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Being useful: How information systems professionals influence the use of information systems in enterprises

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Abstract

Organizations are keen to obtain as much value as they can from their information systems (IS) investments. While the first-order benefits of new information systems are usually obtained when they are used routinely, the full potential of new systems is only unlocked when they are used deeply. While the support of managers and peers can encourage individuals to use systems more deeply, the latter’s lack of technical know-how means that they may not be able to allay fears or doubts that arise as they improvise and experiment with new systems to infuse them into their work processes. We use social cognitive theory to frame infusion as a learning process, where individuals model their behavior based on the behaviors of others they observe in the environment and the resources available. We argue that individual users succeed in infusing new systems into their work when they interact with IS professionals. This interaction allows users to observe the actions of IS professionals and receive knowledge and guidance from them. The results of our study show that the discretionary behaviors exhibited by IS professionals positively affect users’ perceptions of the levels of usefulness and ease of use of new systems, encouraging them to use the systems as fully as possible. This study extends our understanding of the role that the discretionary behavior of IS professionals plays in enhancing the value that organizations obtain from their new IS investments.

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Authors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Hadi Karimikia, Narges Safari & Harminder Singh

Authors
  1. Hadi Karimikia
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  2. Narges Safari
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  3. Harminder Singh
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Corresponding author

Correspondence to Harminder Singh.

Appendices

Appendix A

Table 10 Details about comprehensive subscriber services system
Full size table

Appendix B

Table 11 Items and loadings
Full size table

Appendix C

Table 12 Demographic characteristics of online and offline survey respondents
Full size table

Appendix D

1.1 Fit indexes

Maruyama (1998) categorized different fit indexes into the main three types: absolute, relative and adjusted indexes. The absolute fit indexes indicate the degree to which an estimated model closely fits the sample data. Some of the commonly used absolute fit indexes include root mean squared error of approximation (RMSEA), the goodness-of-fit Index (GFI), and the root mean square residual (RMR).

RMSEA is the most popular fit index and has been reported a number of times by researchers (Kenny et al. 2015). Research on RMSEA cut-off points has distinguished between different values that are indicators of how closely models with estimated parameters fit the population’s covariance matrix (Byrne 2006). Hu and Bentler (1999) recommended that good-fitting models should yield RMSEA values of less than 0.06, while MacCallum et al. (1996) categorized fit quality based on three cut-off points of 0.01, 0.05, and 0.08, indicating excellent, good, and mediocre fit, respectively. A stricter threshold suggested by (Browne and Cudeck 1992) was that RMSEA values of about 0.05 or less are indicative of a close model fit and RMSEA values of about 0.08 or less indicate reasonable error of approximation. They also do not recommend an RMSEA value of greater than 0.1.

The goodness-of-fit index (GFI) ranges from 0 to 1, where 1 indicates a close or perfect fit. Chau (1997) recommended that good-fitting models yield GFI values of at least 0.90 or greater than 0.90, while other researchers have suggested GFI values ranging from 0.80 to 0.89 as demonstrating reasonable fitting models (Lai and Li 2005; Tarafdar et al. 2007). The root mean square residual (RMR) is the third fit index and should be small for good-fitting models (Tabachnick and Fidell 2001). RMR values less than 0.1 should indicate good-fitting models (Chau 1997). As it may sometimes be difficult to interpret an unstandardized residual since the scales of the variables affect the size of the residual, a standardized root mean square residual (SRMR) can be used (Tabachnick and Fidell 2001). The SRMR has a value of 0 to 0.1, where lower values are preferred. Hu and Bentler (1999) mention that values of about 0.08 or even less are desired.

Relative fit indexes, known as comparative fit indices, include the normed fit index (NFI), the non-normed fit index (NNFI) (or the Tucker Lewis Index (TLI)), the incremental fit index (IFI), and the comparative fit index (CFI) (Maruyama 1998). Generally, the recommended range for all relative fit indices for good-fitting models is between 0.90 and 1 (Chau 1997; Hair et al. 1998; Tabachnick and Fidell 2001). In addition, rules of thumb for good-fitting models are that cut-off values for CFI and TLI should be close to 0.95 or even higher (Hu and Bentler 1999). Adjusted indexes, known as parsimonious fit indexes and labelled as adjusted goodness-of-fit index (AGFI), can be adjusted for the number of parameters estimated in a model. The most common recommended AGFI ranges for good-fitting models are greater than 0.80 (Chau 1997; Segars and Grover 1993).

In terms of which fit indices should be reported, researchers have argued that it would be better to select fit indices from different categories, and they have thus suggested a variety of optional/categorical fit indexes. The recommended cut-offs for reasonable and good-fitting models are listed in Appendix Table 12. McDonald and Ho (2002) recommend that the most common fit indexes are the CFI, GFI, NFI, and the TLI. Hu and Bentler (1999) suggested a two-index presentation, always including SRMR with TLI, RMSEA, and the CFI. Kline (2015) strongly believes in reporting the Chi-Square test, RMSEA, CFI, and the SRMR.

Table 13 Recommended cut-offs of goodness-of-fit indexes
Full size table

Our research reports χ2/df, SRMR, IFI, NNFI (TLI), CFI, and RMSEA. It is important to note that the chi-square value is sensitive to the sample size and number of variables. Studies with large sample sizes rarely report a nonsignificant chi-square value, which would indicate a perfect fit, while conversely, significant chi-square values indicate a poor fitting model) (Tabachnick and Fidell 2001). Therefore, this study uses χ2/df, where the χ2/df values of good-fitting models ranges between 1 and 3 (Kline 2015) or 1 and 2 (Tabachnick and Fidell 2001). SRMR is the index that is the most sensitive to models with misspecified factor covariance(s) or latent structure(s) (Hu and Bentler 1999). IFI and NNFI (TLI) are chosen because they are relatively unaffected by sample size, which is useful since some fit indices are high merely because of the large sample sizes (Gerbing and Anderson 1992; Hu and Bentler 1999). In addition, NNFI (TLI) is not affected by the number of parameters of the model. Finally, CFI and RMSEA are the most frequently reported fit indices (Tabachnick and Fidell 2001). RMSEA is the index that is the most sensitive to models with misspecified factor loadings and varies with the number of variables (Kenny and McCoach 2003).

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Karimikia, H., Safari, N. & Singh, H. Being useful: How information systems professionals influence the use of information systems in enterprises. Inf Syst Front 22, 429–453 (2020). https://doi.org/10.1007/s10796-018-9870-7

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