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DECISION MAKING IN THE ERA OF INFOBESITY: A STUDY ON INTERACTION OF GENDER AND PSYCHOLOGICAL TENDENCIES

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Purpose: This study examines information processing during consumer decision making on online platforms as influenced by gender differences and psychological tendencies. Further exploration is ‘how much information is too much information; leading to infobesity.’ Methodology: The methodology to address the objective included the questionnaires for assessment of psychological tendencies and naturalistic experiments to measure decision making in online conditions. An online marketplace prototype was created for mobile purchase, named ‘mobile bazaar,’ and another for hotel booking, named ‘backpackers.’ The prototype was designed in such a way that the manipulation of information presented to the participant is possible. Participants were recruited with purposive and snowball sampling method depending upon their willingness and familiarity with online market platforms. Final data were collected from Three hundred sixty-eight participants during the period of October 2017- March 2018. The data from questionnaires and the computerized task was scored and analyzed with SPSS version 21 with t-test, chi-square and logistic regression analysis methods. Main findings: The present study shows the influence of psychological tendencies (i.e., need for closure, exploratory tendencies, and uncertainty avoidance) and gender difference in decision making. Female seems to follow ‘process less to process better’ strategy, whereas, men seem to follow ‘process more to get better’ strategy. The findings also provided input to the debate of information measurement in consumer research. Implications: Understanding decision making features of Indian consumers can not only contribute to the understanding of the naturalistic decision-making process itself but also can provide inputs to the market researchers, designers, and policymakers. Novelty /originality of the study: The study was novel in terms of its use of the online marketplace prototype as a naturalistic decision making study method. This method allowed the researchers to examine participants' behavior (of information processing and decision making) in real like scenarios and yet had the luxury of manipulation of presenting information as per research design. Therefore the findings of present study will have more generalizability.
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Humanities & Social Sciences Reviews
eISSN: 2395-6518, Vol 7, No 5, 2019, pp 571-586
https://doi.org/10.18510/hssr.2019.7567
571 |www.hssr.in © Maidullah and Sharma
DECISION MAKING IN THE ERA OF INFOBESITY: A STUDY ON
INTERACTION OF GENDER AND PSYCHOLOGICAL TENDENCIES
Sana Maidullah1, Ankita Sharma2
1Assistant Professor, Tourism and Airlines, School of Management and Tourism, Lovely Professional University, Punjab,
India, 2Associate Professor, Department of Humanities and Social Sciences, Indian Institute of Technology Jodhpur, India.
Email: 1sana.24860@lpu.co.in, 2ankitasharma@iitj.ac.in
Article History: Received on 30th August 2019, Revised on 30th September 2019, Published on 16th October 2019
Abstract
Purpose: This study examines information processing during consumer decision making on online platforms as influenced by
gender differences and psychological tendencies. Further exploration is ‘how much information is too much information;
leading to infobesity.’
Methodology: The methodology to address the objective included the questionnaires for assessment of psychological
tendencies and naturalistic experiments to measure decision making in online conditions. An online marketplace prototype was
created for mobile purchase, named ‘mobile bazaar,’ and another for hotel booking, named ‘backpackers.’ The prototype was
designed in such a way that the manipulation of information presented to the participant is possible. Participants were recruited
with purposive and snowball sampling method depending upon their willingness and familiarity with online market platforms.
Final data were collected from Three hundred sixty-eight participants during the period of October 2017- March 2018. The
data from questionnaires and the computerized task was scored and analyzed with SPSS version 21 with t-test, chi-square and
logistic regression analysis methods.
Main findings: The present study shows the influence of psychological tendencies (i.e., need for closure, exploratory
tendencies, and uncertainty avoidance) and gender difference in decision making. Female seems to follow ‘process less to
process better’ strategy, whereas, men seem to follow ‘process more to get better’ strategy. The findings also provided input to
the debate of information measurement in consumer research.
Implications: Understanding decision making features of Indian consumers can not only contribute to the understanding of the
naturalistic decision-making process itself but also can provide inputs to the market researchers, designers, and policymakers.
Novelty /originality of the study: The study was novel in terms of its use of the online marketplace prototype as a naturalistic
decision making study method. This method allowed the researchers to examine participants' behavior (of information
processing and decision making) in real like scenarios and yet had the luxury of manipulation of presenting information as per
research design. Therefore the findings of present study will have more generalizability.
Keywords: Online decision makings, Information load, Gender differences, Psychological tendencies, Information processing,
Computerised task.
INTRODUCTION
Traditionally, the decision-making literature assumed that the decision-maker searches for sufficient information and then takes
the decision. However, with the technological revolution and internet boom, the reality is reversed, and many researchers are
arguing about the effect of over information on the decision. The limited capacity of information processing (Bettman, 1979)
also supports the link between information overload and decision difficulty. Pilli and Mazzon (2016) Suggested that at present
normative and empirical evidence favor an increase in the availability of information and choice (in the decision environment)
and at the same time, dysfunctionality of information overload.
Information overload or infobesity, a term grounded in Cognitive Psychology, has permeated academia; as the digital
revolution has made it a reality of personal, formal/informal and business world. One area which has seen the most drastic
change due to the digital revolution is a movement of the market to online platforms. Resnick (2001) suggested that the online
decision-making environment has almost all the features of real-life decision environment (space for error, confusion,
uncertainty, ambiguity, time constraint, profit/loss, etc.), and it even intensified it. Therefore, decision making research in an
online environment can help in providing insight into the contradictory conclusions related to the benefits of increasing
information/choice and dysfunctions originating from overload. Li and Zhang (2002) sums up the factors moderating decisions
regarding how much one needs information, how they seek, compare, and chose an alternative to individual factors, context
factors, and product characteristics. Present research considers the relevance of online platform for infobesity and importance
of individual factors and context factors, and thus explores the “amount of information processed by Indian males and
females and influence of psychological tendencies in information processing while deciding on the online market
platform.”
