Talk given by myself and David Wagner at WeB2019 about applying the well-established Product Lifecycle model when selecting the right delivery model of Cloud Computing for a digital product in the marketspace. Also provides some insights into how the delivery model should be adapted over time.

1. MANAGING CLOUD COMPUTING ACROSS THE PRODUCT
LIFECYCLE: DEVELOPMENT OF A CONCEPTUAL MODEL
TIMO PUSCHKASCH & DAVID WAGNER
MUNICH, 14.12.2019

2. OUTLINE
1. Motivation
2. Theoretical Foundation
3. Results
4. Conclusion

3. 1. MOTIVATION
 Focus of research: provide a conceptual model for the use of cloud computing in digital product
development
 Cloud computing is considered a – and possibly the – strategic driver of digital product development
(Bharadwaj et al. 2013; Hanelt et al. 2015), with the main reasons being low entry barriers and high
scalability
 In a recent review, only about 9% of the 285 articles under scrutiny were an attempt to conceptualize the
phenomenon of cloud computing, the vast majority of papers (235) were classified as atheoretical,
meaning that no theoretical frame was provided (Senyo et al. 2018)
 The product lifecycle was selected as an established theoretical model because benefits of cloud
computing are not equally distributed across the lifecycle, therefore a detailed analysis by lifecycle stage
is required

4. 2. THEORETICAL FOUNDATION
Cloud Computing
 a way for organizations to obtain “ubiquitous, convenient, on-demand network access to a shared pool of
configurable computing resources” (Mell and Grance 2011, p. 2)
 Four deployment models: (Mell and Grance 2011)
 private cloud: resources used exclusively by one organization
 public cloud: resources shared by all organizations opting to participate
 community cloud: similar to public cloud but used only be a defined group of organizations
 hybrid cloud: a combined use of at least two of the previous models
Product Lifecycle
 Used to define the lifecycle of products in a marketplace (Vernon 1966)
 Consists of four stages: Introduction Stage, Growth Stage, Maturity or Stabilization Stage, and Decline Stage
(Cox 1967; Rink and Swan 1979)

5. 3. RESULT – MAPPING CLOUD BENEFITS TO THE PRODUCT
LIFECYCLE
 the distinct challenges of each stage on the product lifecycle can be defined (Cox 1967; Levitt 1965)
 It can be assumed that the change in challenges across the product lifecycle stages will also require
different aspects from the underlying resources and infrastructure (Strader et al. 1998), of which cloud
computing can be a key part in digital products (Marston et al. 2011)

6. 4. RESULT – CLOUD-LIFECYCLE-BENEFIT-MATRIX
Applicability of Benefit
Stage Cloud Computing Benefit Private Cloud Community Cloud Public Cloud Hybrid Cloud
I. Introduction High scalability of resources Medium Medium High High
Lower cost of entry Low Medium High Medium
II. Growth Lower barrier for innovation Medium Medium High High
III. Maturity Lower barrier for innovation Medium Medium High High
Access to specialized technology Low Medium High High
Ability to optimize for specific workload High Medium Low High
IV. Decline High scalability of resources Medium Medium High High
Own depiction

7. 4. RESULT – MOST BENEFICIAL CLOUD DEPLOYMENT MODEL PER
LIFECYCLE STAGE
 Introduction: Public Cloud
 Growth: Public Cloud or Hybrid Cloud
 Maturity: Hybrid Cloud
 Decline: Public Cloud or Hybrid Cloud
Own depiction, based on mapping provided on previous slide

8. 5. CONCLUSION
 The contribution of this paper is twofold:
 Theoretical: We offer a theoretical concept for connecting the product lifecycle with cloud computing as a
foundation for further research and discussion by other researchers, thus contributing to closing the gap
identified by Senyo et al. (2018)
 Managerial: The paper provides a means for IT managers who are responsible for cloud computing in their
organization for determining the appropriate use of cloud computing when developing or improving a digital
product.
 We have identified three possibilities for furthering our research:
 Researching additional factors influencing the selection of cloud deployment models to facilitate the creation of a
holistic conceptual model to select the optimal model for a new digital product,
 Investigation of ways the critical switch from public to hybrid cloud models can be facilitated, and
 Empirical research into new digital product developments to verify the assumptions underlying our conceptual
model

9. REFERENCES
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., and Venkatraman, N. 2013. “Digital Business Strategy: Toward a Next Generation of Insights,” MIS
Quarterly (37:2), pp. 471–482.
Senyo, P. K., Addae, E., and Boateng, R. 2018. “Cloud Computing Research: A Review of Research Themes, Frameworks, Methods and Future Research
Directions,” International Journal of Information Management (38:1), pp. 128–139.
Hanelt, A., Piccinini, E., Gregory, R. W., Hildebrandt, B., and Kolbe, L. M. 2015. "Digital Transformation of Primarily Physical Industries - Exploring the
Impact of Digital Trends on Business Models of Automobile Manufacturers". Wirtschaftsinformatik 2015 Proceedings. 88.
Mell, P. M., and Grance, T. 2011. “The NIST Definition of Cloud Computing,” Special Publication (NIST SP) - 800-145.
Cox, W. E. 1967. “Product Life Cycles as Marketing Models,” The Journal of Business (40:4), pp. 375–384.
Rink, D. R., and Swan, J. E. 1979. “Product Life Cycle Research: A Literature Review,” Journal of Business Research (7:3), pp. 219–242.
Vernon, R. 1966. “International Trade and International Investment in the Product Cycle,” Quarterly Journal of Economics (80:2), pp. 190–207.
Levitt, T. 1965. “Exploit the Product Life Cycle,” Harvard Business Review (November 1965).
Strader, T. J., Lin, F.-R., and Shaw, M. J. 1998. “Information Infrastructure for Electronic Virtual Organization Management,” Decision Support Systems
(23:1), pp. 75–94.
Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., and Ghalsasi, A. 2011. “Cloud Computing — The Business Perspective,” Decision Support Systems
(51:1), pp. 176–189.