role of haptic touch in shopping

RESEARCH PAPER

Role of haptic touch in shopping

Some methodological contributions

S. Abhishek • Piyush Kumar Sinha •

Neharika Vohra

Published online: 8 January 2014

� Indian Institute of Management Calcutta 2013

Abstract Research on multisensory nature of con-

sumption has highlighted importance of smell, taste,

and touch during product evaluation and subsequent

purchase decisions. However, there are very few

studies in marketing which have examined role of

touch in shopping. This paper builds the argument for

conducting research on role of touch during shopping

in Indian context and provides some methodological

contribution for conducting such a research. The paper

provides schema to differentiate products into cate-

gories of high, moderate, and low haptic salience

based on consumer preferences and sensibilities.

Furthermore, it revalidates the NFT scale in Indian

context which can be used to differentiate consumers

into high and low motivation to touch.

Keywords Haptic touch � Shopping � Retail �NFT scale � Indian consumers

Introduction

Marketers have typically viewed consumers as verbal/

visual information processors (Citrin et al. 2003) and

thus utilized verbal and visual mean of communication

to present information to the consumers. However, in

everyday life, a number of senses are generally

involved in any act. Lindstrom (2005) said that all

five senses are important in any form of communica-

tion and life experiences. Research on multisensory

nature of consumption has also highlighted the non-

verbal and non-visual aspects during product evalua-

tion and subsequent purchase decisions (Hirschman

and Holbrook 1982; Krishna 2012; MacInnis and Price

1987). Research suggest that the opportunity to smell

(Davies et al. 2003; Mitchell et al. 1995; Spangenberg

et al. 1996; Ward et al. 2003), taste (Boutaud 1999;

Hoegg and Alba 2007), and touch (Argo et al. 2006,

2008; Morales and Fitzsimons 2007; Muller 2013;

Peck and Childers 2003a) can orient a person to make

a purchase. Some recent studies (Forster 2011; Hulten

2012; Krishna et al. 2010; Spence and Gallace 2011)

have also examined the role of multiple sensory cues

in influencing the consumers.

However, there are very few studies in the field of

marketing which have examined the role of touch in

shopping. To the best of our knowledge, there has not

been any study conducted in India on role of touch

during shopping in Indian context. This paper builds

the argument for conducting research on role of touch

during shopping in Indian context and makes some

methodological suggestions for conducting such

research.

Evolving store formats and shopping behavior is

leading to changes in the way stores function (Sinha

S. Abhishek (&) � P. K. Sinha

Marketing Area, Indian Institute of Management

Ahmedabad, Ahmedabad 380015, Gujarat, India

e-mail: [email protected]

N. Vohra

Organizational Behavior Area, Indian Institute of

Management Ahmedabad, Ahmedabad, India

123

Decision (December 2013) 40(3):153–163

DOI 10.1007/s40622-013-0017-x

and Uniyal 2005). Traditional format stores have a

clear division of space wherein a physical counter in

the form of wooden furniture has been used to divide

the store space between consumers and shop staff. The

merchandise is stocked such that there is direct control

of the shop staff. The consumer asks for the products

and is often provided the same. The shop staff serves

as the shopper’s intermediary to the world of things

(Underhill 1999). This arrangement prohibits and

discourages the consumer to actually touch and assess

products. However, with a number of new format

retail stores opening up in India, consumers can

directly pick up the merchandise from the display put

up by stores. Competition from modern format retail

stores has forced a number of traditional retailers to

change the display arrangements at their stores,

allowing consumers direct access of products. Con-

sumers can now increasingly touch to discover the

merchandise on their own. Thus, it is important to

understand the process by which touch influences the

consumer decision.