Humanities & Social Sciences Reviews
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THEORETICAL BACKGROUND
Information overload: When information is too much
The concept of information overload is discussed in different areas for a long time. Miller's (1956) study in the human capacity
of information processing has influentially contributed to its discussion, and this concept continues to be steadily explored
(Melinat, Kreuzkam, & Stamer, 2014). Speier, Valacich, and Vessey (1999a) quoted (Milord & Perry, 1977) for defining
information overload, which says, ‘Information overload occurs when the amount of input to a system exceeds its processing
capacity.’
Early work on the role of information overload on consumer decision making was done by (Jacoby, Speller, & Kohn, 1974).
Eppler and Mengis (2004) in their systematic review, reported that there was increasing interest in the topic from 1970 to 2000
in different areas such as organization science, accounting, management information system, and marketing. This exploration
mainly concentrated on definitions; situations explored causes, effects, and countermeasures.
Speier, Valacich, and Vessey (1999b) Concluded that information overload occurs when the time required to meet a decision-
makers processing requirement exceeds the amount of time available for such processing, resulting in degradation of decision
quality. Eppler and Mengis (2004) summarizes the causes of information overload to (1) information itself (quantity,
frequency, intensity, and quality), (2) person receiving and processing the information, (3) the task or processes need to be
completed, (4) organizational design, and (5) the information technology used. These five factors in combination create two
fundamental variables of information overload: information processing capacity and information processing requirement.
According to Miller’s magical number (1956), the information processing capacity is 7+/-2. However, Wright (1975) suggested
that “six is expected to represent the maximum comfortable load” in the decision-making process. Bettman (1979) also
supported Wright (1975) by concluding that consumers are likely to adopt simplifying information processing strategy when
the number of choice alternatives exceeds five. In the seminal work, (Malhotra, 1982) stated that the span of easily processed
information for any consumer lies somewhere between 10 or less than ten combinations of information. In the last decade, Lee
and Lee (2004) concluded that more than eight attributes significantly impose information overload and led to a negative effect
on choice quality.
Currently, there are two significant debates in the field which are related to the measurement of information (i.e., information
structure and information load) and effective countermeasures for information overload. Research on information overload
tends to consider information overload in terms of criteria rather than the alternative, i.e., it is less about extending the choices
and more focused on extra information about those choices. In connection to factors leading to information load, the extra
information about a choice could relate to the alignability or non-alignability across choices. The information overload is only
described using fix number of attributes and options (Pilli & Mazzo, 20152; Rudd, 2009), and no clear indication is there that
contribution of alignability is more or non-alignability is more in creating information overload. However, researches have
concluded that through the structural approach to information load suggests that it has a negative effect on the decision; there
have been debates over how best to define and measure the amount of information, leading to inconsistent conclusions (Hwang
and Lin, 1999; M.-H. Huang, 2000; Lee and Lee, 2004).
Though these findings provide the trend, the issue remains inconclusive when combined with the measurement of information
debate similarly, the question arises from the relative importance of task-related factors and individual factors in creating
information overload. Kock (2001) Explored if individual factors are comparatively more or less important than task factors in
influencing perceived information overload and concluded for their similar contribution.
INDIVIDUAL FACTORS
Most commonly considered internal variables are influencing information processing includes gender, social class, culture,
education, and knowledge (Putrevu, 2001). Gender continues to be one of the most common segmentation in marketing
researches, and hence (Kim, Lehto, & Morrison, 2007) questioned whether the consistent differential pattern of gender
differences could be created in information processing and judgment. Though the general conclusion is that there are no
significant gender differences in cognitive theories, the research literature of hemispheric dominance (Everhart et al. 2001),
cognitive processes (Geary 1996; Schumacher & Morahan-Martin 2001) personality (Darley & Smith 1995; Else-Quest et al.
2006; Meyers-levy & Loken 2014), information search and processing (ChanLin 1999; Kim, Lehto, & Morrison 2007;
Shashaani 1997) show significant differences in multiple dimensions. (Meyers-levy & Loken, 2014) commented that
investigation and understanding gender differences had been few in numbers, often a week in theory and somewhat limited in
progress, especially concerning consumer researches. They attempted to reinvigorate the inquiry by identifying the areas of
opportunity. The first area they identified was the development of an encompassing theory that can integrate connections
between gender’s cognitive processes and their temperament.
Humanities & Social Sciences Reviews
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There are several types of research evidence for each of these components reported separately. (Else-Quest et al., 2006) in their
meta-analysis on gender differences in temperament concluded for significant differences in inhibitory control, perceptual
sensitivity, surgency. Similarly, (Gysler, Brown Kruse, & Schubert, 2002) provided evidence for the link between risk
processing and ambiguity aversion, Coley and Burgess (2003) and (Tifferet &Herstein, 2012) suggested that women are more
cognitively, and affectively impulsive purchase decision-maker; Darley and Smith (1995) gave a selective model to explain
that men and women use different strategies and stages to process personal and environmental stimuli. However, there is a
need to synthesize these findings to develop a theory.
The literature on online consumer decision making has several established trends relating to gender, cognitive processes, and
purchase behavior. Park et al.(2009) suggested that females need more detailed information and assistance than males while
shopping, probably because males effectively use a more heuristic approach in information processing (Downing, Chan,
Downing, Kwong, & Lam, 2008). Previous studies also found a significant difference in the male and female motivational
levels of online shopping. Huang and Yang (Huang & Yang, 2010) reported that males are mainly looking for utilitarian
motivation (convenience, choice, availability of information, lack of social interaction and cost-saving) whereas females are
looking for hedonic motives (adventure, sociality and fashion and value). (Javadi, Rezaie Dolatabadi, Nourbakhsh, Poursaeedi,
& Asadollahi, 2012) commented that online decision making includes financial risk and non-delivery risk and therefore there
could be significant gender differences due to females being more risk aversive than males (Meyers-levy & Loken, 2014). In
general, researchers say “Women need the right atmosphere, space, and time to find just the right item. Men want to get the job
done”, according to the situation, they use different proposition to choose, select and process information.
