Implementing Affinity Analysis in Determining Critical Factors

On e-Service Systems in Malaysia

Ahgalya Subbiah Department of Information System

Faculty of Computer Science and Information System Universiti Teknologi Malaysia

utmahgalya@gmail.com

Othman Ibrahim Department of Information System

Faculty of Computer Science and Information System Universiti Teknologi Malaysia

othmanibrahim@utm.my

Abstract— Understanding on what people desires and require become a great challenge of e-service based industries today. This paper tries to understand the issues in service experience in e-governments service applications of this complex society’s as well as likeability and expectation on the e-services by implementing affinity analysis to discover critical factors for further exploration. This paper also explains how affinity analysis can be applied to interpret subjective data into an analytical approach, and finally determine the critical factor using cause effect analysis. It aid researcher with a simpler yet richer idea of the subject been interpreted in the subjective perspective. A pilot study’s data is applied in this analysis which consists of focus group and personal interview. This study includes users from different age, genders and professions of e-service users in e-government applications in Malaysia. Data were collected from users residing from 13 states in Malaysia. Keywords- Affinity Analysis, e-Services, Malaysian Government, Pilot Study

I. INTRODUCTION Malaysia’s multicultural environment leads its victorious development in various scales of governmental management such as introduction of e-government in 1997[16]. This also unlocks the door globally towards continuous collaborations where unknowingly the citizens living style too changes permanently. Though this changes figurine a developed nations, but considering the requirements of personalization, trust, better service, faster and availability made this e-related services pressures for improvement [6]. The scale and cost to re-infrastructure the idea of e-service raises arguably, possibly undergoing a paradigm shift in the concept of service; from top down to bottom up [11]. Despite the additional input of e-government as a platform for citizens’ benefit, managing the citizen itself is becoming increasingly complex as information is at their fingertips and the value of demand proves this style [7]. Hence, managing this issues and to provide a better service particularly those relates with e-government activities, guises important elements in service delivery propaganda to avoid failure [4].

Research related to understanding the concept of ‘servicing the service’ initiated the researcher to explore users’ likeability and expectation on the e-services which has been provided by the e-government via various agencies to citizen. Positively, this means that current trend of service concepts in government to citizen (G2C) particularly bridge gabs for action and plans as it was not prudent to assume the boundary-less information flow allows user to demand and express dissatisfaction which actually engaged with underlying intent and rationale of personalized service.

It is therefore, important to acknowledge these circumstances in the base of this research. Many studies been closely looked in the area of service [14], [6], [10], [2], [3], [8], [11], [12] and [13] where various instruments were used as source of data collection. Studies in Interprevite persective use various instruments for data gathering purposes. Some of the well-known tools are: Focus Group, Interview, Grounded Theory,Criticalanalysis,Ethnomethodology, Phenomenology, Hermeneutics and Observation [14]. Acknowledging the time-consuming task on interpreting qualitative data, probes researcher to explore feasible approaches to solve the concern. This is particularly to avoid redundancy in the analyzed data as well as to safe time. Affinity Analysis (AA) is a data analysis technique which is commonly used to discover co-occurrence association among activities executed in groups. This technique is used in vast disciplines and one such is in marketing where, it is applied to understand the customers’ behavior [15]. User’s information’s which is a valuable asset can assist the agents to either improve or up-price a sale. This motion can be seen in additions to the discount plans, sale-promotions and so on. It actually provides the retailer on insight of what user really looking forward and clusters them for futuristic profit, or in other words a roadmap towards co-creation of value in service success. This technique reasoned the importance of understanding the customers’ behavior to overcome complexity in retail and as such it is also suits well in order to manage the issues on user demands towards service likeability or personalization on e-services which has been provided by the e-government in this nation. In the matter to this research, NEWS matrix of AA is used which acts as interpreter of qualitative data in mathematical

approach. Every segment provides an individual reasoning to understand users’ behavior of in satisfaction in e-services system. An example, Need of NEWS matrix actually probes possible answer of what users really need for personalized e-service system. Every matrix related to each other where higher the degree of site promises higher degree of satisfaction. Each collected data will be coded according to the scheme and the relationship will be tested. The obtained result based on the coded scheme guided the interpretation that promises discovery of co-occurrence and grouping the respond obtained in cause effect analysis confirmed the identified factor. A pilot study was conducted closely with the experts’ review, to attain most relevant critical factor that caused in-satisfaction in e-service delivery among users. This paper is divided into 4 sections, where section 1 opens up with the introduction of case and approach continued with section 2: briefing on pilot study. Section 3 explores on the affinity analysis with the process includes fishbone diagram analysis and finally section 4 concludes the study with discussion for further study.

