Combining the Statistical and Interpretative Analyses for Testing E-Commerce Customer Loyalty Questionnaire

Combining the Statistical and Interpretative Analyses for Testing E-Commerce Customer Loyalty Questionnaire

Conference Paper · August 2018

DOI: 10.1109/CITSM.2018.8674342

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Combining the Statistical and Interpretative

Analyses for Testing E-Commerce Customer Loyalty Questionnaire

A’ang Subiyakto

¹

, Muhammad Rasyid Juliansyah

²

, Meinarini Catur Utami

³

, Aries Susanto

⁴

Departement of Information Systems, Syarif Hidayatullah State Islamic University Jakarta

Jl. Juanda, No. 95, Tangerang Selatan, Banten, 15412, Indonesia

Email: aang_subiyakto@uinjkt.ac.id

¹

, rasyid.juliansyah12@mhs.uinjkt.ac.id

²

, meinarini@uinjkt.ac.id

³

, ariessht@uinjkt.ac.id

⁴

Abstrak—Despite the interpretative data analysis may have often used practically to confirm the statistical analysis results in the questionnaire testing, but the confirmation is also often not expressed clearly by researchers. It seems like the statistical analysis is only the technique for testing a questionnaire. In addition, although the interpretative confirmation is common for the experienced researchers, it may be a distinctive constraint for beginners. This study elucidated how to combine the statistical and interpretative analyses for testing e-commerce customer loyalty (ECL) questionnaire. The aims were to assess statistically the questionnaire and to interpret the statistical analysis results.

Besides the findings may be helpful practically for the other scholars in terms of the questionnaire testing works, the confirmation results can be a consideration point for revising the questionnaire.

Keywords—statistical analysis; interpretative analysis;

questionnaire testing; e-commerce customer loyalty questionnaire.

I. I

NTRODUCTION

It may a common procedure that, besides using the statistical analysis, a questionnaire revision is also performed by employing the interpretation analysis of the researchers.

Despite the interpretative data analysis may have used practically for testing questionnaire in many survey studies [1- 6], but the combination of both data analysis techniques is also often not revealed clearly in the literature. It seems like the statistical analysis is only the technique for testing a questionnaire [7, 8]. In addition, although the interpretative confirmation is common for the experienced researchers, it may be a distinctive constraint for beginners. Thus, the presentation of the combination may still indispensable.

This study elucidated how to combine the statistical and interpretative analyses for testing ECL questionnaire. The aims were to assess statistically the questionnaire and to interpret the statistical analysis results. Besides the findings may be helpful practically for the other scholars in terms of the questionnaire testing works, the confirmation results can be a consideration point for revising the questionnaire. The two research questions were used in the study to guide the research implementation. There are:

Q1. Does the ECL questionnaire have statistically a good property?

Q2. Does the ECL questionnaire elucidate the responsiveness and cognition of the sampled people?

This article is elucidated sequentially in five points. In the second one, besides describing a short literature review, the point also explains the model, indicators, and questions of the main study as the input of the questionnaire testing study. The research method descriptions are then demonstrated in the third point of the article. The article presents the results and discussion parts on the fourth point. Lastly, the researchers close the paper with the conclusion descriptions in the last point.

II. L

^ITERATURE

R

^EVIEW

The Association of Indonesian Internet Service Providers (APJII) in the year 2017 reported that around 143.26 (±54.68%) of the 262 million people in the country are the internet users. This kind of transaction is gaining popularity from day to day around the world, as well as in Indonesia [9].

Nowadays, information technology (IT) has been evolved over the past five decades and it will change the human life sectors [10]. For example, e-commerce is one of many online activities that shows the greatest growth of the internet use.

This trading style has spread rapidly because of the many benefits to humankind [11]. On-line shopping or retailing is a form of electronic commerce that allows consumers to directly purchase goods or services from sellers over the internet using a web browser.

The previous studies [12-15] indicated that the e-commerce customers are affected by the different variables in the online activities. Eid [12] and Li [13] revealed that the customer re- purchase intention is influenced by many factors. Several scholars [14, 15] mentioned the factors, i.e., trust, perceived quality, and loyalty, are the factors that have been shown to have an effect on the intentions of consumer buybacks for online shopping.