Amidst the merger of one of the largest Indian online stores (Flipkart) with the world’s largest retailer (Walmart), the shift in
the market and change in nature of Indian economy is more pronounced now. The same is proven by the ASSOCHAM report
on India being one of the biggest online markets and is still rapidly growing (more than 100 million by the end of 2017,
ASSOCHAM, 2017). Given the above background extending and exploring (Meyers-levy & Loken, 2014), the suggestion for
integrating the connection between genders cognitive processes and their temperament is worthwhile. Therefore, the present
study aims to examine the Gender difference in information processing as influenced by psychological tendencies in
online decision making.” Specifically: gender differences in psychological tendencies; gender differences in processing
information load for online decision making; and psychological tendencies (need for control, uncertainty avoidance,
impulsivity, and exploratory tendencies) influencing information processing in both the genders.
Hypothesis: By previous literature, the hypothesized trend for the objective mentioned above could be as follows:
H1. Females will show more impulsive and exploratory behavior, whereas males will show more need for control and
uncertainty avoidance behavior.
H0. There will not be any gender difference in processing information load.
H2. Impulsivity and exploratory behavior will influence information processing in females.
H3. The need for control and uncertainty avoidance will influence information processing in males.
METHODOLOGY
Sample: The study included 368 participants (Female, N = 165, Mean age = 25.33; Male, N = 203, Mean age = 27.71). The
participants were recruited with a purposive and snowball sampling method, depending upon their willingness and familiarity
with the online marketplace. The data was collected from October 2017- March 2018 with the help of questionnaires to
measure the psychological tendencies and two computerized tasks to measure the information processing in online decision
making.
Questionnaire: Need for closure, uncertainty avoidance, impulsivity, and exploratory tendencies, all three of these tendencies
associate with the way an individual seek, process and react to the information and therefore they were explored in the study.
The need for closure scale (Roets & Van Hiel, 2007) has 42 items for five factors naming order, predictability, decisiveness,
ambiguity, and close-mindedness. Intolerance of Uncertainty Scale (Buhr & Dugas, 2002) was used to assess uncertainty
avoidance. The scale has 27 items for four factors naming desire for predictability, uncertainty paralysis, uncertainty distress,
and inflexible belief. The Barratt impulsivity scale (Patton et al, 1995) has 30 items for three factors naming non-planning
impulsiveness, cognitive impulsiveness, and motor impulsiveness The exploratory tendency scale (Raju & Venkatesan, 1980)
has 39 items for six factors naming Innovativeness, Risk-taking, Exploratory through shopping, Interpersonal communication,
Brand switching, and Information seeking.
Experiment- The e-commerce websites give an advantage of studying the decision-making process, similar to the real-world
scenario and it may also provide scope for experimental manipulation. Thus an online platform for the product purchase was
created. The mobile phone and hotel were chosen as the products due to it being an everyday use and being sold through e-
commerce websites in reality. The mobile site named ‘Mobile bazaar’ and hotel website named ‘Backpacker’ was created, and
Humanities & Social Sciences Reviews
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participants were asked to use the site, assuming that they are purchasing mobile and booking a hotel room. The experimental
interface was designed with the Xampp software; the frontend is HTML CSS JAVASCRIPT and Backend is PHP MySQL.
Analysis and result: The choice of mobile and hotel in connection with the participant’s gender and psychological tendencies
is analyzed to answer the objectives by using SPSS version 23. First, of, data was cleaned for any outliers or missing data, and
the fundamental analysis for gender difference was done
Table:1 t table Gender difference in psychological tendencies
Personality factor
Gender
N
Mean
Sd
T
P
D2
Need for closure: order
Female
165
35.06
5.746
-.194
.846
.020
Male
203
35.18
6.181
Need for closure: predictability
Female
165
25.65
5.252
-.704
.482
.073
Male
203
26.04
5.304
Need for closure: decisiveness
Female
165
19.78
4.008
.089
.929
.010
Male
203
19.74
3.889
Need for closure: ambiguity
Female
165
31.89
4.564
1.774
.077
.186
Male
203
30.98
5.154
Need for closure: close-
mindedness
Female
165
20.18
3.624
1.109
.268
.118
Male
203
19.75
3.635
Total need for closure
Female
165
132.5576
14.65387
.541
.589
.058
Male
203
131.6995
15.52077
Exploratory tendency:
repetitive behaviour proneness
Female
165
18.28
3.372
1.259
.209
.130
Male
203
17.87
2.899
Exploratory tendency:
innovativeness
Female
165
28.64
4.033
1.084
.279
.112
Male
203
28.18
4.144
Exploratory tendency: risk
taking
Female
165
25.96
3.420
.259
.796
.026
Male
203
25.87
3.260
Exploratory tendency: Through
shopping
Female
165
23.23
3.299
3.880
.000
.406
Male
203
21.87
3.394
Exploratory tendency:
interpersonal communication
Female
165
9.46
1.751
-.721
.471
.075
Male
203
9.59
1.708
Exploratory tendency: brand
switching
Female
165
22.34
3.369
1.115
.266
.145
Male
203
21.95
3.367
Exploratory tendency:
Female
165
38.28
4.238
2.690
.007
.282
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information seeking
Male
203
37.03
4.618
Total exploratory tendency
Female
165
166.19
16.331
2.261
.024
.236
Male
203
162.34
16.162
Intolerance uncertainty scale:
desire for predictability
Female
165
22.21
5.171
.125
.901
.012
Male
203
22.15
4.701
Intolerance uncertainty scale:
uncertainty paralysis
Female
165
17.69
4.821
.510
.610
.052
Male
203
17.44
4.639
Intolerance uncertainty scale:
uncertainty distress
Female
165
14.45
4.188
-.402
.688
.041
Male
203
14.62
4.005
Intolerance uncertainty scale:
inflexible uncertainty beliefs
Female
165
11.49
3.225
-.165
.869
.018
Male
203
11.55
3.247
Intolerance uncertainty scale:
total
Female
165
65.842
15.297
.058
.954
.006
Male
203
65.753
14.133
Impulsivity: non-planning
impulsiveness
Female
165
19.37
4.539
1.131
.259
.118
Male
203
18.85
4.217
Impulsivity: cognitive
impulsiveness
Female
165
12.54
2.555
1.372
.171
.143
Male
203
12.16
2.731
Impulsivity: motor
impulsiveness
Female
165
19.30
4.354
-.570
.569
.059
Male
203
19.55
4.030
Impulsivity: total impulsiveness
Female
165
21.59
5.387
.211
.833
.023
Male
203
21.47
5.018
*p<.05, **p< .01, ***p<.001
The results were per previous literature that on the majority of information processing psychological tendencies there were no
significant differences. However, females are higher in exploratory tendency, through shopping (t= 3.88, p<.00), information
seeking (t=2.69, p<.007), and total (t=2.261, p<.024) than males.