With the waning power of product brand name

and increased variety seeking behavior (Underhill

1999), customers need to feel a certain level of

confidence in a product and its value, which comes

only from hard evidence, and not from television

commercials or word of mouth. Consumers believe

in a product when they see/smell/touch/hear/taste/try

it (Underhill 1999). This becomes particularly

important for private label brands for which there

in no advertising and where consumers make

purchase decisions which are mainly based on feel

and touch of products at display in store. With the

growth of private label brands in India (Abhishek

2011), marketers need to understand the underlying

process of touch so that appropriate marketing

actions can be planned.

Research background

Stevens and Green (cf. Citrin et al. 2003) have defined

touch as ‘‘sensation aroused through stimulation of

receptors in the skin.’’ Although studies of touch

involve different parts of the human body, primary

interest is in studies using hands as principal source of

input to touch. The term haptic is used to describe the

information gained by an active movement of hand or

arm (Gibson 1962). The term was coined by Max

Dessoir, who alluding to the term optic and acoustic

suggested that teaching of sense of touch involving

tactile and muscular sensations be called haptic (Jutte

2009).

The haptic system is capable of encoding a number

of object dimensions and properties: surface texture,

internal substance, and thermal attributes, collectively

called material attributes, as well as structural attri-

butes of contour and size (Klatzky et al. 1987). Haptic

touch has been found to be more important when

encoding information about an object’s material

(Klatzky et al. 1993). Studies in marketing have

involved haptic touch as the stimulus.

Hornik (1992) pioneered the introduction of role

of touch in marketing with a study that demon-

strated the positive role of casual interpersonal

touch on consumer behavior. Since then the role of

touch studies in marketing can be broadly catego-

rized into two streams. (a) Studies examining

consumer behavior when a salesperson makes touch

contact with consumer and (b) Studies examining

consumer behavior when a consumer touches the

product.

Studies (Gueguen and Jacob 2006; Hornik 1992;

Smith et al. 1982) conducted in the first stream of

research have examined the role of salesperson’s

touch on shopping time, store evaluations, evaluation

of salesperson during consumption activity, and

compliance to marketing request. The findings indi-

cate that in case a salesperson touches the consumers,

it results in increased shopping time, higher evalua-

tions of store and salesperson during consumption

activity, and greater compliance to marketing request.

In a related study, Martin (2012) showed that

accidental interpersonal touch from a stranger on

shopper produces a negative effect on consumer

evaluations and shopping times.

The other and relatively more researched stream in

the field of touch has examined consumer’s touch of

products where haptic touch was employed. Peck and

Childers (2003a) proposed the haptic information

framework which examined the product, individual

and situational factors that affected use of touch

information during product evaluation. The situational

factors in haptic information framework were exam-

ined in case of touch and no-touch conditions.

In this paper, we attempt to classify the products

based on haptic salience and also segment the

customers on their need to touch.

154 Decision (December 2013) 40(3):153–163

123

Research methodology

In order to conduct studies examining role of touch

during shopping in India, there is a need to develop

better understanding about haptic salience of products

and consumer’s motivation to touch in India. The

study developed a methodology to classify the

products into high, moderate, and low haptic salience.

This methodology, consisting of three steps, employed

a combination of observation methods and self-report

measures. In the first step, observation method was

used to prepare a comprehensive list of products which

were relevant for the study. In the second step, short-

listing of products was undertaken by observing

consumers in retail stores. In the third and final step,

questionnaire was administered to respondents and

collected data were used to classify products into high,

moderate, and low haptic salience.

Similarly, while consumer’s motivation to touch

can be measured by NFT scale, there is a need to pre-

validate the scale in Indian conditions. The scale

testing process consisted of qualitative and quantita-

tive methods. Qualitative interviews were conducted

for face validity tests to ascertain the suitability of

items in the scale. After some changes in the wording

of items in the scale, data were collected through a

questionnaire. The collected data were quantitatively

analyzed by calculating Cronbach’s Alpha and con-

ducting factor analysis.

