a rock and a hard place: Organizational change and performance under conditions of fundamental environmental transformation.” Administrative science quarterly 48-75. [↩]
  62. Musaji, Serghei and Schulze, William S and De Castro, Julio O. 2020. “How long does it take to get to the learning curve?” Academy of Management Journal 205-223. [↩]
  63. Browder, Russell E and Koch, Hope and Long, Anna and Hernandez, James M. 2022. “Learning to Innovate with Big Data Analytics in Interorganizational Relationships.” Academy of Management Discoveries 139-166. [↩]
  64. Domingos, Pedro. 2015. The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books. [↩]
  65. Tubaro, Paola and Casilli, Antonio A and Coville, Marion. 2020. “The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence.” Big Data & Society. [↩] [↩]
  66. Brynjolfsson, Erik and McAfee, Andrew. 2014. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company [↩]
  67. Chui, Michael and Manyika, James and Miremadi, Mehdi. 2015. “Four fundamentals of workplace automation.” McKinsey Quarterly 1-9 [↩]
  68. Frey, Carl Benedikt and Osborne, Michael A. 2017. “The future of employment: How susceptible are jobs to computerisation?” Technological forecasting and social change 254-280. [↩]
  69. Acemoglu, Daron and Restrepo, Pascual. 2018. “The race between man and machine: Implications of technology for growth, factor shares, and employment.” American Economic Review 1488-1542. [↩]
  70. David, HJJOEP. 2015. “Why are there still so many jobs? The history and future of workplace automation.” Journal of economic perspectives 3-30 [↩]
  71. Bessen, James. 2019. “Automation and jobs: When technology boosts employment.” Economic Policy 589-626. [↩]
  72. David, H & Dorn, David. 2013. “The growth of low-skill service jobs and the polarization of the US labor market.” American economic review 1553-1597. [↩]
  73. Gray, Mary L & Suri, Siddharth. 2017. “The humans working behind the AI curtain.” Harvard Business Review 2-5. [↩]
  74. Gorwa, Robert & Guilbeault, Douglas. 2020. “Unpacking the social media bot: A typology to guide research and policy.” Policy & Internet 225-248. [↩]

RELATED ARTICLESMORE FROM AUTHOR