As the present study extends (Lurie, 2002) study with manipulation of attribute level also the number of alternatives provide
(following the traditional and structural approach); k-mean cluster analysis was done to create different information load.
Further descriptions and results are discussed separately for both the experiments.
Study 1: Mobile Experiment
This experiment follows the two-phase plan. To start the experiment, the participant had to fill in their demographic
information, then phase one starts. In phase one, participants had to create a wish list from multiple mobile choices. The mobile
options were organized into three categories (four, eight, and twelve options per page) x three attribute level (four, eight,
twelve attributes per option). Total of 72 mobile options were created and displayed on nine pages, creating nine factors (4 x 4,
4 x 8, 4 x 12, 8 x 4, 8 x 8, 8 x 12, 12 x 4, 12 x 8, and 12 x 12). The pages follow an increasing amount of options and attribute
wise information. The choices were arranged in basic (least price and lowest version of attribute), fully loaded (highest price
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and best version of attribute), and middle option (gradually increasing the price with mix versions of attributes), in every factor.
Fully loaded options were always the last display on every page.
In the phase-one, participants can see any page as many times as they want with the help of ‘previous’ and ‘next’ button, or
they can go to the cart with the help of ‘go-to final choice’ button. On the final page, the participant can make the decision or
terminate the experiment without choosing any option.
Figure 1: Depicting the 4x4 factor of the online platform
An example of elements, factor one (with four options and four attributes), factor two (with eight options and eight attributes),
and factor three (with twelve options and twelve attributes) is illustrated in table 2. For each choice, the brand was the first
attribute displayed, and the price was the last one.
Table 2: Includes details of attributes provided in each option level
RESULTS AND DISCUSSION
The k-mean cluster was computed to combine similar choice categories. The analysis resulted in two distinct categories, and
according to experimental understanding, they were named as low and high information load.
Table 3: Chi-square table information load and factor wise
Factor wise options
Cluster 2
Chi-square
High information
4*4
0
368.0***
4*8
0
4*12
0
8*4
10(100.0%)
8*8
68(100.0%)
8*12
53(100.0%)
12*4
5(100.0%)
Choice
task
Brand
Ram
Primary camera
Processor
Screen size
Internal memory
Expandable memory
Weight
Sim slot
Battery life
Colour
Price
1
2
3
4
5
6
7
8
9
10
11
12
4X4
8X8
12X12
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12*8
53(100.0%)
12*12
68(100.0%)
*p<.05, **p< .01, ***p<.001
Table 4: chi-squaredtableGender wise
Gender
Cluster 1
Cluster 2
Low Information
High information
X2
Female
60(34.4%)
105(63.6%)
Male
51(251%)
152(74.9%)
5.46***
*p<.05, **p< .01, ***p<.001
Table 5: t table personality factor and mobile information load wise
Personality factor
Number of Case
N
Mean
Sd
T
P
D2
Need for closure: Order
Low information
111
35.52
5.366
.832
.406
0.09.