II. PILOT STUDY Focus group which contributes to the qualitative instrument was used primarily in the pilot study. It provides the researcher with ideas, approaches, and clues that may not have foreseen before conducting the pilot study [1]. Likewise, focus group benefits researcher from the ability to query with open ended questions allows participants to expel information without boundary based on own individual knowledge and understanding [5]. Based on the evaluation given by the experts, researcher re-modified the focus group plan accordingly. The groups were premeditated to address the extend issue in service experience in e-governments service applications. Participants were recruited from 13 states of Malaysia. A total of 3 groups which sums of 24 participants consists of 8 people in a group were setup. Participants were chosen randomly based on profession and gender including age factor. It is an excellent way to get people's opinions, or to learn how they feel about something, or how they think about something. Three sessions was done in separate time and venue. The participants was asked to fill a registration form consists of: age, gender, active or passive user and origin states for the purpose of time saving as shown in Table I. Each participant was given a name tag for acknowledging purpose during the discussion. Researcher conducted each section separately with an assistance of a recorder to note the discussions. Researcher followed the developed discussion guide to follow the schedule along with simple refreshments. The researcher act as the moderator begin the session by prompting a purpose statement of: “ to gain understanding of e-service system users sheathing behavior towards in-satisfaction and to explore potential factors which leads to such situation, if

any to encourage personalization possibilities for service success”. Later more questions were asked which is based on NEWS matrix of AA where the representation of N-need, E-expectation, W-want and S-satisfaction (NEWS) that generally let participants to voice out rather than to follow pre-standard rules. Researcher maintained a free-voice environment to allow implicit information gathering as well as understand user’s point of view. Mixture of this is believed would help researcher to generalize the gap that capture sheathing in the e-service. Questions which was related to subject were asked commonly rather in direct approach. Researcher also observed each participant’s behavior through out the section as it also provided a general idea of user likeability of the services and their mood throughout the session. Of the 24 participants, female users dominated the study mainly due to researcher’s personal interest to understand female perspectives over ICT and its impact in their lifestyle as shown in Figure1. This study was mainly done to understand the situation from the citizens’ view, where enhancement would come from their requirements.

Table I. Focus Group Results Based on the focus group result, as shown in Figure I gender comparisons provide researcher with a unique feedback. Results showed that female users intend to be more approachable compared to male participants. Male participants merely provide direct and short answers compared of female users who gives informative replies. Though male participants were the active user of e-services to compliment their task, yet they do provide little issues. It could be mainly because of nature in the gender itself. Comparing with female users, researcher found that it’s easier to approach in term of participating behavior .Female

Figure III. Affinity Analysis NEWS Matrix

Female

Male

user provides ample guidance as well as good team work compared to male participants. Hence, further study in same perspective would be added as an advantage. But, in comparing the difference between active and passive users, results showed that both gender pays almost similar remark. Figure I. Ratio between genders in term of participation of e-service user However, different result triggers the study towards female user when Figure II showed those female users are passive towards e-service applications provided by the e-government.

Figure II. Comparisons between active and passive user This could be age factor or ones lifestyle as well as education background. However, deeper understanding and exploration is considered important to acknowledge this, because ICT become the necessity in every aspect of e-services and by neglecting such services may lead to complex challenges and even service failure [4]. Another issue which possibly caters this would be the issue related to co-creating personalization [6] in e-government vision for every citizen. Based on the extraction of the interviews, data was later positioned for a dependence test and to gather main critical factor.

III. AFFINITY ANALYSIS Affinity analysis (AA) enriched with method for experimenting the variance degree of relationships in an activity performed. It’s also known as an association rule, data analysis or data mining technique to discover co-

occurrence in a specified group. It’s a very flexible method that can be applied to any process to identify unique information. It immerses the participants strategically in process to integrate the divergent ideas generated with convergent thinking. Divergent thinking allow participants to expel information without boundary based on own individual knowledge and understanding. Convergent thinking is a process of pooling the idea from participants to avoid duplication. Through this activity individual terms are edited minding significant of the project, later grouped into categories [5]. AA consists of 2 preliminary steps of NEWS. Initial phase begin with divergent thinking sessions which usually conducted using interview or focus group sessions by applying NEWS code. Continued with the dependence test and coding via convergent thinking process which commonly uses cause effect analysis, done in second phase. Researcher followed advice from the expert by occupying original data in the appropriate matrix consisting of degree of sites (column) and by degree of importance (row) or 2x2 matrix of similarity co-efficient as shown in Figure III. In order to understand the relationship between two variable of x and y, researcher collected random data n from 24 participants that related to x and y, this is defined as in (1)

y=f(x) (1)

