The Fit between the Task and Technology

The ‘fit’ between the technology used by the task performer, and the task performed by the technology user, is the third component of Task-Technology Fit (TTF). The ‘fit’

between task and technology is conceptualized by drawing on the work of Venkatraman (1989), who classified six perspectives of ‘fit’ (p. 438). Four of these ‘fit’ perspectives are used for the purposes of the present study. The first perspective of ‘fit’, Fit as Matching, refers to the pairing of two related variables (Venkatraman, 1989, p. 430). It has been used to inform ‘fit’ concepts in strategy research (Bergeron, Raymond and Rivard, 2001), and adapted for IS research. For example, Dishaw and Strong (1998b) expressed the relationship between user activities and tool functionality using a TTF matrix to illustrate their matching pairs (p. 110), and postulated that ‘fit’ as the matching of certain task (user) activities and technology (tool) support functions, occurs as shown on the shaded diagonal depicted in Figure 5.1. They then modelled its intended effect on tool use as depicted in Figure 5.2.

Figure 5.1. Task-Technology Fit (TTF) Matrix (Dishaw and Strong, 1998b, p. 110)

Figure 5.2. Fit of Tool Functionality to User Activity (Dishaw and Strong, 1998b, p. 109)

Similarly, in the present study, the relationship between the identified mHealth tool support functions and CHW task characteristics can be expressed using a TTF matrix as illustrated in Figure 5.3.

Figure 5.3. Task-Technology Fit (TTF) Matrix: Matching

These matching CHW task and mHealth tool characteristics form the shaded diagonal in Figure 5.3. Subsequently, the effects of TTF as Matching on use and user performance can be modelled as illustrated in Figure 5.4.

Figure 5.4. Task-Technology Fit (TTF) as Matching Model

The second perspective of ‘fit’, Fit as Moderation, occurs when the impact of a predictor variable on a criterion variable depends on the level of a third variable, the moderator (Venkatraman, 1989 p. 424). Venkatraman (1989) observed that Moderation could be examined by testing ‘fit’ as an interaction effect (p. 425). This perspective has been applied in IS research where TTF as Moderation was modelled as the interaction (Figure 5.5) of Knowledge Management (KM) task and technology characteristics (Teo and Men, 2008). Since its effects on a criterion are specified, Fit as Moderation has been classified as a criterion-specific form of ‘fit’ (Venkatraman, 1989).

Figure 5.5. Fit of Knowledge Management (KM) Technology to Knowledge Task (Teo and Men, p. 561)

This interaction is calculated as the cross-product of each task with each technology characteristic. In the present study, similar interactions can be conceptualized to include

both on-diagonal and off-diagonal cells, expressed using a TTF matrix as illustrated in Figure 5.6.

Figure 5.6. Task-Technology Fit (TTF) Matrix: Moderation (Interaction)

Based on the approach of Teo and Men (2008), the effects of TTF as Moderation on use and user performance can be modelled as depicted in Figure 5.7.

Figure 5.7. Task-Technology Fit (TTF) as Moderation (Interaction) Model

The third perspective of ‘fit’, Fit as Mediation, involves an intervening mechanism, a mediator, positioned between one or more predictor and criterion variables

(Venkatraman, 1989, p. 428). This perspective originated from research on strategic management (Bergeron et al., 2001), and can be used to conceptualize a ‘fit’ between task and technology characteristics. Venkatraman (1989) suggested that this ‘fit’ could be evaluated by testing the intervening, indirect effects of a predictor (or set of predictors) on a consequent variable. This perspective of ‘fit’ is adaptable to TTF research. For example, in their Fit-Focus model (Figure 5.8), which is representative of a traditional TTF model, Goodhue and Thompson (1995) positioned the TTF construct as a user-evaluation between antecedent task and technology characteristics, and consequent utilization and performance impacts (p. 215). Since its effects on a criterion are specified, Fit as Mediation has also been classified as a criterion-specific form of ‘fit’ (Venkatraman, 1989).

Figure 5.8. Fit-Focus Model (Goodhue and Thompson, 1995, p. 215)

The Goodhue and Thompson (1995) Fit-Focus model is adopted for the present study where the ‘fit’ of mHealth technology characteristics to CHW task characteristics is modelled as a user evaluation. Notably, it appears that in prior works, ‘fit’ as a user evaluation has not typically been described as mediating despite its positioning as an intervening variable between antecedent task and technology characteristics, and consequent use and user performance outcomes, and has neither been classified nor tested as such. This intervening ‘fit’ links these task and technology characteristics to use and user performance, as depicted in Figure 5.9.

Figure 5.9. Task-Technology Fit (TTF) as Mediation Model

The fourth and final perspective of ‘fit’ is Fit as Covariation, which is observed as a pattern of internal consistency among a set of underlying and theoretically related variables (Venkatraman, 1989 p. 435). This ‘fit’ perspective has been used in research on

‘fit’ in strategic management (Bergeron et al., 2001), and in the IS discipline in research on ‘fit’ for ERP implementation (Wang, Shih, Jiang and Klein, 2008). However, curiously, it has never been adapted for TTF research. Venkatraman (1989) suggested that ‘fit’ could be evaluated as a pattern of internally consistent, co-aligned factors, tested for its effects on use and user performance. In a broader IS study, Wang et al’s (2008) conceptualization of co-alignment as internal consistency for their study of Enterprise Resource Planning (ERP) success factors, is depicted in Figure 5.10. The co-alignment amongst these six success factors is further depicted as impacting on outcomes such as decision-making and control, and efficiency and profitability. Although this form of ‘fit’

was originally classified as criterion-free (Venkatraman, 1989), it can be evaluated as criterion-specific since its effects on an outcome or outcomes are specified.

Figure 5.10. The Fit as Covariation (Consistency) of Enterprise Resource Planning (ERP) Factors (Wang et al., 2008, p. 1613)

In the present study, this perspective of ‘fit’ as a pattern of co-aligned CHW task characteristics and mHealth technology characteristics is depicted³⁷ in Figure 5.11, with expected consequent effects for use and performance. Specifically, covariation ‘fit’ is represented as a second-order factor, with first-order task and technology factors as its reflective indicators (Venkatraman, 1990; Segars, Grover and Teng, 1998). This model specification has been described as a reflective first-order, reflective second-order (Type I) model, one in which the second-order construct (TTF) has underlying first-order factors (task and technology characteristics) as reflective dimensions, which themselves are measured using reflective manifest indicators³⁸ (Jarvis, Mackenzie and Podsakoff, 2003, p. 204).

37 For schematic clarity, the reflective indicators of the first-order factors (task and technology characteristics) are not drawn here. These task and technology characteristics are latent constructs, each being a reflective indicator of ‘fit’ (Jarvis et al., 2003).

38 Please refer Tables E.1 and E.2 of Appendix E for a detailed description of task and technology characteristics.

Figure 5.11. Task-Technology Fit (TTF) as Covariation Model

The technology the task performer uses to perform the task is linked through TTF theory to use and user performance. The TTF outcome constructs of use and user performance are discussed in Sections 5.3 and 5.4.