High information
257
34.96
6.231
Need for closure:
Predictability
Low information
111
26.59
5.261
1.737
.083
.005
High information
257
25.56
5.263
Need for closure:
Decisiveness
Low information
111
20.24
4.034
1.565
.118
0.173
High information
257
19.54
3.884
Need for closure:
Ambiguity
Low information
111
32.14
4.678
1.946
.052
0.223
High information
257
31.06
4.984
Need for closure: Close-
mindedness
Low information
111
20.14
3.670
.698
.486
.076
High information
257
19.86
3.618
Total need for closure
Low information
111
134.6486
14.9354
2.148
.032
.244
High information
257
130.9767
15.0988
Exploratory tendency:
Repetitive behaviour
proneness
Low information
111
18.81
3.192
3.101
.002
.349
High information
257
17.72
3.040
Exploratory tendency:
Innovativeness
Low information
111
28.95
4.012
1.757
.080
.199
High information
257
28.14
4.115
Exploratory tendency:
Risk taking
Low information
111
26.37
3.278
1.754
.080
.199
High information
257
25.71
3.337
Exploratory tendency:
Exploratory through
shopping
Low information
111
23.15
3.512
2.509
.013
.280
High information
257
22.19
3.338
Exploratory tendency:
Interpersonal
communication
Low information
111
9.48
1.612
-.402
.688
.047
High information
257
9.56
1.776
Exploratory tendency:
Brand switching
Low information
111
22.62
2.976
1.875
.062
.218
High information
257
21.91
3.509
Exploratory tendency:
Information seeking
Low information
111
37.97
4.358
1.069
.286
.121
High information
257
37.43
4.544
Total Exploratory
tendency
Low information
111
167.3604
15.9572
2.559
.011
0.291
High information
257
162.6498
16.3134
Intolerance uncertainty
scale: Desire for
Predictability
Low information
111
22.97
4.639
2.053
.041
.236
High information
257
21.83
4.993
Intolerance uncertainty
scale: Uncertainty
Paralysis
Low information
111
18.03
4.475
1.272
.204
.146
High information
257
17.35
4.811
Intolerance uncertainty
scale: Uncertainty
Distress
Low information
111
15.53
3.712
3.086
.002
.357
High information
257
14.12
4.169
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Intolerance uncertainty
scale: Inflexible
Uncertainty Beliefs
Low information
111
12.24
3.131
2.841
.005
.323
High information
257
11.21
3.232
Intolerance uncertainty
scale: Total
Low information
111
68.7748
13.5570
2.586
.010
.299
High information
257
64.5058
14.9345
Impulsivity: non-planning
impulsiveness
Low information
111
19.21
4.489
.355
.723
.040
High information
257
19.03
4.319
Impulsivity: Cognitive
Impulsiveness
Low information
111
12.63
2.663
1.434
.152
.161
High information
257
12.20
2.649
Impulsivity: Motor
Impulsiveness
Low information
111
19.68
4.256
.727
.468
.083
High information
257
19.33
4.142
Impulsivity: total
impulsiveness
Low information
111
21.41
5.408
-.267
.789
.030
High information
257
21.57
5.089
*p<.05, **p< .01, ***p<.001
Statistically, all four choice options (4x4, 4x8, and 4x12) formed the low information category, and all 8 and 12 choice options
created a high information category. It appears that both males and females have used high information options for decision
making that low information options; however, psychological tendencies interact differently with information. Individuals
select low information choices if they are high on need for closure: ambiguity (t=1.946, p<.05), total need for closure (t=2.148,
p<.03), repetitive behaviour proneness (t=3.101, p<.002), exploratory through shopping (t=2.559, p<.001) desire for
predictability (t=2.053, p<.041), uncertainty distress (t=3.086, p<.002), inflexible uncertainty belief (t=2.841, p<.005) and total
intolerance for uncertainty (t=2.586, p<.01).
Table 6: Logistic table
Variable
b [95%C.I. B]
S.E.(b)
Wald
Sig
Exp(b)
Need for closure: Order
0.044
0.032
1.872
0.171
1.045
Need for closure: Predictability
-0.087
0.039
4.832*
0.028
0.917
Need for closure: Decisiveness
-0.031
0.048
0.426
0.514
0.969
Need for closure: Ambiguity
0.005
0.038
0.014
0.906
1.005
Need for closure: Close-mindedness
0.056
0.048
1.373
0.241
1.058
Gender (female)
3.645
2.305
2.5
0.114
38.296
Gender (female) * Need for closure Order
-0.082
0.046
3.124
0.077
0.922
Gender (female) * Need for closure Predictability
0.11
0.055
4.043*
0.044
1.116
Gender (female) * Need for closure Decisiveness
0.009
0.066
0.018
0.894
1.009
Gender (female) * Need for closure Ambiguity
-0.069
0.057
1.453
0.228
0.934
Gender (female) * Need for closure Close-mindedness
-0.109
0.068
2.591
0.107
0.897
Constant
1.214
1.526
0.633
0.426
3.368
Omnibus χ2 (11) = 18.990, p>.05, R 2= .050(Cox & Snell), .071 (Negelkerke) *p<.05, **p< .01, ***p<.001 Ϯ—95% C.I. for
EXP(B)
A logistic regression analysis shows that there is a significant influence of the need for closure subset predictability with
gender and as well as alone on the selection of information (χ 2 (11) = 18.990, p > .05). The model explained 7.1% variance in
information selection (Negelkerke R) and was able to identify 71.5% of cases accurately. The sensitivity of the model was
98.1%,and specificity of the model was 9.9%. The results show that for every unit decrease in predictability the odds for
making a decision from high information load is .917,and when gender interact with predictability the result shows that for
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every unit increase in predictability for males (in comparison to females) the odds for making a decision from high information
load is 1.116.
Table 7: Logistic table
Variable
b [95%C.Ib.]
S.E.(b)
Wald
Sig
Exp(b)
Exploratory tendency: Repetitive behaviour proneness
-.078
.073
1.156
.282
.925
Exploratory tendency: Innovativeness
.046
.057
.636
.425
1.047
Exploratory tendency: Risk-taking
-.031
.067
.214
.643
.969
Exploratory tendency: Exploratory through the shopping
-.098
.067
2.144
.143
.906
Exploratory tendency: Interpersonal communication
.108
.106
1.039
.308
1.114
Exploratory tendency: Brand switching
.021
.070
.091
.763
1.021
Exploratory tendency: Information seeking
-.032
.046
.505
.477
.968
Gender (Female)
-1.657
2.564
.418
.518
.191
Gender (Female) * Exploratory tendency: Repetitive behavior
proneness
-.080
.100
.647
.421
.923
Gender (Female) * Exploratory tendency: Innovativeness
-.101
.080
1.604
.205
.904
Gender (Female) * Exploratory tendency: Risk-taking
-.013
.092
.021
.886
.987
Gender (Female) * Exploratory tendency: Exploratory through
shopping
.029
.097
.091
.763
1.030
Gender (Female) * Exploratory tendency: Interpersonal
communication
-.078
.146
.286
.593
.925
Gender (Female)* Exploratory tendency: Brand switching
.027
.102
.069
.793
1.027
Gender (Female) * Exploratory tendency: Information seeking
.143
.071
4.062*
.044
1.154
Constant
3.908
1.807
4.678*
.031
49.791
Omnibus χ 2 (15) = 25.101*, p >.05, R 2= .066(Cox & Snell), .093 (Negelkerke) *p<.05, **p< .01, ***p<.001 Ϯ—95% C.I. for
EXP(B)
Logistic regression analysis shows that there is a significant influence of exploratory tendency subset Information seeking with
the gender on the selection of information 2 (15) = 25.101, p < .05). The model explained 9.3% variance in information
selection (Negelkerke R) and was able to identify 72.6% cases accurately. The sensitivity of the model was 97.3%, and the
specificity of the model was 15.3%. The exploratory tendency does not influence decision making; however, when gender
interacts with the exploratory tendency of information seeking, the results show that for every unit increase in exploratory
tendency subset information seeking for males (in comparison to females) the odds for using high information load is 1.154.