Differentiating products into three categories

of haptic salience

In product-related factors of haptic information frame-

work, it has been pointed that texture, hardness,

temperature, and weight information provided instru-

mental and autotelic material properties. While autote-

lic forms of information are related to the sensory

experience and hedonic appreciation of the product,

instrumental properties are related more to its structural

properties and less to the sensory enjoyment of the

product (Peck and Childers 2003a). These material

properties can be used to differentiate products as high,

moderate, and low on haptic salience. Dividing pro-

ducts into three categories provide a tool to marketers to

focus on role of touch for relevant product categories.

Multi-stage process was adopted for categorizing

products based on haptic salience. As the first step, a

comprehensive list of all the products for which haptic

touch is allowed in retail stores was made based on

observations made while visiting the store as a

shopper. All the items which were available without

any primary packaging were listed. The stores visited

for the exercise included new format supermarkets and

hypermarkets. The list was supplemented by items for

which customers could ask the external packaging to

be removed by the salespersons, such as glass bowl

sets in corrugated sheet boxes, socks, and dress

materials.

The details were noted down on the same day

following the guidelines by Wells and Sciuto (1966)

where they have highlighted the importance to transfer

the day’s observations from notes to permanent record

cards before the notes got cold. After preparing the list,

in some cases, the researcher went again to retail store

to reconfirm if all the relevant items were included in

the list. This led to generation of the first list of 125

products for which touch was allowed in stores

(Table 3 in Annexure).

In the second step, the retail outlets were re-visited

and customers were observed during shopping process

for eliminating the items where consumers were not

seen devoting efforts in touching the products, even if

haptic touch opportunity was present. It was decided to

observe customers as observation, as a primary

research method, has been found to be particularly

useful when researcher is seeking to establish how

people actually behave (Baker 2002). Direct observa-

tion also produces a highly detailed, nearly complete

record of what people actually do, as distinguished

from what people say. It can yield the correct answer

when faulty memory, desire to impress the inter-

viewer, or simple inattention to details would cause an

interview answer to be wrong (Wells and Sciuto

1966). Often, how people behave spontaneously in the

act of shopping or consuming products is different

from the descriptions they give in interviews. They are

much more responsive to social and environmental

stimuli when they are consciously aware and one way

in which researchers can overcome all these influences

is by observing the behaviors as they occur in the field

(Rust 1993a, b).

It was decided to follow non-intrusive observation

of consumers as we did not want the consumers to be

conscious and thus change their normal shopping

behavior. Rust (1993a) said that there are times when

non-intrusive observational methods provide a more

Decision (December 2013) 40(3):153–163 155

123

complete and accurate picture of the purchasing

experience and can be used. Since the observation

was not about something that was private or personal,

there were no ethical issues in such observations.

Following the guidelines of Baker (2002) who said

that in the case of exploratory research when one is

seeking to get a feel for a situation it is often best to

follow an unstructured approach, we did not structure

the observations. The researcher stood in the aisles

where unpackaged products are displayed and

observed what people did with the products. The

researcher observed behavior of 8–10 shoppers for

listed products in the aisle and then moved to another

aisle. In order to ensure consistency in observations of

consumer behavior, all stores were visited between

6.00 p.m. and 9.00 p.m. Moreover, shopping behavior

of only those products which were available through-

out the year were observed by researcher. This led to

generation of the second list of 46 products (Table 4 in

Annexure). This list of products was used to classify

the products based on their haptic salience.

In the third step, a questionnaire was developed for

capturing consumer responses about haptic salience of

short-listed products. For each product, respondents

were asked to indicate whether they thought that the

products were high, moderate, or low on haptic salience.

The questionnaire was administered to group of respon-

dents consisting equal number of men and women.

The questionnaire was divided into two parts. The

first part required the respondents to identify the

attributes (from the four attributes—texture, hardness,

weight, and temperature) which they considered as

relevant for a particular product. The important

attributes were ranked by the respondents as per their

importance in the evaluation process. Attributes

considered not important were indicated as NA. In

the second part of the questionnaire, taking into

consideration the attribute which was ranked first

(highest) in part one of the study, the respondents

classified the products into one of the three catego-

ries—high, moderate, and low on haptic salience.

