Understanding perceived performance impacts of mobile phone use among smallholders in Uganda: the influence of task-technology fit and current use

Figures & data

Figure 1. Conceptual Model for assessing perceived performance impacts of mobile phone use among smallholders in Uganda.

Source: (Aljukhadar et al., 2014; Dishaw & Strong, 1999; Goodhue & Thompson, 1995). TAC, Task-characteristics; TEC, Technology characteristics; PU, perceived usefulness; PEOU, Perceived ease of use; SN, social norm.

Table 1. Respondent’s sociodemographic characteristics (N = 250)

Respondent characteristics	Mean	Standard deviation
Age	39.03	13.86
Household income (UGX.)	2,109,376	2,333,859
Household size	6.29	2.53
Farm size in acres	4.01	2.90
Farming experience (years)	17.62	14.21
Phone ownership duration (years)	5.18	5.004
Education N Frequency (%)
Formal education	175	70
No formal education	75	30
Gender
Male	80	32
Female	170	68
Mobile phone ownership
Owners	158	63
Non-owners	92	37

N, Number of respondents in each category, Formal education; Primary (48%), Secondary (17%), Tertiary (4%) and University (1%).

Table 2. Construct reliability and validity of the measurement model

Construct	Α	(rho_A)	(rho_c)	AVE
PEOU	0.9	0.9	0.9	0.7
PPI	0.9	0.9	0.9	0.7
PU	0.8	0.8	0.8	0.6
SI	0.7	0.7	0.7	0.5
TAC	0.8	0.9	0.8	0.6
TEC	0.6	0.8	0.7	0.5
PTTF	0.7	0.7	0.7	0.5
U	0.8	0.8	0.8	0.6

TAC, task characteristics; TEC, technology characteristics; PPI, perceived performance impact; PTTF, Perceived Task-Technology Fit; U, current Utilization of Mobile phones; PEOU, perceived ease of use; PU, perceived usefulness; SN, social norm, and AVE = Average variance extracted; both rho_A and rho_c denote composite reliability; and α = Cronbach’s alpha.

Table 3. Heterotrait-monotrait criterion assessment of discriminant validity of constructs

	PEOU	PPI	PU	SI	TAC	TEC	PTTF	U
PPI	0.427
PU	0.31	0.393
SN	0.44	0.23	0.074
TACS	0.411	0.327	0.159	0.561
TEC	0.475	0.304	0.147	0.787	0.767
PTTF	0.61	0.633	0.502	0.112	0.399	0.237
U	0.398	0.17	0.11	0.71	0.625	0.719	0.151

TAC, task characteristics; TEC, technology characteristics; PPI, perceived performance impact; PTTF, Perceived Task-Technology Fit; U, current Utilization of Mobile phones; PU, perceived usefulness; SN, social norm.

Table 4. Indicators of model fit for the structural model

Latent variable	Adj. R^2	Q^2	VIF	SRMR
PTTF	0.157	0.076	2.165
PEOU	0.474	0.085	1.612
PU	0.250	0.005	1.435
U	0.509	0.036	1.414
PPI	0.406	0.059	1.023
Goodness-of-fit measure				0.063

PPI, perceived performance impact; PTTF, Perceived Task-Technology Fit; U, current utilization of mobile phones; PEOU, perceived ease of use; PU, perceived usefulness; SN, Social norm, Q² values are presented for endogenous variables only. Q2 > 0 is indicative of predictive relevance and VIF, Value Inflation Factor. R² values of 0.25, 0.50, and 0.75 signify weak, moderate, and substantial predictive accuracy of the exogenous factors on the endogenous variables (Hair et al., 2011; Henseler et al., 2009); SRMR, Standardized Root Mean Square Residual.