In this study, the researchers developed ECL model (Fig.1)

by adopting the loyalty [12, 16] and future behavior models

[17], combining both models, and adapting the model based on

the input-process-output (IPO) logic [18] and the processional

and causal assumption of the previous model development

studies [19-22]. The six variables of the developed model are

Image (IMG), Service (SRV), Satisfaction (STF), Trust (TRS), Behavioral Intention (BHI), and Loyalty (LYT) variables. The questionnaire was broken down from the developed model by defining the variables and indicators from the earlier works referring to the adoption and adaptation points (Table I and II).

Fig. 1. The developed ECL model

TABLE I

LISTOF THE VARIABLESAND INDICATORS

Variables Indicators References

Image Size, Number, Color, Aesthetics, Resolution, Social Presence

[13, 16, 17, 23, 24]

Perceived Quality (Service)

Tangible Quality, Reliability Quality, Empathy Quality, Assurance Quality,

Responsive Quality,

[13, 16, 25]

Satisfaction E-Commerce Site Performance, Ease, Product Selection, Product Quality, On Time

[12, 13, 17, 26]

Trust Trustworthy, Reputation, Security,

Confidentiality, No cheating [12, 27-29]

Behavioral Intention

Based on Experience, Perceived Quality,

Intention, Planned purchase [17, 30]

Loyalty

Re-Use e-Commerce Sites, Provide Recommendations, Faithfully Use e- Commerce, Feel always together with e- Commerce Sites, Feel Having e-Commerce

Sites

[12, 13, 26]

III. R

ESEARCH

M

ETHODS

In terms of the main research scope, as a sub-study, this questionnaire assessment study was started by the previous preliminary studies (i.e., the literature review, model development, and the instrument development studies). The inputs of the study were the design of the main research and the questionnaire of the developed model. Fig. 2 presents the six phases of this study. In regard to the design of the main research, the researchers selected 29 valid data from almost 40 respondents who have experiences in the e-commerce use.

Previously, the online survey using the Google Forms was sent to approximately 100 email accounts and around 100 the WhatsApp numbers.

The researchers processed the collected data using the MS.

Excel 2013 and the IBM SPSS 20 for preparing the data analysis phase. In respect of the amount of the data, the PLS- SEM method was then employed in the analysis stage with the SmartPLS 2.0 for performing the indicator reliability, internal

consistency reliability, convergent validity, and the discriminant validity assessments [31-35].

TABLE II LIST OF THE QUESTIONS

Code Questionnaires

IMG1 The size of the product image affects my satisfaction IMG2 The number of product images has an effect on my satisfaction IMG3 The color composition of the product interest me to buy IMG4 Presentation of product images matching with website design E-

commerce website

IMG5 The image resolution affects my intention to purchase IMG6 The involvement of humans in the image while using the product

brings a sense of attractiveness and social presence SRV1 The e-commerce site provides various security and comfort facilities

for me

SRV2 E-commerce Website Services provided on time when needed, without delay, and reliable

SRV3 Service The provided e-commerce site is ready to give full attention whenever I have a problem

SRV4 The e-commerce Website Service is able to answer my technical questions and requests

SRV5 Service The provided e-commerce site is ready to give full attention whenever I need help

STF1 The performance of the website/e-commerce site is appropriate and meets expectations

STF2 Have experience in marketing the product STF3 Offer the product as per my requirement STF4 Have the best resources to run their activities

TR 1 Reliable e-commerce site

TR 2 E-commerce sites instill trust in users

TR 3 E-commerce sites fulfill the promises and commitments they assume TR 4 E-commerce sites do not behave opportunistically BHI1 I get a good experience using e-commerce Site BHI2 I am satisfied with the overall quality of the e-commerce Site BHI3 I always plan my purchase by using this e-commerce Site BHI4 I always plan my purchase by using this e-commerce Site BHI5 I will not change my intention to reuse this e-commerce site LYT1 I will use the services/buy products in the near future LYT2 I advise others to use this Website / e-Commerce Site LYT3 I will not switch to another Website / E-Commerce Site LYT4 I feel inseparable with this e-commerce site

LYT5 I feel I own this site

(P1) Preliminary

Studies (P2) Data Collection

(P3) Psychometric Data Analysis

(P4) Interpretative Data Analysis

(P6) Report Writing Questionnaire

Data

Statistical Results

Interpretative

Results Report

(P5) Confirmation Interpretation

Interpretation Results Research

Design

Fig.2 The research procedure

The researchers processed the collected data using the MS.