In all the cases, the classification was done in the

presence of researcher. Each of the respondents was

individually approached and explained the instruc-

tions for first part of the questionnaire. Once the

respondents finished the first part, they were told about

the second set of questionnaire and it was given to

them. It took on an average 20 min to complete the

questionnaire.

Six products emerged as the highest ranked

products on high haptic salience. The set of respon-

dents classified mobile phones, apple, bedsheet, sofa,

clothes, and soft toys as high on haptic salience.

Digital cameras, carpets, bhindi (okra), tomatoes,

oranges, and cushions were given the second rank. The

product list with high haptic salience suggests that it

included electronic items—mobile phones and digital

cameras; fruits—apples and oranges; vegetables—

bhindi (okra) and tomatoes; and upholstery items—

bedsheet, sofa, carpets, and cushions along with

clothes and soft toys.

The next ranked products were steel plates along

with vegetables like brinjal, lemon, cabbage, and

parwal (pointed gourd) and accessories like laptop

bags and shoes. These products were followed by

slippers/sandals, cloth bags, plastic containers, steel

glasses, calculators, and vegetables like cucumber and

capsicum. All these products can be classified as

moderate on haptic salience.

The remaining products of Table 4 in Annexure

were classified as low on haptic salience. These

included categories like electric products—sandwich

maker, press iron, and landline handsets; utensils—

sauce pan, kadhai (circular deep cooking pot), tawa

(flat concave disk-shaped griddle), cups, and glasses;

household utility items—bucket and serving tray;

grocery items—rice, pulses, peanuts, tea, and dried

coconuts; stationery items—pencils and file folders;

and accessories—belts and backpack bags.

Measuring motivation to touch

While examining the consumer-related factors in

haptic information framework, Peck and Childers

(2003a) differentiated the customers based on their

motivation for touch which was measured through

‘‘Need For Touch’’ (NFT) scale. A similar exercise

was undertaken by Citrin et al. (2003) who developed

a scale to measure need for tactile input (NTI) in

product/brand evaluations. NFT scale has two dimen-

sions, autotelic and instrumental, containing 12 items

(Peck and Childers 2003b) (Table 1). The NTI scale is

a 6-item instrument with all items loading on single

factor. Consumers who scored high on NFT scale were

high on motivation to touch and for such individuals

barriers to touch inhibited the use of haptic informa-

tion and consequently decreased confidence in product


123

evaluations. On the other hand, consumers having low

NFT score, thus indicating low motivation to touch,

may forgo product touch before making the purchase.

Research has shown that people high or low on

motivation to touch get affected differentially in

different contexts (Peck and Johnson 2011; Vieira

2012).

The NFT scale (Peck and Childers 2003b) which

measures individual differences in preference for

haptic (touch) information has been developed and

tested in the United States. The NFT scale has so far

not been used in Indian research context. It was

decided to validate the scale in Indian conditions

before using it to classify consumers having high and

low motivation to touch.

For the face validity test, discussions were con-

ducted with six consumers to ascertain their under-

standing of the items mentioned in the scale. Based on

the discussions about the items in the scale, small

changes were made in the wording of items to make it

more suited to the English spoken and understood in

India. In India, most of the stores have over-the-

counter experience where customers do not get the

opportunity to touch the products. In such a case, some

of the consumers were not able to relate with phrase

‘‘walking through.’’ However, some of the respon-

dents identified stores as modern format retail stores

and were able to identify with the phrase ‘‘walking

through.’’ It was decided to change it (‘‘walking

through’’) by phrase ‘‘I am in’’ to take care of

differences in interpretations. In items 1, 5, and 12,

the word ‘‘kind’’ was replaced with ‘‘types’’ as the

respondents suggested that ‘‘type’’ was a more com-

monly used word in comparison to ‘‘kind.’’ In items 5

and 11, a decision was taken to replace word ‘‘handle’’

with ‘‘hold’’ as the word ‘‘handle’’ come across as

having different meanings for respondents. For some

respondents, ‘‘handle’’ meant that they were able to

hold the product whereas for some, ‘‘handle’’ meant

operating the product, especially in case of electronic

products. Table 2 lists the items which were used for

the study.