Table 8: Logistic table
Variables
b [95%C.I. b]
S.E.(b)
Wald
sig
Exp(b)
Desire for Predictability
.021
.053
.153
.696
1.021
Uncertainty Paralysis
.015
.055
.074
.785
1.015
Uncertainty Distress
-.122
.064
3.617*C
.057
.885
Inflexible Uncertainty Beliefs
-.077
.068
1.289
.256
.926
Gender (Female)
-1.652
1.210
1.863
.172
.192
Gender (Female) * Desire for
Predictability
-.061
.075
.666
.414
.941
Gender Female) * Uncertainty
Paralysis
.135
.083
2.664
.103
1.144
Gender (Female) * Uncertainty
Distress
.003
.096
.001
.975
1.003
Gender (Female) * Inflexible
uncertainty beliefs
-.003
.099
.001
.979
.997
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Constant
3.114
.924
11.352***
.001
22.519
Omnibus χ 2 (9) = 25.018, p >.05, R 2= .066 (Cox & Snell), .093 (Negelkerke) *p<.05, **p< .01, ***p<.001 Ϯ—95% C.I. for
EXP(B)
A logistic regression analysis shows that there is a significant influence of uncertainty Distress on the selection of information
2 (9) = 25.018, p < .05). The model explained a 9.3% variance in information selection (Negelkerke R) and was able to
identify 70.1% of cases accurately. The sensitivity of the model was 96.1%, and specificity of the model was 9.9%. The results
show that the overall model was fit, but the individual components were not significant. The close to significant result showed
that every unit decrease in uncertainty distress the odd for decision making from high information load is .885.
The result suggests that for a product like mobile (consumable product), people prefer detailed information; however, people
with tendencies associated with dislikes of uncertainty, ambiguity, and desire for predictability direct them to seek less
information for decision making. This probably relates to ‘process less to process better.’ Also, though there was no significant
gender difference for the desire for predictability, psychological tendencies appear to interact with gender in the decision-
making process differently. There seems a positive relationship between the desire for predictability and seeking low
information, but for men this is opposite; for men higher the desire for predictability, the more they seek more information. The
information-seeking tendency was significantly more in women than men; however, higher the information-seeking tendency
in men the more they seek information for decision making.
So, in conclusion, it can be said that generally, people look for lots of information in buying consumable products, especially
men with a desire for predictability and information-seeking tendency. At the same time, probably for females the more they
desire predictability, the lesser the amount of information they seek.
Study 2: Hotel Experiment
The hotel website named ‘Backpackers’ was created, and participants were asked to use the website for booking the room for a
trip to Delhi. As the researches show that the price of a product is a significant determinant of choice; the manipulation of price
with a mix of the alignable and non-alignable attributes is done. However, to give a range of choices three categories (with
different price range) was created. To see the available hotel, participants use filters related to per page ‘view options’ and
‘price filter.’ Price filter contains three categories of hotels mentioned as 700-1800, 1800-3700 and 3700-4500. In each hotel
category, there is a total of fourteen options; from ‘view per page’ filter, the user can choose the number of hotels presented on
one page.
To observe the possibilities of the combined effect of product align ability and price range on the decision the manipulation of
price, no. of alignable attribute present and no. of non-alignable attribute present is done. The fourteen hotels in each category
are divided into two sets: 1) alignable only, 2) alignable, and non-alignable both. Similarly, value-wise there are three sets:
basic, middle options, and fully loaded. These fourteen options contain three types of attributes: fixed, alignable and non-
alignable. Fixed attributes are common for each category. Alignable attribute, where better version is added to options in
increasing order, and the non-alignable, where a different attribute is added to an option which is not present in other option of
the same category.
In the first category, 8 out of 14 options were alignable only, and five were an alignable and non-alignable mix. In this
category, two alignable attributes were added to all options. In the first eight options, two attributes were added with an
increasingly better version. In the next five options, the alignable attributes were repeated in the same manner, and one
different non-alignable attribute was added with each option. The basic option means the lowest price with the lowest version
of alignable attribute and least valued non-alignable attribute. Fully loaded option means highest price, the best version of an
alignable attribute, and all non-alignable attributes added in other options. Middle option means: increasing higher price, better
version of alignable attribute and more preferred non-alignable attribute. Further manipulation of price and alignability to
create basic, middle and fully loaded options can be understood from the following table:
Table 9: Table explaining characteristic manipulation in experiment 2
Example of the category: 1
OPT
ION
PRI
CE
COM
MON
COM
MON
COM
MON
ALIGN
ABLE
ALIGN
ABLE
NON
ALIG
NON
ALIGN
NON
ALIGN
NON
ALIGN
NON
ALIGN
BASIC -ALIGNABLE /NON ALIGNABLE OPTIONS
MIDDLE OPTIONS
FULLY LOADED ALIGNABLE/NON ALIGNABLE OPTIONS
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NO.