Excel 2013 and the IBM SPSS 20 for preparing the data

analysis phase. In respect of the amount of the data, the PLS-

SEM method was then employed in the analysis stage with the

SmartPLS 2.0 for performing the indicator reliability, internal

consistency reliability, convergent validity, and the

discriminant validity assessments [31-35]. The statistical results were then used as the input of the interpretative assessments within two brainstorming sessions. Besides the demographic information of the respondents, the cognition and responsiveness aspects of the respondents were also the consideration points of the discussions in the interpretative analysis phase [8, 36-38]. Both results of the statistical and interpretative analyses were then interpreted using a confirmation matrix in order to represent the confirmation findings [8]. Moreover, the recommendations were then revealed based on the findings and limitations of the study.

IV. R

ESULTSAND

D

ISCUSSION A. Demographic Information

Table III presents the three characteristics of the respondents, i.e., the gender, education, and the territory of the people.

TABLE III

THE RESPONDENT CHARACTERISTICS

Characteristic Group n %

Gender Male 7 24

Female 22 76

Education

High School 25 25.86

Bachelor 2 2.7

etc 2 2.7

Territory

Jakarta 15 15.55

Bogor 1 1.4

Tangerang 11 11.41

Bekasi 0 0

In short, although the questionnaire distribution does not cover the targeted territory, the characteristic information may have consistency with the phenomenon of e-commerce users.

Based on the estimation aspects [39], the consistency can be used to predict the study findings. Other considerations and sample data may be one of the limitations of the study.

Undoubtedly the consistency of data used with the real conditions of the research object may be more helpful to estimate the validity of the research findings. Therefore, it is recommended for the main study to examine the used sample.

B. The Statistical Analysis Result

Based on the statistical examinations, the results show that six of the 31 indicators were rejected (Table IV).

•

The reliability indicator examination results revealed that six of the 31 indicators (i.e, IMG2, IMG4, STF3, STF2, STF4, and BHI4) are rejected because the value did not meet the requirements of outside loading and cross- loading as described by PLS-SEM method.

•

The consistency reliability examination results show that the composite reliability (CR) of the six variables are above 0.7

•

The convergent validity examination results show that the average variance extracted (AVE) values of the variables are greater than the threshold value (0.5).

•

The discriminant validity examination results explain that five indicators (i.e., STF2, STF4, and BHI5) are also deleted referring to the Fornell and Lacker rules [31-35]

TABLE IV

THE STATISTICAL ANALYSIS RESULTS

Var. Items OL CL

BI IM LY SE SF TR

BHI

BHI1 0.782 0.782 0.258 0.425 0.233 0.444 0.622 BHI2 0.833 0.833 0.011 0.482 0.15 0.507 0.558 BHI3 0.8 0.8 0.131 0.465 0.484 0.576 0.632

BHI4 Rejected

BHI5 0.624 0.624 0.239 0.518 0.13 0.365 0.469

IMG

IMG1 0.785 0.108 0.785 0.107 0.31 0.288 0.248

IMG2 Rejected

IMG3 0.887 0.245 0.887 0.259 0.271 0.268 0.355

IMG4 Rejected

IMG5 0.527 -0.134 0.527 -0.211 0.344 -0.228 -0.052 IMG6 0.729 0.21 0.729 0.39 0.269 0.255 0.316

LYT

LYT1 0.75 0.276 0.067 0.75 -0.23 0.148 0.323 LYT2 0.737 0.689 0.376 0.737 0.149 0.421 0.709 LYT3 0.788 0.397 0.24 0.788 -0.007 0.458 0.435 LYT4 0.831 0.43 0.05 0.831 0.072 0.437 0.394 LYT5 0.785 0.37 0.149 0.785 -0.029 0.237 0.297

SRV

SRV1 0.641 0.215 0 -0.014 0.641 0.145 0.192

SRV2 Rejected

SRV3 0.65 -0.049 0.041 -0.224 0.65 -0.015 -0.054 SRV4 0.921 0.316 0.376 0.053 0.921 0.431 0.355 SRV5 0.891 0.283 0.366 0.004 0.891 0.315 0.373

STF

STF1 0.764 0.437 0.099 0.264 0.489 0.764 0.55

STF2 Rejected

STF3 Rejected

STF4 Rejected

STF5 0.81 0.545 0.32 0.468 0.157 0.81 0.644

TRS

TRS1 0.672 0.522 0.091 0.256 0.299 0.451 0.672 TRS2 0.833 0.695 0.177 0.482 0.256 0.623 0.833 TRS3 0.752 0.589 0.461 0.481 0.553 0.531 0.752 TRS4 0.714 0.48 0.373 0.692 0.132 0.548 0.714 TRS5 0.845 0.584 0.187 0.415 0.293 0.73 0.845

In short, despite the fact that six item indicators were removed, the proposed model can be statistically justified as a model with the psychometric properties [31-35]. However, the assessment may still be a limitation referring to the developed instrument and the used data.