Data were collected through a questionnaire from

67 respondents (34 male respondents and 33 female

respondents) who were students of MBA program in

two institutes. The respondents were given a small

incentive as a token of appreciation for their involve-

ment after they completed the questionnaire.

Table 1 Items of need for touch scale

1. When walking through stores, I cannot help touching all

kinds of products (A)

2. Touching products can be fun (A)

3. I place more trust in products that can be touched before

purchase (I)

4. I feel more comfortable purchasing a product after

physically examining it (I)

5. When browsing in stores, it is important for me to handle

all kinds of products (A)

6. If I cannot touch a product in stores, I am reluctant to

purchase the product (I)

7. I like to touch products even if I have no intention of

buying them (A)

8. I feel more confident making a purchase after touching a

product (I)

9. When browsing in stores, I like to touch lot of products

(A)

10. The only way to make sure a product is worth buying is

to actually touch it (I)

11. There are many products that I would only buy if I could

handle them before purchase (I)

12. I find myself touching all kinds of products in stores (A)

A Autotelic scale item, I instrumental scale item

Table 2 Items of need for touch scale after face validity test

1. When I am in stores, I cannot help touching all types of

products (A)

2. Touching products can be fun (A)

3. I place more trust in products that can be touched before

purchase (I)

4. I feel more comfortable purchasing a product after

physically examining it (I)

5. When browsing in stores, it is important for me to hold

all types of products (A)

6. If I cannot touch a product in stores, I am reluctant to

purchase the product (I)

7. I like to touch products even if I have no intention of

buying them (A)

8. I feel more confident making a purchase after touching a

product (I)

9. When browsing in stores, I like to touch lot of products

(A)

10. The only way to make sure a product is worth buying is

to actually touch it (I)

11. There are many products that I would only buy if I could

hold them before purchase (I)

12. I find myself touching all types of products in stores (A)

A Autotelic scale item, I instrumental scale item


123

The Cronbach’s Alpha for the scale was 0.782, well

above the accepted level of 0.70 (Hair et al. 2003).

When reliability analysis was conducted for only

instrumental items, Cronbach’s Alpha was slightly

better, coming to be 0.838. However, the Cronbach’s

Alpha for only autotelic items was 0.676, just below

the accepted level of 0.70. The item-total statistics

showed that Cronbach’s Alpha did not drop signifi-

cantly even after dropping any of the items from

12-item scale (Table 5 in Annexure).

Factor analysis was conducted to check whether the

twelve items loaded on the two factors as per the two

dimensions suggested in literature. The results showed

that the twelve items loaded on three factors. The

factor analysis for instrumental items showed that all

the items loaded on single dimension (Tables 6, 7 in

Annexure). However, the factor analysis of autotelic

items showed that the six items loaded on two

dimensions (Tables 8, 9 in Annexure). Peck and

Childers (2003b) have suggested that based on the

underlying theory, researchers could employ either the

composite scale or one of the two subscales pertaining

to instrumental and autotelic dimensions. Previously,

researchers have used only one dimension of scale as

well (Krishna and Morrin 2008; Peck and Wiggins

2006).

The mean NFT score for female respondents was

above the mean NFT score for male respondents. This

confirms the findings of Peck and Childers (2003a, b)

where mean NFT score of female respondents was

higher than mean NFT score of male respondents.

