Abstract
Nonprofit organizations (NPOs) operate in environments characterized by growing competition for resources and greater stakeholder demands for accountability, which makes deploying and maintaining information systems a struggle. In this competitive, resource-constrained environment, enterprise Software-as-a-Service (SaaS) platforms offer NPOs a cost-effective way access reliable, low-maintenance information technology services. Thus, the extent to which NPOs assimilate SaaS is an important area of inquiry. Yet despite the wealth of research on organizational IT assimilation, we know little regarding whether, when, and how NPOs assimilate IT innovations. We further our understanding of NPO assimilation of SaaS by conducting a context-based study. To do so, we employ multiple methods with data collected from US-based NPOs executives. Our first study showed that organizational factors (e.g., readiness, top management participation) and environmental factors (e.g., social gains, industry) affect the degree to which NPOs assimilate SaaS. However, we also found that technological factors do not appear to play a significant role in NPO SaaS assimilation. We conducted a phenomenological analysis to shed further light on this unexpected finding. Our analysis found that readiness to adopt SaaS, perceptions about SaaS complexity, and the use of outside consultants all played a key role in NPOs’ assimilation process.
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Editor: Frantz Rowe.
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Appendices
Appendix A: Literature review
We used a systematic approach in reviewing the contextual assimilation literature for this study. We first searched the publication histories of each of the journals in the AIS basket of eight (Saunders et al, 2007) for studies on assimilation of technologies, and we performed more general searches by keyword using Google Scholar. We used keywords related to assimilation of technology, as well as those relating to NPOs and cloud computing. To ensure that we fully examined the literature on NPO assimilation, we also searched through the top journals from public administration, including Public Administration Review, Administration & Society, the Journal of Public Administration Research and Theory, the American Review of Public Administration, the International Review of Public Administration, and Nonprofit and Voluntary Sector Quarterly.
For inclusion in our literature review, a retrieved article had to fulfill a number of criteria. The article had to be empirical (either quantitative or qualitative) and have technology assimilation as a key dependent variable under examination. In the for-profit sector, we limited our set of articles to only those addressing late-stage assimilation (as opposed to early-stage adoption).
As the articles addressing assimilation in NPOs and assimilation of cloud technologies were much less numerous, we relaxed this requirement for studies examining assimilation in the nonprofit sector or of cloud technologies. A study in the table focused on adoption rather than assimilation is marked with an asterisk under the “Sector” column. In order to be classified as a nonprofit organization study in our literature review, the paper had to examine true nonprofit organizations such as voluntary organizations, charities, and the like. Public organizations such as governments or utility companies were excluded. Additionally, NPOs in the education or healthcare industries (e.g., universities and hospitals) were excluded (Table 10).
Appendix B: Items and psychometric properties
See Table 11.
Appendix C: Industry NTEE code counts
Table 12 is the breakdown of the different nonprofit verticals that responded to the two surveys request according to the National Taxonomy of Exempt Entities (NTEE).
Appendix D: Concept maps
There are several cues a reader should look for when interpreting these concept maps. First, a reader will see many words populated throughout the figure and attached to nodes (e.g., very small circles). These words are the concepts that are also developed through the previously mentioned bootstrapping procedure from related words in the dataset. All of the concepts that are relevant (i.e., likelihood greater than 0%) are shown in the figures. Next, one will see these concepts grouped by circles. These circles of concepts are called themes. The location of the themes is calculated by the relationship between each concept. Themes help the reader understand the co-occurrence between concepts. Third, the reader should note the links between concepts (i.e., lines). These links from the concept nodes represent a likelihood of the concepts being mentioned together. Fourth, spatially, the closer the concept nodes and themes are the more likely they are to be statistically related. For example, concept nodes on the opposite side of the figure are the least likely to be related (Figures 4, 5, 6).
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Wright, R.T., Roberts, N. & Wilson, D. The role of context in IT assimilation: A multi-method study of a SaaS platform in the US nonprofit sector. Eur J Inf Syst 26, 509–539 (2017). https://doi.org/10.1057/s41303-017-0053-2
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DOI: https://doi.org/10.1057/s41303-017-0053-2