Ambiguity tolerance and confusion avoidance in the intent to purchase farmed fish

Figures & data

Figure 1. Conceptual model.

Table 1. Descriptive statistics of consumers’ ambiguity tolerance (AT), confusion avoidance (CA) and purchase intention (PI).

	Observations and references	Mean and S.D. of scores
Item	Factor: Ambiguity Tolerance (McLain 2009)
01.	I am tolerant of ambiguous situations.	4.78 ± 1.61
02.	I enjoy tackling problems that are complex enough to be ambiguous.	4.64 ± 1.58
03.	I generally prefer novelty to familiarity.	5.40 ± 1.36
04.	I prefer a situation in which there is some ambiguity.	4.03 ± 1.67
	Factor: Confusion Avoidance (Schweizer, Kotouc, and Wagner 2006)
01.	I am capable of thinking with the clarity or quickness with which I can normally think.	5.06 ± 1.23
02.	I enjoy troubleshooting that may create confusion.	5.09 ± 1.23
03.	I think the violation of trademarks of products is the basis of the likelihood of confusion.	5.56 ± 1.19
04.	I want to see a product (i.e., farmed fish species) with a common name in all markets.	5.50 ± 1.24
05.	I prefer food with a label to food without one.	5.66 ± 1.31
06.	I consider the externalities while I buy farmed fish.	5.30 ± 1.16
	Factor: Purchase Intention (Steadman and Rutter 2004)
01.	I intend to purchase farmed carp next time I buy fish.	4.06 ± 1.49
02.	I plan to buy farmed fish next time I buy carp.	3.94 ± 1.52
03.	I would like to buy farmed carp next time I buy fish.	3.89 ± 1.56
04.	I want to buy farmed carp next time I buy fish.	3.48 ± 1.56
05.	How likely is it that you will buy farmed carp the next time you buy fish?	3.88 ± 1.50

N = 1053; 1 = “strongly disagree” and 7 = “strongly agree”

Table 2. Kaiser–Meyer–Olkin (KMO) and Bartlett’s test of sphericity.

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		0.773
Bartlett’s Test of Sphericity	Approx. Chi-Square	3979.149
	Df	45
	Sig.	0.000

Table 3. Measurement model.

Construct and Items	λ	α	ρ	Eigenvalue	AVE
Ambiguity Tolerance		0.64	0.81	1.645	0.58
I enjoy tackling problems that are complex enough to be ambiguous.	0.823				0.68
I generally prefer novelty to familiarity.	0.688				0.50
I prefer situations in which there is some ambiguity.	0.774				0.60
Confusion Avoidance		0.70	0.83	2.005	0.62
I think the violation of trademarks of products is the basis for the likelihood of confusion.	0.798				0.64
I want to see a product (i.e., a particular farmed fish species) with a common name in all markets .	0.779				0.60
I prefer food with a label to food without one.	0.788				0.63
Purchase Intention		0.89	0.93	3.509	0.72
I intend to purchase farmed carp next time I buy fish.	0.811				0.66
I plan to buy farmed carp next time I buy fish.	0.869				0.76
I would like to buy farmed carp next time I buy fish.	0.871				0.76
I want to buy farmed carp next time I buy fish.	0.791				0.63
How likely is it that you will buy farmed carp next time you buy fish?	0.837				0.70

Table 4. Descriptive statistics and correlations among the latent constructs.

Item	Mean	S.D.	AT	CA	PI
Ambiguity Tolerance (AT)a	4.69	1.51	(0.58)	0.000	0.002
Confusion Avoidance (CA)a	5.71	1.09	−0.008	(0.62)	0.007
Purchase Intention (PI)a	3.87	1.44	0.049	0.089	(0.72)

ameasured on a 7-point Likert scale Note: the diagonal values represent AVE, and the lower diagonal value represents correlation between the constructs, whereas the upper diagonal values represent squared correlation between the constructs.

Table 5. Goodness of fit indices.

Category	Index	Recommended lowest value	Attained value
Absolute Fit	χ2	P > .05	Significant at 0.139
	RMSEA	<0.08^a,d	0.017
	GFI	>0.90^b,c	0.994
Incremental Fit	AGFI	>0.90^e	0.985
	CFI	>0.90^a	0.972
Parsimonious Fit	χ2/df (normed χ2)	<3-5^e	1.302

RMSEA = root mean square error approximation; GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index; CFI = comparative fit index. ^aHair et al. (2010), ^bForza and Filippini (1998), ^cGreenspoon and Saklofske (1998), ^dAwang (2012), ^eHaque et al. (2015).