However, the t test indicated that the difference was

not significant between male respondents (M = 5.91,

s = 9.918) and female respondents (M = 6.15,

s = 8.078), t (65) = -0.108, p = 0.914, a = 0.05.

Discussion

According to cue utilization theory, product consists

of an array of cues that serve as surrogate indicators of

quality to shoppers (Cox 1967). In many situations,

consumers do not know the true quality of competing

products (or brands) before making their purchase

decisions. In such cases, research suggests that

consumers are likely to rely on simple heuristics, or

cues, to assess product quality. Zeithaml (1988)

divided these cues into intrinsic and extrinsic cues.

Intrinsic cues are product-related attributes which

when changed will result in changes in composition of

product itself such as ingredients, flavor, color, and

texture (Blair and Innis 1996). Extrinsic cues are

product attributes which are not part of the physical

product and they can be changed without affecting the

composition of the product itself, e.g., price and brand

name (Blair and Innis 1996). While respondents may

get information about the product quality through

extrinsic cues, wherever they cannot receive such

information they rely on haptic touch to access this

information. Probably this is the reason for product

categories like fruits and vegetables to figure prom-

inently in category of high and moderate haptic

salience product categories.

The earlier study by Peck and Childers (2003a) also

included products which were available in packages

(such as cereals and toothpaste) for classifying

products into high, moderate, and low on haptic

salience. This limitation has been overcome by

including only those products where direct touch

was allowed and excluding products which were

always sold in packages.

This research has classified products into different

haptic saliences with a limited number of respondents.

As an extension, further research needs to be conducted

over a larger dataset representing a wider spectrum of

shoppers. While this will help in further building the

generalizability of schema, it also needs to be stated

that the study already mirrors the real-life situations as

it is grounded in the observations of shoppers.

The motivation to touch, which discriminates

individual differences in preference for haptic infor-

mation, was measured by NFT scale. The quantitative

tests showed that while the instrumental items of scale

loaded on one dimension, the autotelic items did not

load on a single dimension. This calls for further

investigation to look into the causes of items loading

on two dimensions. Probably item A4, which is not

loading with remaining five items, is not being

construed as relevant with hedonic aspect of shopping

by the respondents. Another reason could be with

respect to the nature of respondents. In this study,

respondents were young consumers in the age group of

20–30 who were students in post graduate program of

management. It is possible that young consumers

behave in a slightly different manner than the general

population with respect to haptic touch in stores.

Researchers working in this field can take this as an

area of future research.


123

Annexure

See Tables 3, 4, 5, 6, 7, 8, and 9.

Table 3 List of items where touch was allowed in retail stores

Category

Electrical and electronic products

Television

Refrigerator

Washing machines

VCD/DVD Player

Air conditioner

Air coolers

Geyser

Toaster

Sandwich maker

Press iron

OTG machines

Mixie

Microwave machine

Ceiling fans

Mobile phones

Digital cameras

Landline handsets

Laptops

Hair dryer

Calculator

Household grocery items

Rice

Wheat

Pulses

Gram

Peanuts

Rajma (kidney bean)

Lobia (black-eyed bean)

Mustard

Jeera (Cumin)

Tea

Sugar

Dried coconut

Glass items

Cups

Glasses

Table 3 continued

Soup bowls

Sweetdish bowl sets

Plates

Decorative Items

Utensils

Plates

Bowls

Spoons

Glasses

Sauce pan

Serving tray—steel

Serving tray—plastic

Casserole

Knives

Seiver

Spoon stand

Glass stand

Cook and serve

Kadhai

Tawa

Chakla-belna

Tadka Pan

Clothes and accessories

T-shirts

Shirts

Trousers

Skirt

Jeans

Shorts

Towels

Track suits

Track pants

Ladies salwar suit

Ladies kurti

Mens kurta

Sarees

Slippers/sandals

Shoes

Belt

Stationery

Books

Notebooks


123

Table 3 continued

Pencils

Pens

File folders

Fruits and vegetables

Brinjal

Broccoli

Pea

Capsicum

Chili

Parwal

Tinda

Bhindi (ladies finger)