Where, f is an unknown factor. Therefore, the form of f(x) is modeled by using random (n) data in rule (2) as explained in rule (1): yί= αί+Σί (2)

where, ί is data of yί is data of xί and Σί is data of error; by estimating data to minimize α and β to minimize error from the sum of squares of error or as shown in rule (3); Σn =Σί2ί (3) And upon completion, the result produced as; ^y= ax+b Where, a is estimation of α and b is estimation of β;

femalemale

Is important to acknowledge that the assumption on error that mentioned is assumed that all errors of Σ2,Σ3….Σn are

independent, whether its huge or little is depend on R2 as followed in rule (4); Σқ ~N (0, β2) (4) Where, n is normal. It is also important to acknowledge that lower the R2 higher the β2 and higher the R2 ≤ β2, it’s important to rise to avoid underestimation or estimation. Note here, that R2 which has been defined as R2 is the population of information explained by the model as in rule (5); 0≤ R2 ≤ 1 (5) According to experts suggestions R2=1 is considered excellent, which showed the data is correlated. Based on the analysis done, which says that if there is only one result for all classification the correlation is =1. The reported margin of error is typically about twice the standard deviation which is the radius of 95% confidence interval. The results of the test conducted are placed in Table II.

Table II. Results of coded schemes and correlation analysis

Using the 80/20 rules where, 20% of work and 80% of the advantage of doing entire job is selected, because 80% actually presents the majority problem that is produced by the key point causes by 20%. With the correlation level resulted as 1, which also showed that the confidence level of 95%, results the deviation is highly dependent. And to acknowledge the variable that represents the 80% of work, researcher applied cause effect analysis to consider thoroughly of a problem. It provides a structured way to compete all possible causes of a problem. It is also known as Fishbone Diagram [9]. It is utilized upon completing the AA for understanding the root cause for answering the question of role of citizen to enhance service delivery towards successful service system of e-government. Researcher used 8Ps that used in service industry by Ishikawa. The clustered cases are then labeled based on these categories:

I. Product=Service II. Place

III. People (key person) IV. Physical Evidence V. Price

VI. Promotion/Entertainment VII. Process

VIII. Productivity & Quality

Finally, with the guidance of expert, data were clustered with the themes accordingly. Based on the root cause information, researcher developed a fish bone diagram as shown in Figure IV. Figure IV. Result of Critical Factor Using Fish Bone Diagram

No

Classification

Gro

up

1

Gro

up

2

Gro

up

3

Tota

l # o

f ans

wer

s (R

elat

ive

Freq

uenc

y)

Tota

l %

Cum

ulat

ive

Fre

quen

cy

Cum

ulat

ive

%

1

Functionality 9 16 11 36 24 36 24

2 Usability 6 13 9 28 18.67 64 42.

67

3

Connectivity 8 9 4 20 13.33 104 56

4 Flexibility 7 9 4 20 13.33 104 69.

33

5

Expandability 7 7 3 17 11.33 121 80.67

6 Reliability 7 5 5 17 11.33 138 92

7 Security 4 5 1 10 6.67 148 98.

67

8 Privacy 2 0 0 2 1.33 150 100

TOTAL 50 64 36 150 100 TOTAL 150

Hence, the initial pilot study which derived from the social perspectives generates further suggestions by identifying the fit between social & technical aspects on the cultured based interaction that actually derived from the user requirement. Based on the root cause analysis in Figure IV, this clearly shown that users demand towards finer products in the manner of : interactive features, pleasant pages that is also attractive yet simple without many distraction and most importantly relevant subject is presented merely grooming towards personalization on service that they requires. Affinity analysis provided the relevant issue that needs further exploration as well. Functionality with highest frequency scores of relevant answers also triggers the further study which in other way relates to product as in the root cause analysis factor. In other word, product with appropriate function leads towards higher degree of importance in users’ daily utility. As mentioned earlier, researcher attempts to find the sheathing of in satisfaction among e-service delivery users in Malaysia that may promote enhancement towards bottom up serving system. In order to further understand the phenomena, researcher chooses the emerging issues concerning two important factors of time and cost involved. Therefore, functionality would be furthered with a case involves government agencies whom has similar issues.