C. The Interpretative Analysis Results

In terms of the reliability and validity of the indicators, as it is indicated by the previous statistical analysis; the results were then used to initiate the interpretive evaluation by considering the responsiveness and cognition of the sampled people. Briefly, the interpretative evaluation results relatively confirmed the statistical examination results.

•

However, Li et al. [24] indicated that IMG2 and IMG4

are the accepted indicators; but both indicators are

rejected in this study. The researchers assume that

rejection may relate to the focus of respondents, as it is

presented by the demographic characteristics [40]. In

addition, the researchers also assume that the rejection of STF3 may bias with IMG2 and IMG4. Due to the demographic limitations of the data the biases, the researchers recommended for deleting both item indicators.

•

Like IMG2 and IMG4, the rejections of the STF 2, STF 3, STF 4, SRV2, and BHI4 indicators may be due to the respondent orientation which refers to the demographics.

Another indication is that rejection may relate to an optional point of the user perceptions referring to the use of compulsory systems [41]. Thus, it is also considered to remove the four above-mentioned indicators.

In summary, Table V shows the confirmation interpretation between the statistical and interpretative results. The table demonstrates about 25 agreements with six indicators (i.e., IMG2, IMG4, STF3, STF2, STF4, SRV2, and BHI4) are suggested to be accepted taking into account the demographic distribution of the used sample used [29]. Here, the study recommended to review and refine the question.

TABLE V

THE CONFIRMATION INTERPRETATION

Results

Recommendations

S I

BHI1 Accepted Confirmed Confirmed to accept BHI2 Accepted Confirmed Confirmed to accept BHI3 Accepted Confirmed Confirmed to accept BHI4 Rejected Unconfirmed Review the question BHI5 Accepted Confirmed Confirmed to accept IMG1 Accepted Confirmed Confirmed to accept IMG2 Rejected Unconfirmed Review the sample IMG3 Accepted Confirmed Confirmed to accept IMG4 Rejected Unconfirmed Review the question IMG5 Accepted Confirmed Confirmed to reject IMG6 Accepted Confirmed Confirmed to accept LYT1 Accepted Confirmed Confirmed to accept LYT2 Accepted Confirmed Confirmed to reject LYT3 Accepted Confirmed Confirmed to reject LYT4 Accepted Confirmed Confirmed to reject LYT5 Accepted Confirmed Confirmed to accept SRV1 Accepted Confirmed Confirmed to accept SRV2 Rejected Unconfirmed Review the question SRV3 Accepted Confirmed Confirmed to accept SRV4 Accepted Confirmed Confirmed to accept SRV5 Rejected Confirmed Confirmed to reject STF1 Accepted Confirmed Confirmed to accept STF2 Rejected Unconfirmed Review the question STF3 Rejected Unconfirmed Review the question STF4 Rejected Unconfirmed Review the question STF5 Accepted Confirmed Confirmed to accept TRS1 Accepted Confirmed Confirmed to accept TRS2 Rejected Confirmed Confirmed to reject TRS3 Accepted Confirmed Confirmed to accept TRS4 Accepted Confirmed Confirmed to accept TRS5 Accepted Confirmed Confirmed to accept

V. C

ONCLUSION

The question around the suitability of the developed questionnaire in this study may interesting for IS researchers, especially those who developed the survey instruments based on the adoption, combination, and the adaptation of the previous studies. However, besides the relatively limited number of instruments being assessed, the instrument assessment study may remain some gaps. Therefore, the implementation of the study may still be interesting to continue.

In this study, sequential implementation of psychometric and interpretative assessments was conducted to examine and explore the validity and reliability of the questionnaire. The results show that six of 31 instrument questions are recommended to be rejected. In addition to the results of psychometric and interpretative analysis confirmations, a clear presentation of meta conclusions may also be the second point highlighted in the study.

In addition, the study also restricts its use according to the samples used, the developmental questionnaire itself, and the techniques and abilities of interpretive analysis. Therefore, the findings can not be generalized to others. Furthermore, although this research may not contribute theoretically to the field of research, at least, the recommendations proposed here may be one of the practical considerations for revising the main study questionnaire and the clarity of the conduct of the study may be one of the questionnaires. alternative assessment for similar works.

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