ABLE
ABLE
ABLE
ABLE
ABLE
1
699
V
V
V
W1
X1
2
759
V
V
V
W2
X2
3
859
V
V
V
W3
X3
4
899
V
V
V
W4
X4
5
999
V
V
V
W5
X5
6
899
V
V
V
W6
X6
7
1009
V
V
V
W7
X7
8
1099
V
V
V
W8
X8
9
1399
V
V
V
W9
X9
A
10
1369
V
V
V
W10
X10
B
11
1388
V
V
V
W11
X11
C
12
1376
V
V
V
W12
X12
D
13
1389
V
V
V
W13
X13
E
14
1799
V
V
V
W14
X14
A
B
C
D
E
Table 10: Chi-square table information load and factor wise
Cluster Number of Case
Low information
High information
Chi-Square
Alignable basic
29(100.0%)
0(0.0%)
368.000***
Alignable fully loaded
47(100.0%)
0(0.0%)
Non alignable basic
(100.0%)
9(0.0%)
Non alignable fully loaded
0(0.0%)
87(100.0%)
Alignable compromise
136(0.0%)
0(100.0%)
Non alignable compromise
0(0.0%)
60(100.0%)
*p<.05, **p< .01, ***p<.001
Table 11: Chi-squaredtableGender wise
Gender
Low information
High information
X2
Female
85(51.5%)
80(48.5%)
4.55***
Male
127(62.6%)
76(37.4%)
*p<.05, **p< .01, ***p<.001
Table 12: t table personality factor and hotel information load
Personality factor
Number of Case
N
Mean
Sd
t
p
D2
Need for closure: Order
Low information
212
35.00
6.008
-.459
.646
.048
High information
156
35.29
5.961
Need for closure:
Predictability
Low information
212
25.79
5.618
-.326
.744
.034
High information
156
25.97
4.791
Need for closure:
Decisiveness
Low information
212
19.52
3.955
-1.318
.188
.139
High information
156
20.07
3.904
Need for closure: Ambiguity
Low information
212
31.42
4.980
.142
.887
.014
High information
156
31.35
4.835
Need for closure: Close-
mindedness
Low information
212
20.10
3.430
.990
.323
.103
High information
156
19.72
3.890
Total need for closure
Low information
212
131.844
15.12348
-.354
.723
.037
High information
156
132.410
15.16697
Exploratory tendency:
Low information
212
17.98
3.290
-.504
.614
.054
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Repetitive behaviour
proneness
High information
156
18.15
2.887
Exploratory tendency:
Innovativeness
Low information
212
28.55
4.237
.906
.365
.095
High information
156
28.16
3.898
Exploratory tendency: Risk
taking
Low information
212
26.04
3.375
.874
.383
.093
High information
156
25.73
3.267
Exploratory tendency:
Exploratory through
shopping
Low information
212
22.32
3.543
-1.031
.303
.109
High information
156
22.69
3.234
Exploratory tendency:
Interpersonal
communication
Low information
212
9.50
1.682
-.483
.629
.046
High information
156
9.58
1.789
Exploratory tendency:
Brand switching
Low information
212
22.00
3.332
-.780
.436
.082
High information
156
22.28
3.422
Exploratory tendency:
Information seeking
Low information
212
37.49
4.563
-.530
.596
.055
High information
156
37.74
4.398
Total Exploratory tendency
Low information
212
163.877
17.18437
-.264
.792
.026
High information
156
164.333
15.14035
Intolerance uncertainty
scale: Desire for
Predictability
Low information
212
21.96
5.109
-.976
.330
.104
High information
156
22.47
4.627
Intolerance uncertainty
scale: Uncertainty Paralysis
Low information
212
17.36
4.807
-.893
.372
.095
High information
156
17.81
4.595
Intolerance uncertainty
scale: Uncertainty Distress
Low information
212
14.50
4.084
-.264
.792
.026
High information
156
14.61
4.095
Intolerance uncertainty
scale: Inflexible Uncertainty
Beliefs
Low information
212
11.42
3.252
-.672
.502
.076
High information
156
11.65
3.212
Intolerance uncertainty
scale: Total
Low information
212
65.2453
15.18936
-.837
.403
.088
High information
156
66.5385
13.88821
Impulsivity: non-planning
impulsiveness
Low information
212
19.53
4.342
2.312
.021
.244
High information
156
18.47
4.337
Impulsivity: Cognitive
Impulsiveness
Low information
212
12.39
2.771
.488
.626
.053
High information
156
12.25
2.501
Impulsivity: Motor
Impulsiveness
Low information
212
19.43
4.245
-.004
.997
.002
High information
156
19.44
4.090
Impulsivity: total
impulsiveness
Low information
212
21.98
5.042
1.979
.049
.208
High information
156
20.90
5.316
*p<.05, **p< .01, ***p<.001
Statistically, all alignable options formed a low information category, and non-alignable options formed a high information
category. The chi-square results show that there is not much difference for females in decision making from low or high
information choices, but males were making significantly more decision from low information choices. Similarly, the
psychological tendencies were also not creating much of the difference in decision making; only individuals high on non-
planning impulsiveness (t=2.31, p<.021) and total impulsivity (t=1.979, p<.049) were using low information for decision
making.
Table 13: Logistic table
Variable
b [95%c.i. B]
S.E.(b)
Wald
Sig.
Exp(b)
Desire for predictability
.002
.046
.001
.970
1.002
Uncertainty paralysis
.061
.050
1.516
.218
1.063
Uncertainty distress
-.103
.057
3.258
.071
.902
Inflexible uncertainty beliefs
-.002
.060
.001
.974
.998
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Gender (Female)
-1.303
1.037
1.578
.209
.272
Gender (Female)*Desire for predictability
.045
.068
.447
.504
1.046
Gender (Female)* uncertainty paralysis
-.112
.076
2.165
.141
.894
Gender (Female)* uncertainty distress
.173
.088
3.901
.048
1.189
Gender (Female)* inflexible uncertainty beliefs
.017
.091
.035
.853
1.017
Constant
-.101
.723
.020
.889
.904
Omnibus χ 2 (9) = 13.824, p>.05, R 2= .037(Cox & Snell), .050 (Negelkerke) *p<.05, **p< .01, ***p<.001 Ϯ—95% C.I. for
EXP(B)
A logistic regression analysis shows that there is no significant influence of exploratory tendency. However, intolerance for
uncertainty and impulsivity were significant predictors. A logistic regression analysis shows that there is a significant influence
of intolerance of uncertainty on the selection of information 2 (9) = 13.824, p >.05). The model explained 5% variance in
information selection (Negelkerke R) and was able to identify 62.8% of cases accurately. The sensitivity of the model was
34%, and the specificity of the model was 84%. The result showed that with every unit increase in uncertainty distress for
males (in comparison to females) the odd for a decision from high information load is 1.189.