Table 5. Structural Model PLS path analysis results

Hypotheses	Path	Path coefficients	p Values	Significance levels	f^2	Outcomes
H5a	PTTF → PPI	0.626	0.000***	p < .001	0.65	Supported
H4c	SN → U	0.671	0.000***	p < .001	0.725	Supported
H3b	PTTF → PEOU	0.525	0.000***	p < .001	0.495	Supported
H1	TAC → PTTF	0.468	0.011**	P < .05	0.32	Supported
H3a	PTTF → PU	0.497	0.000***	p < .001	0.207	Supported
H2b	TEC → PEOU	0.338	0.000***	p < .001	0.205	Supported
H2a	TEC → PTTF	0.074	0.000***	p < .001	0.118	Supported
H5b	U → PPI	0.147	0.000***	p < .001	0.16	Supported
H4a	PEOU → U	0.092	0.391	p > .05	0.012	NS
H4b	PU → U	0.041	0.601	p > .05	0.03	NS

^** P < 0.05; ***P < 0.001;  f² is effect sizes; NS means the hypothesis was not supported.

TAC, task characteristics; TEC, technology characteristics; PPI, perceived performance impact; PTTF, Perceived Task-Technology Fit; U, current Utilization of Mobile phones; PEOU, perceived ease of use; PU, perceived usefulness; SN, social norm.

Table 6. PLS Multi-group analysis results

		Female (N = 170)		Male (N = 80)
Relationships	Path coefficient	t Values	P values	Path coefficient	t Values	P values	Invariant
PEOU -> U	0.206	2.653	0.008**	0.101	0.774	0.439	No
PTTF -> PEOU	0.422	6.391	0.000***	0.1	1.036	0.3	No
PTTF -> PU	0.324	3.579	0.000***	0.244	1.456	0.146	No
	Non-owner (N = 92)			Owner (N = 158)
TAC -> PTTF	0.213	1.418	0.156	0.384	3.069	0.002**	No
U -> PPI	−0.026	0.289	0.773	0.206	2.483	0.013**	No
	Educated (N = 175)			Uneducated (N = 75)
PEOU -> U	0.217	2.657	0.008**	0.173	1.541	0.123	No
TAC -> PTTF	0.276	2.6	0.009**	0.253	1.612	0.107	No
U -> PPI	0.15	2.521	0.012**	−0.041	0.346	0.729	No

** P < 0.05, ***P < 0.001.

TAC, task characteristics; TEC, technology characteristics; PPI, perceived performance impact; PTTF, Perceived Task-Technology Fit; U, current utilization of mobile phones; PEOU, perceived ease of use; PU, perceived usefulness. All respondents with levels of education from primary school to university were considered educated, and those without formal education were considered not educated; non-owners refer to those who do not own mobile phones, while owners are those who own mobile phones.

Table 7. Group differences in perceived performance impacts

Variable	Group	N	Mean Rank	H-statistic	df	P-value
Gender	Male	80	166.36	37.924	1	0.000
	Female	170	106.27
Age (Years)	18–25	42	117.65	18.533	4	0.001
	26–35	78	133.81
	36–45	55	149.74
	46–55	44	116.95
	>55	31	84.35
Education Level	No formal education	76	95.20	22.562	4	0.000
	Primary education	120	132.25
	Secondary education	42	152.32
	Other institutions	10	157.90
	University	2	146.75
Annual Income (Ugx)	< 500,000	65	81.74	53.466	5	0.000
	500,001-1,500,000	81	114.95
	1,500,001–2,500,000	30	156.20
	2,500,001–3,500,000	28	147.84
	3,500,001–4,500,000	17	168.47
	>4,500,000	29	174.53
Phone ownership	Yes	157	143.08	25.179	1	0.000
	No	93	95.83
Agric. Phone use	Never	66	121.14	40.119	4	0.000
	Rarely	83	99.68
	Sometimes	57	126.76
	Often	21	154.33
	Always	23	201.72

Ugx; Uganda shillings is the country’s legal currency.

Aljukhadar, M., Senecal, S., & Nantel, J. (2014). Is more always better? Investigating the task-technology fit theory in an online user context. Information & Management, 51(4), 391–397. https://doi.org/10.1016/j.im.2013.10.003

 Web of Science ®Google Scholar

Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9–21. https://doi.org/10.1016/S0378-7206(98)00101-3

 Web of Science ®Google Scholar

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual-performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689

 Web of Science ®Google Scholar

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

 Google Scholar

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(2009), 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014

 Google Scholar

Data availability statement

Data will be made available on request.