Table 6. Demographic profile of the respondents.

Sample size (households)	1053
Age (mean ± St.dev.)	41.25 ± 13.28
Gender (%)
Male	69.20
Female	29.20
Do not want to mention	01.60
Income (’000) (mean ± St.dev.)	48.47 ± 31.78
Children (aged 1–16) in household (%)
Yes	53.30
No	46.70
Number of family members (mean ± St.dev.)	4.67 ± 2.33
Education in years (mean ± St.dev.)	13.89 ± 3.84
Profession (%)
Self-employed	58.40
Employed	41.60
Do you do most of the food shopping for your family? (%)
Yes	62.40
No	37.60
Overall fish consumption (%)
Less than once a month	3.10
Once a month	2.20
Several times a month	17.90
Once a week	9.20
Several-times a week	54.10
Daily	13.40
I buy fish from (%)
Wet market	60.80
Supermarket	07.40
Both	31.80
Are you a registered member of any environmental organization (%)
Yes	14.30
No	85.70

BDT 85 = 1 USD.

Table 7. Respondents’ frequency and levels of their given scores for different constructs.

Level	Ambiguity Tolerance			Confusion Avoidance			Purchase Intention
	AT1	AT2	AT3	CA1	CA2	CA3	PI1	PI2	PI3	PI4
Low	271	108	425	67	76	82	360	409	400	435
Medium	154	131	158	154	179	141	172	172	208	164
High	628	814	470	832	798	830	521	472	445	454
Total	1053	1053	1053	1053	1053	1053	1053	1053	1053	1053

Table 8. Results of structural equation modeling: standardized path estimates.

Structural Path		Standardized path co-efficient (β)	S.E	C.R	P
Ambiguity Tolerance → Purchase Intention	H1	0.06	0.034	1.726	0.084*
Ambiguity Tolerance → Confusion Avoidance	H2	0.12	0.028	4.130	0.00 ***
Confusion Avoidance → Purchase Intention	H3	0.002	0.037	0.063	0.95
Mediating Path		Test	Test Statistics	Standard Error	p-Value
Ambiguity Tolerance → Confusion Avoidance → Purchase Intention		Sobel Test	0.0540	0.004	0.956
	H4	Aroian Test	0.0525	0.004	0.958
		Goodman Test	0.0556	0.0041	0.955
Variables		Pearson Correlation
No. of family members and purchase intention		0.047			0.125
Income and purchase intention		−0.119			0.000***
Education and purchase intention		0.003			0.912
Age and purchase intention		−0.062			0.044**
Gender and purchase intention		0.022			0.485
Children in family and purchase intention		0.076			0.013**
Profession and purchase Intention		0.047			0.125
Fish consumption and purchase intention		0.005			0.859
Responsibility for food shopping and purchase intention		0.039			0.203

*significant at 10% level of significance. ** significant at 5% level of significance. *** significant at <1% level of significance.

Table 9. Results of the two regression models: the effects of demographics on confusion avoidance and ambiguity tolerance.

Demographic variables	Model 1 Confusion avoidance (CA)		Model 2 Ambiguity tolerance (AT)
	Coefficient	S.E.	Coefficient	S.E.
Age	−0.003	0.003	−0.006*	0.003
Income	−0.000***	0.000	0.000	0.000
Presence of children in family	0.140**	0.067	−0.018	0.074
Number of family members	0.006	0.014	0.009	0.016
Education	0.025***	0.009	0.003	0.010
Responsible for food shopping in the family	0.196***	0.071	0.047	0.078
Member of an environmental club	−0.235**	0.095	0.267**	0.104
Constant	5.314***	0.197	4.733***	0.215
	N = 1053; F(7, 045) = 5.43; Prob (F) = 0.000; R^2= 0.0351		N = 1053; F(7, 045) = 1.69; Prob (F) = 0.107; R^2 = 0.0112

S.E. = Standard errors; *** p < 0.01, ** p < 0.05, * p < 0.1. Parameter estimates from the multiple regression model.

Table

Consumer confusion indicates a state of mind that leads them to make imperfect purchasing decisions or to lack confidence in the accuracy of their purchasing decisions (Walsh 1999). In the eyes of consumers, a set of overwhelmingly large purchasing options can be a source of confusion (Babin, Darden, and Griffin 1994).

Consumer ambiguity is the absence of information required for consumers to understand a situation or identify a given future state (McLain 2009). In the eyes of consumers, the remanufacturing process may be a source of ambiguity (Hazen et al. 2012).

Figure A1. Consumers Confusion

Figure A2. Consumer ambiguity.

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