IV. DISCUSSION This paper discusses on implementing affinity analysis (AA) in qualitative study to determine the critical factor on problems in e-services in Malaysia. A pilot study involving focus groups of 24 participants from Malaysia were gathered to identify users’ issues on sheathing in satisfaction in service delivery in e-government service system. Based on the focus group discussion, participants were mainly felt confused on the usage of the system rather than the process that actually involved. Results from the AA showed that, functionality holds 80% of the issue caused by 20% of overall functionally of the serving system itself. This could be reasoned mainly because of users’ behavior is accumulating through the 80% functions which interrelated to behavior that again is blended with culture and living style, whereby 20% of the functionality would be the technical issues that lies implicitly. This also would be the general overview of the current system which resembles in the root cause analysis. With the correlation level resulted as 1, which also showed that the confidence level of 95%, results that the deviation is highly dependent. The affinity analysis uses NEWS matrix which gives a new experience on interpreting qualitative data in experimenting method, and has clearly showed that affinity analysis can be used to interpret qualitative data in mathematical codes in non-inaccuracy output. It guides researcher to interpret and understand the confidence level which root the cause towards required critical factor. Determining critical factor

of qualitative data using experimenting method such as affinity analysis relieves the issues of generalization in Qualitative method makes the result obtained more reliable for further study. The pilot study also provides the next phase of the research, which is to focus on female users of e-services. Based on the study, result provided indicates female users are reluctant towards ICT. It is considered important to analyze this issue, because ICT became the backbone of every transaction today. Though this is only a pilot study, it helps researcher to understand the feel and behavior as well as need of every citizen to make the service provided by government via e-government initiative a success. Focus group method added as an advantage to get closer to citizen from various profession, age and gender to explore and expose their liking for citizen oriented government.

ACKNOWLEDGMENT This project is sponsored by National Science Foundation, MOSTI (Malaysia) and Research University Grant, V02J13 Universiti Teknologi Malaysia.

REFERENCES

[1] Doug A.N.B, Innes C.Micheal F.Janes H,Laura. P (2006).Why do a pilot study? On National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3R):NC3Rs Experimental Design Working Group.

[2] Micheal Grimsley, A.M (2008).Attaining Social Value from Electronic Government. The Electronic Journal of e-Government. 6(1).

[3] Gummerson,E. ( 2004). Return on Relationship (ROR): The value of relationship marketing and CRM in business-to-bussiness context.Journal of Bussiness and Industrial Marketing, 1(2) 136-148.

[4] Heeks,R. ( 2006).”Government for Development: Causes of e-Government Success and Failure: Factor Model” IDPM, University of Manchester, UK. Retrieved from http://www.egov4dev.org/causefactor.htm

[5] Konstanze Aurich, S.N. Elena Heister and Werner Weitschies. ( 2008). Affinity Analysis for biomolecular interactions based on magneto-optical relaxation measurements. Nanotechnology 19(505102)

[6] Mohsin Ahmad, R.O. ( 2007).Implementation of Electronic Government in Malaysia. The Status and Potential for Better Servce to the Public. Retrieved from http://apps.intan.my/psimr/vol1no1/Editorial%20and%20Editorial%20Commitee.pdf.

[7] Prahalad C.,V.Ramaswamy (2004).Co-Creation Experience. The Next Practice in Value Creation. Journal of Interactive Marketing, 18(3).

[8] Pine, .J. &Gilmore,J.H.( 1999).The experience economy: Boston.Harvard University Press

[9] Ozeki, Kazuo & Tetsuichi (1990). Handbook of Quality Tools. Productivity Press, Cambridge, MA.

[10] RamlahHussein,Abdul Rahman Ahlam, Murni Mahmud and Umar Aditiawarman. (2009).G2C Acceptance in Malaysia: Trust, Perceived Risk ad Political Efficacy. Paper presented at the International Symposium on Innovation in Information and Communication Technology ISCIT’2009, Amman Jordan

[11] Spohrer,J. (2006). Convergence and Co evolution: Towards a Service Science: M.C Roco and W.S Bainbridge, eds. Nanotechnology: Societal Implication: Maximizing Benefits for Humanity, Springer 1.

[12] Subbiah A, Ibrahim O. (2011). Value Co-Creation Process: A Case of E-Service for G2C in Malaysia. European Journal of Social Science, 19(3)

[13] Vargo, S. Lursh,, RF. (2006).The Service Dominant Logic of Marketing Dialog, Debate and Direction. New York,USA:M.E.Shape,Inc.

[14] Walsham, G. (2006). ‘Doing Interpretive Research’. European Journal of Information Systems. 15(3),320-330.

[15] Y. Aumann and Y. Lindell. A statistical theory for quantitative association rules. In Proc. 1999 Int.Conf. Knowledge Discovery and Data Mining (KDD'99), pages 261-270, San Diego, CA, Aug. 1999.

[16] Yildiz, M. (2007).E-Government research: Reviewing the literature, limitations and ways forward. Government Quartely, 24(4), 646-665.