Table 14: Logistic table
Variable
b [95%c.i. B]
S.E.(b)
Wald
Sig.
Exp(b)
Non planning impulsiveness
-.065
.085
.595
.441
.937
Cognitive impulsiveness
-.100
.068
2.199
.138
.905
Motor impulsiveness
.069
.042
2.663
.103
1.071
Total impulsiveness
.034
.072
.222
.638
1.035
Gender (Female)
.916
1.303
.494
.482
2.501
Gender (Female)*non-planning impulsiveness
-.068
.124
.298
.585
.934
Gender (Female)*cognitive impulsiveness
.222
.103
4.624
.032
1.249
Gender (Female)*motor impulsiveness
-.074
.061
1.451
.228
.929
Gender (Female)* total impulsiveness
-.019
.105
.033
.855
.981
Constant
-.148
.874
.029
.866
.863
Omnibus χ 2 (9) = 18.801, p<.05, R 2= .050(Cox & Snell), .067 (Negelkerke) *p<.05, **p< .01, ***p<.001 Ϯ—95% C.I. for
EXP(B)
A logistic regression analysis shows that there is a significant influence of impulsivity on the selection of information (χ 2 (9) =
18.801, p <.05). The model explained 6.7% variance in information selection (Negelkerke R) and was able to identify 60.9%
cases accurately. The sensitivity of the model was 32.1%, and the specificity of the model was 82.1%. The result showed that
for every unit increase in cognitive impulsivity in males (in comparison to females) the odd for making a decision from high
information load is 1.249.
It is clear from results that when it comes to decision making for service products (hotel booing) males prefer to process less
information, and the same applies to people with high impulsivity. However, males with tendencies to avoid uncertainty and
high on cognitive impulsiveness look for more information for making the decision.
GENERAL DISCUSSION
The present study was conducted to understand the gendered information processing and influence of psychological tendencies
in online decision making. The results indicate that information processing related psychological tendencies do have a
significant influence over decision making and it also interacts with gender. The interaction appears to be complicated and
differs from the content of the decision to be made. It appears that the decision for consumable products and service product
are processed differently and get influenced by different factors. This reflects that understanding any behavior requires taking
an individual in its totality as much as possible; the fragmented approach does not give an accurate picture of reality. The
broader view as reflected by the findings of both the studies relates to the different strategies used by males and females in the
same situation in addition to the interactive effect of their psychological tendencies. For example, information seeking is more
in women, but this tendency influences decision-making process in men only.
Similarly, the desire for predictability influences information processing differently in men and women. Where for men, the
higher this tendency, the more they try to satisfy it by looking for more information, but for women, the higher this tendency
the less information they seek. So probably women satisfy this need by narrowing the options field whereas, men satisfy it by
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expanding the options field. The service product where uncertainties are higher, it is understandable that people prefer limited
information, ‘process less-process better’ strategy. However, at the same time, males appear to avoid uncertainty by expanding
the options field whereas women do not follow this strategy. This strategy seems to get even more strengthen if impulsivity is
higher in male decision-makers.
Similarly, more information was sought for consumables and less for service products, but men with certain psychological
tendencies sought more information for all kinds of products. Probably it can be said that women work with ‘process less to
process better’ strategy, whereas males with certain psychological tendencies work with ‘process more to get better’ strategy.
This strategic difference should influence the gender difference in the amount of information leading to information overload.
This assertion was supported in our previous publication. The initial analysis of study one experiment (as reported in Maidullah
& Sharma, 2019) clearly showed that majority of females were making their decision from 8x8 (8 options with eight attributes)
category whereas, males were making their decision from 12x12, 12x8, and 8x12 category respectively and the difference was
significant.
In addition to providing insight into gender and psychological tendency's role in online decision making, present work also
provides insight into the debate of information measurement (Huang, 2000). It is clear that beyond the understanding by Miller
(1956), Bettman (1979) or Lee and Lee (2004) information with more than four choices or attributes forms the high
information load, which is taxing on the mental operation. Similarly, the non-alignable attributes are more taxing and create a
high information load.
CONCLUSION
Findings from present work fill the gap in information processing limit debate. Information measurement should include not
the only number of options provided but also the amount of information provided in each option. Similarly, the findings
provided insight into the probable differential strategy to information processing and added an answer to the information
measurement debate. It is clear that even with similar psychological tendencies men and women use different strategies,
‘process less to process better’ and process more to get better’ in online decision making.
Limitation and Study forward: Present study findings are limited due to the product category involved. As the findings
clearly show that people process service products and consumables in different way future work should include more products
in each category and then compare the trend of results. Similarly, further work can look for ways to understand individuality in
online decision making more holistically and in a more realistic scenario.
The practical implication of the Study: Understanding decision making features of Indian consumers can not only contribute
to the understanding of the naturalistic decision-making process itself but also can provide inputs to the market researchers,
designers, and policymakers. Identifying typical strategies adopted by both the genders may help in marketing strategies at
different platforms. The study also adds to methodological rigor by using the computerised task in combination with
questionnaire thus future studies in Psychological Science and Management studies should take this into consideration.
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