Combined Matching

The combined effects of matched-pairs time criticality fit, interdependence fit, mobility fit, and information dependency fit, on use and user performance was also examined. The structural path model estimated to test the simultaneous effects of all the four matched-pairs on use and user performance is presented in Figure 6.10. The model has significant predictive accuracy for the endogenous constructs of use (R ² = 0.309) and user performance (R ² = 0.466), respectively. The model also has significant predictive relevance for the endogenous constructs of use (Q ² = 0.181) and user performance (Q ² = 0.290) as Q ² values are above 0 (Hair et al., 2014, p. 178). Figure 6.10 shows that all of the mHealth tool characteristics are significant for user performance along with time criticality fit, mobility fit, and information dependency fit matched-pairs. The CHW task characteristics time criticality support and information dependency support are significant for use along with the mobility fit and information dependency fit matched-pairs.

Figure 6.10. Path Model – Task-Technology Fit (TTF) as Matching: Simultaneous Effect

The path coefficients, t values, p values, f ² (q²) effects, and significance levels of the structural path model estimated to test a combined matching fit are summarized in Table 6.2.

Table 6.2. Results: Combined Matching Effects

Predictor (Matching Pair) Endogenous Construct

Use User Performance

Path Coefficient f ² q ² Path Coefficient f ² q ² Time Criticality x Time Criticality Support

(TC * TCS)

0.053 (0.79 ^NS) 0.00 0.01 0.131 (1.92 *) 0.03 ^S 0.01

Interdependence x Interdependence Support

(I * IS)

-0.053 (0.89 ^NS) 0.00 0.01 0.048 (0.82) 0.00 -0.00

Mobility x Mobility Support (M * MS) -0.245 (2.83 **) 0.08 ^S 0.04 ^S -0.233 (3.62 ***) 0.09 ^S 0.03 ^S Information Dependency x Information

Dependency Support (ID * IDS)

-0.125 (1.78 *) 0.02 ^S 0.02 ^S 0.210 (2.64 **) 0.07 ^S 0.04 ^S

R ² (Use) = 0.309, Q ² (Use) = 0.181, R ² (User Performance) = 0.466, Q ² (Use Performance) = 0.290 NS = Not Significant. *p < 0.10. **p < 0.05. ***p < 0.01, S = Small Effect

6.6 Discussion

6.6.1 Time Criticality Fit

A match between CHW perceptions of the mHealth tool’s time criticality support and the task need for time criticality does not have significant effects on use. CHW dependence on the mHealth tool is therefore not contingent upon this match. This fit pairing, however, significantly influences user performance. It is observed that matching functional support to the CHW need to respond urgently, e.g. during emergencies, leads to CHW delivery of higher quality patient care, more effectively and efficiently. Junglas, Abraham, and Ives (2009) similarly observed that in a hospital setting, a time criticality fit was not particularly important for nurse dependence on mobile technology (p. 641), but that utilizing a system that generated timely emergency notifications improved nursing performance (p. 642). The finding in this study is consistent and supports the notion that for effective patient care in time-sensitive scenarios, health workers require timely notifications (p. 635). It is also instructive to note that during emergencies in health settings, a lack of access to timely notifications has been observed to adversely affect patient care delivery (Junglas et al., 2009).

6.6.2 Interdependence Fit

Matching interdependence support of the mHealth tool to the CHW task need to co-operate with co-workers does not have significant effects on either use or user performance. CHW dependence on the mHealth tool for effective and efficient delivery of quality patient care is not conditional upon this match. It appears that CHWs tend to co-operate through established interpersonal relationships, informally co-ordinating and exchanging information. The notion that CHW co-workers would instantly adapt to mHealth tools for this purpose and disrupt their established mechanisms for facilitating interdependence is thus not reinforced. This non-significant finding corroborates Teo and Men’s (2008) observation that system utilization is often incompatible with existing work practices and that collaborating co-workers in a particular setting may prefer their more traditional customs of interpersonal contact (p. 569). This finding, however, contradicts Dishaw and Strong (1998b) who observed that similarly matching co-ordination tool functionality to co-ordination task activities, contributes to increased tool utilization (p.

115), although their study was situated in a software development setting. It is thus

evident that the significance or non-significance of an interdependence match for use and performance is context-sensitive.

6.6.3 Mobility Fit

A match of mobility support as an mHealth tool function to the CHW need for manoeuvrability has an unexpected negative effect on both use and user performance.

Contrary to expectations, this matched pair is associated with less CHW dependence on the mHealth tool, and lower performance, thus diminished effectiveness, efficiency, and quality in the delivery of patient care. Graphical plots of the interaction effects of this paired fit show that when there is high CHW task mobility, mHealth tool dependence and user performance are not contingent on technological support. However, in a low CHW task mobility environment, the tool used drives user dependence and task performance.

As such, the importance of tool design is recognized as an essential contributor to a positive fit between mobile technology and the user’s need for mobility (Junglas et al., 2009; p. 638). In a related study, Junglas, Abraham, and Ives (2009) observed that a similar construct, physical fit, was not found to be instrumental to mobile technology utilization and nursing performance (p. 641). It therefore appears that not every user may benefit from all mobile technology tools, especially when their tasks are information-intensive. The finding in this study however, contradicts Yuan, Archer, Connelly and Zheng’s (2010) observation that a fit between mobility task needs and mobile work support characteristics leads to an increase in user intentions to use mobile systems (p.

131). Users who perceive the tool as more supportive of mobility than their tasks necessitate, are more likely to use it and perform better, while others who acknowledge the mobility demands of their work may attribute less of their performance to tool functionality. Evidently, not all mobile technology users in particular contexts necessarily enjoy the same advantages that accrue from a tool’s supporting functionality.

6.6.4 Information Dependency Fit

The matching of information dependency support as an mHealth tool function to the CHW need to access information at the point-of-care has significant positive effects on user performance. This finding corroborates Junglas, Abraham, and Ives (2009) who established that data access for health workers was necessary for their effective patient care delivery (p. 637). However, the match had a negative impact on usage dependency

such that CHWs are less dependent on using the mHealth tool to perform tasks. This contradicts Yuan, Archer, Connelly, and Zheng (2010) who observed that a fit between location dependence tasks and equivalent mobile technology support functions signified a positive utilization experience for workers (p. 131). CHWs who exhibit high information dependency are dependent on the tool even if they do not always perceive functional support. It is only those users who exhibit low information dependency who report low dependence on the tool and are less likely to consider the support it may provide for the information dependency characteristics of their work.