The effect of boilerplate language on nonprofessional investors’ judgments

Figures & data

Figure 1. Predictions-H1 and H2. This figure presents predictions for the measures used in my experiment to capture participants’ investment likelihood judgments.

Table 1. Tests for hypotheses H1 and H2.

Panel A: Descriptive statistics (dependent variable: likelihood of investing; mean [standard deviation])
	Specificity
Prior risk information	Less specific	More specific	Average
Prior information	4.19 [2.07] N = 36	3.21 [2.25] N = 33	3.72 [2.20] N = 69
No prior information	4.82 [2.19] N = 50	4.55 [2.27] N = 51	4.68 [2.23] N = 101
Average	4.56 [2.15] N = 86	4.02 [2.34] N = 84	4.29 [2.26] N = 170
Panel B: Estimated marginal means (mean [standard error])
			95% confidence interval
Prior risk information	Specificity		Lower	Upper
Prior information	Less specific	4.55 [0.25]	4.07	5.04
	More specific	3.24 [0.26]	2.73	3.75
No prior information	Less specific	4.50 [0.21]	4.09	4.92
	More specific	4.59 [0.21]	4.18	5.00
Panel C: Analysis of covariance
	SS	Df	MS	F-stat	p-value
Pre-attractiveness	445.328	1	445.328	204.203	<0.01
Prior risk information	17.148	1	17.148	7.863	<0.01
Specificity	15.322	1	15.322	7.026	<0.01
Specificity X prior risk information	20.041	1	20.041	9.190	<0.01
Error	359.834	165	2.181
Panel D: Follow-up simple effects tests
Prior information condition
	SS	Df	MS	F-stat	p-value
Pre-attractiveness	177.92	1	177.92	88.220	<0.01
Specificity	28.686	1	28.686	14.218	<0.01
Error	133.162	66	2.018
No prior information condition
	SS	Df	MS	F-stat	p-value
Pre-attractiveness	268.421	1	268.421	116.608	<0.01
Specificity	0.290	1	0.290	0.126	0.723
Error	255.586	98	2.302

Note: The dependent variable is investment likelihood, measured by the question ‘What is the likelihood that you would consider HiddenEscapes Inc. as a potential investment?’ on an 11-point scale ranging from ‘1-Not at all likely’ to ‘11-Very likely’; p-values are two-tailed.

Figure 2. Results for H1 and H2. This figure presents the estimated marginal means of the measures used in my experiment to capture participants’ investment likelihood judgments.

Table 2. Test for hypothesis H3.

Panel A: Descriptive statistics (dependent variable: credibility construct after risk realisation; mean [standard error])
	Specificity
Prior risk information	Less specific	More specific	Average
Prior information	6.42 [2.03] N = 36	7.44 [2.49] N = 33	6.91 [2.30] N = 69
No prior information	7.35 [2.31] N = 50	7.55 [2.07] N = 51	7.45 [2.18] N = 101
Average	6.96 [2.23] N = 86	7.51 [2.23] N = 84	7.23 [2.24] N = 170
Panel B: Estimated marginal means (mean [standard error])
			95% confidence interval
Prior risk information	Specificity		Lower	Upper
Prior information	Less specific	6.50 [0.37]	5.780	7.227
	More specific	7.47 [0.38]	6.693	8.198
No prior information	Less specific	7.27 [0.31]	6.658	7.887
	More specific	7.56 [0.31]	6.954	8.165
Panel C: Analysis of covariance
	SS	Df	MS	F-stat	p-value
Pre-attractiveness	26.066	1	26.066	5.442	0.02
Prior risk information	7.900	1	7.900	1.649	0.20
Specificity	15.461	1	15.461	3.228	0.07
Specificity X prior risk information	4.349	1	4.349	0.908	0.34
Error	790.316	165	4.790

Panel D: Analysis of variance (dependent variable: difference in credibility construct before and after risk realisation)
	SS	Df	MS	F-stat	p-value
Prior risk information	3.628	1	3.628	1.174	0.28
Specificity	11.677	1	11.677	3.778	0.05
Specificity X prior risk information	0.693	1	0.693	0.224	0.64
Error	513.148	166	3.091

Note: The credibility construct is the arithmetical average of trustworthiness (‘How trustworthy is HiddenEscapes Inc.’s management?’) and competence (‘How competent is HiddenEscapes Inc.’s management at providing risk disclosures?’), both measured on an 11-point scale ranging from ‘1-not trustworthy/competent at all’ to ‘11-very trustworthy/competent’. The dependent variable for Panels A to C is the credibility construct after risk realisation. The dependent variable for Panel D is the difference between the credibility construct before and after the risk realisation. P-values are two-tailed.

Table

Less specific	More specific
We face intense competition in every aspect our business.	We face intense competition in every aspect of our business.
We currently compete with (1) other online travel agencies, (2) other travel information providers on the web, and traditional travel agencies who	We currently compete with (1) other online travel agencies, such as FarEscapes Inc. and ExclusiveEscapes Inc., (2) internet search engines offering travel itineraries and recommendations, and traditional travel agencies who
(3) can reach certain customers more easily. Some of these competitors have more financial resources than us, and	(3) can reach elderly customers more easily. Some of these competitors have more financial resources than us, and
(4) can or are using these resources in a variety of ways to compete with us, including improving their brand recognition.	(4) can use these resources in a variety of ways to compete with us. For example, one of our competitors has started to advertise on the television.
It is likely that competition risk will adversely affect us in the future. If that occurs, competition would have a considerably negative impact on our revenues.	It is likely that competition risk will adversely affect us in the future. If that occurs, competition would have a considerably negative impact on our revenues.