French bean

Lemon

Tomato

Potato

Onions

Garlic

Ginger

Cauliflower

Cabbage

Carrot

Corn/maize

Bitter gourd

Bottle gourd

Cucumber

Apple

Banana

Oranges

Pear

Papaya

Others

Gold jewelery

Artificial flowers

Soft toys

Laptop bags

Backpack bags

Strolley

Plastic chairs

Sofa

Dining table and chairs

Bed

Cushion

Camera pouches

Mobile pouches

Table 4 List of items where touch was observed in retail

stores

Sandwich maker

Press iron

Mobile phones

Digital cameras

Landline handsets

Calculator

Plates

Glasses

Sauce pan

Serving tray—steel

Serving tray—plastic

Kadhai

Tawa

Cups

Glasses

Plastic container

Buckets

Bed sheets

Cloth bags

Carpets

Rice

Pulses

Peanuts

Tea

Dried coconut

Brinjal

Capsicum

Parwal

Bhindi (ladies finger)

Lemon

Tomato

Cabbage

Cucumber

Apple

Oranges

Pencils

File folders

Soft toys

Laptop bags

Backpack bags

Sofa

Cushion

Slippers/Sandals

Shoes

Belts

Dress Materialsa

a Will include all items (other than slippers/sandals, shoes, and belts) in

Clothes and accessories category


123

Table 5 Item-total statistics for all 12 items of NFT scale

Scale mean if

item deleted

Scale variance if

item deleted

Corrected item-total

correlation

Squared multiple

correlation

Cronbach’s alpha

if item deleted

I1 4.60 67.426 0.496 0.601 0.746

I2 4.03 71.332 0.426 0.437 0.755

I3 5.75 65.404 0.546 0.456 0.740

I4 4.54 68.252 0.587 0.643 0.740

I5 5.48 69.647 0.411 0.459 0.756

I6 4.93 69.737 0.460 0.551 0.751

A1 6.21 69.107 0.337 0.404 0.765

A2 6.12 67.501 0.428 0.449 0.754

A3 6.01 70.530 0.369 0.372 0.760

A4 6.07 76.131 0.077 0.259 0.795

A5 5.91 67.265 0.477 0.446 0.748

A6 6.69 69.794 0.417 0.461 0.755

Table 6 Total variance explained: instrumental items of NFT scale

Component Initial eigenvalues Extraction sums of squared loadings

Total % of variance Cumulative % Total % of Variance Cumulative %

1 3.502 58.367 58.367 3.502 58.367 58.367

2 0.720 11.994 70.361

3 0.658 10.973 81.334

4 0.541 9.013 90.347

5 0.310 5.162 95.508

6 0.269 4.492 100.000

Extraction method: principal component analysis

Table 7 Component matrix:

instrumental items of NFT

scale

1 Components extracted.

Extraction method: principal

component analysis

Component

1

I1 0.779

I2 0.754

I3 0.653

I4 0.865

I5 0.724

I6 0.791


123

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Component Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings

Total % of

variance

Cumulative

%

Total % of

variance

Cumulative

%

Total % of

variance

Cumulative

%

1 2.595 43.254 43.254 2.595 43.254 43.254 2.563 42.724 42.724

2 1.147 19.117 62.371 1.147 19.117 62.371 1.179 19.647 62.371

3 0.807 13.455 75.826

4 0.555 9.248 85.074

5 0.498 8.307 93.381

6 0.397 6.619 100.000

Extraction method: principal component analysis

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Component

1 2

A1 0.715 -0.265

A2 0.765 0.062

A3 0.438 -0.594

A4 0.184 0.830

A5 0.785 0.200

A6 0.791 -0.151

Rotation converged in 3 iterations. Extraction method:

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