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Summary articles MTC
- Instelling
- Rijksuniversiteit Groningen (RuG)
A quite extensive summary of all the articles for the course Managing Technological Change in year 2020/2021.
[Meer zien]Voorbeeld 4 van de 64 pagina's
Voorbeeld 4 van de 64 pagina's
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Voorbeeld van de inhoud
Managing Technological ChangeLecture 1:
1. Brynjolfsson, E., and Hitt, L. M. 2000. Beyond computation: Information technology,
organizational transformation and business performance. Journal of Economic Perspective, 14(4),
23-48.
2. Markus, M. L., and Robey, D. 1988. Information technology and organizational change: Causal
structure in theory and research. Management Science, 34(5), 583-598.
Lecture 2:
3. Huang, J., Henfridsson, O., Liu, M. J., and Newell, S. 2017. Growing on steroids: Rapidly scaling
the user base of digital ventures through digital innovation. MIS Quarterly, 41(1), 301-314.
4. Cennamo, C. 2018. Building the value of next-generation platforms: The paradox of diminishing
returns. Journal of Management, 44(8), 3038-3069.
5. Kapoor, R., and Lee, J. M. 2013. Coordinating and competing in ecosystems: How organizational
forms shape new technology investments. Strategic Management Journal, 34(3), 274-296.
6. Zhu, F., and Liu, Q. 2018. Competing with complementors: An empirical look at Amazon.com.
Strategic Management Journal, 39(10), 2618-2642.
Lecture 3:
7. Maula, M. V. J., Keil, T., and Zahra, S. A. 2013. Top management’s attention to discontinuous
technological change: Corporate venture capital as an alert mechanism. Organization Science,
24(3), 926-947.
8. Tumbas, S., Berente, N., and vom Brocke, J. 2018. Digital innovation and institutional
entrepreneurship: Chief Digital Officer perspectives of their emerging role. Journal of Information
Technology, 33(3), 188-202.
9. Singh, A., Klarner, P., & Hess, T. (2019). How do chief digital officers pursue digital
transformation activities? The role of organization design parameters. Long Range Planning.
http://doi.org/10.1016/j.lrp.2019.07.001
10. Benaroch, M., & Chernobai, A. (2017). Operational IT failures, IT value destruction, and board-
level IT governance changes. MIS Quarterly, 41(3), 729–762.
Lecture 4:
11. Mathieson, K. 1991. Predicting user intentions: Comparing the technology acceptance model with
the theory of planned behavior. Information Systems Research, 2(3), 173-191.
12. Rai, A., Lang, S. S., and Welker, R. B. 2002. Assessing the validity of IS success model: An
empirical test and theoretical analysis. Information Systems Research, 13(1), 50-69.
13. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. 2003. User acceptance of
information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
14. Venkatesh, V., Thong, J. Y. L., and Xu, X. 2016. Unified theory of acceptance and use of
technology: A synthesis and the road ahead. Journal of the Association for Information Systems,
17(5), 328-376.
Lecture 5:
15. Kim, H.-W., and Kankanhalli, A. 2009. Investigating user resistance to information systems
implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567- 582.
16. Lapointe, L., and Rivard, S. 2005. A multilevel model of resistance to information technology
implementation. MIS Quarterly, 29(3), 461-491.
17. Jasperson, J. S., Carter, P. E., and Zmud, R. W. 2005. A comprehensive conceptualization of post-
adoptive behaviors associated with information technology enabled work systems. MIS Quarterly,
29(3), 525-557.
18. Hornyak, R., Rai, A., and Dong, J. Q. 2020. Incumbent system context and job outcomes of
effective enterprise system use. Journal of the Association for Information Systems, forthcoming.
, Brynjolfsson, E., and Hitt, L. M. 2000. Beyond computation:
Information technology, organizational transformation and
business performance.
Information technology, defined as computers as well as related digital communication technology,
has the broad power to reduce the costs of coordination, communications and information
processing. Information technology is best described not as a traditional capital investment, but as a
“general purpose technology”. In this paper, we review the evidence on how investments in
information technology are linked to higher productivity and organizational transformation, with
emphasis on studies conducted at the firm level. Our central argument is twofold: first, that a
significant component of the value of information technology is its ability to enable complementary
organizational investments such as business processes and work practices; second, these
investments, in turn, lead to productivity increases by reducing costs and, more importantly, by
enabling firms to increase output quality in the form of new products or in improvements in
intangible aspects of existing products like convenience, timeliness, quality, and variety.
Case examples
Companies using information technology to change the way they conduct business often say that
their investment in information technology complements changes in other aspects of the
organization. These complementarities have a number of implications for understanding the value of
computer investment. To be successful, firms typically need to adopt computers as part of a system
or cluster of mutually reinforcing organizational changes. For technological changes, it is important to
have an “all or nothing” approach. Three examples of changes are:
Transforming the firm: With introducing technological changes, it is also important to change
the behavior of employees. Behaviors related to the old system should be replaced with
behaviors that work the new system.
Changing interactions with suppliers: Computer-based supply chain integration is a form of a
technological change that can save costs and improve efficiency. To respond to these
technological change opportunities, firms are restructuring their supply arrangements and
placing greater reliance on outside contractors.
Changing customer relationships: The Internet has opened up a new range of possibilities for
enriching interactions with customers.
Large-sample empirical evidence on Information Technology, Organization and Productivity
The case study literature offers many examples of strong links between information technology and
investments in complementary organizational practices. The different links:
Information technology and productivity: many studies were performed to examine this
relationship. Taken collectively, these studies suggest that information technology is
associated with substantial increases in output and productivity. Questions remain about the
mechanisms and direction of causality in these studies. There appears to be a fair amount of
causality in both directions, certain organizational characteristics make information
technology adoption more likely and vice versa. Information technology has more effect in
the long term than the short term, suggesting that multiple years of adaptation and
investment is required before their influence is maximized.
Direct measurement of the interrelationship between Information Technology and
Organization: different studies were performed to measure the influence of organizational
complements on information technology. Some key findings:
o Use of information technology and extent of organizational change: IT investment is
greater in organizations that are decentralized and have a greater investment in human
capital.
, o Computers are most likely to substitute for jobs that rely on rule-based decision-making
while complementing nonprocedural cognitive tasks
o Strong connection between investment in high technology equipment and the demand
for skilled, educated workers.
o Greater levels of investment in information technology are associated with smaller firm
and less vertical integration.
o Firms that adopt decentralized organizational structures and work structures appear to
have a higher contribution of information technology to productivity.
The divergence of firm-level and aggregate studies on Information Technology and Productivity
While the evidence indicates that information technology has created substantial value for firms that
have invested in it, it has sometimes been a challenge to link these benefits to macroeconomic
performance. A major reason for the gap in interpretation is that traditional growth accounting
techniques focus on the (relatively) observable aspects of output, like price and quantity, while
neglecting the intangible benefits of improved quality, new products, customer service and speed.
There are several measurement problems that can affect economic growth and productivity
calculations. In a steady state, this makes little difference, because the amount of new organizational
investment in any given year is offset by the “depreciation” of organizational investments in previous
years. The net change in capital stock is zero. Thus, in a steady state, classifying organizational
investments as expenses does not bias overall output growth as long as it is done consistently from
year to year. However, the economy has hardly been in a steady state with respect to computers and
their complements. This has led to difficulties for measuring the effects. The productivity gains from
investments in new information technology have been underestimated, also because of failure to
fully account quality changes in consumable outputs. Another unmeasured benefit is from new
goods.
The collection of results suggests that information technology may be associated with increases in
the intangible component of output, including variety, customer convenience and service. Because it
appears that the amount of unmeasured output value is increasing with computerization, this
measurement problem not only creates an underestimate of output level, but also errors in
measurement of output and productivity growth when compared with earlier time periods which
had a smaller bias due to intangible outputs. Meanwhile, however, firm-level studies can overcome
some of the difficulties in assessing the productivity gains from information technology. For example,
it is considerably easier at the firm level to make reasonable estimates of the investments in
intangible organizational capital and to observe changes in organizations, while it is harder to
formulate useful rules for measuring such investment at the macroeconomic level.
Conclusion
Concerns about an information technology productivity paradox were raised in the late 1980s. The
firm-level studies suggest that, rather than being paradoxically unproductive, computers have had an
impact on economic growth that is disproportionately large compared to their share of capital stock
or investment, and this impact is likely to grow further in coming years. In particular, both case
studies and econometric work point to organizational complements such as new business processes,
new skills and new organizational and industry structures as a major driver of the contribution of
information technology. The use of firm level data has cast a brighter light on the black box of
production in the increasingly information technology-based economy. The outcome has been a
better understanding of the key inputs, including complementary organizational assets, as well as the
key outputs including the growing roles of new products, new services, quality, variety, timeliness
and convenience. Measuring the intangible components of complementary systems will never be
easy. But if researchers and business managers recognize the importance of the intangible costs and
, benefits of computers and undertake to evaluate them, a more precise assessment of these assets
needn’t be beyond computation.
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