Business Strategy SeriesApplication of importance-performance analysis (IPA) to formulate customer satisfaction strategies in the direct salesindustry in MalaysiaRosidah Musa John Pallister Matthew Robson Norzaidi Mohd Daud

Article information:To cite this document:Rosidah Musa John Pallister Matthew Robson Norzaidi Mohd Daud, (2010),"Application of importance-performance analysis (IPA) toformulate customer satisfaction strategies in the direct sales industry in Malaysia", Business Strategy Series, Vol. 11 Iss 5 pp. 277 - 285Permanent link to this document:http://dx.doi.org/10.1108/17515631011080687

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Application of importance-performanceanalysis (IPA) to formulate customersatisfaction strategies in the direct salesindustry in Malaysia

Rosidah Musa, John Pallister, Matthew Robson and Norzaidi Mohd Daud

Introduction

A number of studies have provided evidence validating the importance of satisfaction as a

means to strategic ends; such as customer loyalty and customer retention, that directly

affects company’s profits (Bernhardt et al., 2000; Burnham et al., 2003; Rosidah et al., 2010;

Zeithaml, 2000). In view of this, it comes as no surprise that customer satisfaction has

received much attention in the marketing literature and is the primary focus of our research

endeavor. Whilst there has been resurgent interest in this research area, the critical issue of

identifying the key factors that drive customer satisfaction has not been an easy task,

specifically to the practitioners. Customer satisfaction findings particularly from the

academic community have been criticized for being operationally irrelevant and difficult to

be translated into managerial actions (Piercy, 2002; Westbrook, 1997, 2000). In a similar

voice, Patterson et al. (1997) assert that considerable research has focused on modeling the

influence of perceived performance on satisfaction at the aggregate construct level;

examining the effect of individual attribute performance on satisfaction judgment is more

useful as it provides more diagnostic and actionable decisions for practicing managers

(Roszkowski, 2003). Accordingly, Westbrook (1997) suggests that to improve customer

satisfaction measurement, research should address specific product or service

features/dimensions and demonstrate how the key attributes are associated with overall

customer satisfaction.

In this sense, it is no surprise that issues such as how and why direct and derived methods

may differ has long been debated and is a major concern among several authors (Bacon,

2003; Oh, 2001; Matzler et al., 2003). Indeed, the validity of importance-performance

analysis (IPA) as a managerial analytical tool has been frequently questioned (Bacon, 2003;

Oh, 2001; Matzler et al., 2003). Recognizing the aforementioned drawbacks and unresolved

disagreements associated with the classical IPA, which primarily relies on the stated

importance, it is thus clear that a modified or an alternative approach be proposed. The

primary purpose of our paper is to compare the importance ratings and rankings of the two

importance measurements (stated and derived approach), and demonstrate the critical

differences when the key attributes are visually plotted on the IPA grids.

Research methodology

The target population criteria for this study were adult consumers (over 16 years of age) who

have purchased beauty or healthcare products from the direct seller within the 12 months

prior to the data collection period, and who live or work within the selected geographic

locations; in this case three designated districts (Petaling, Kelang and the Federal Territory

of Kuala Lumpur). A sample of 400 survey respondents was obtained based on a quota

sampling technique. Initially, 34 items were assessed individually to determine the relative

DOI 10.1108/17515631011080687 VOL. 11 NO. 5 2010, pp. 277-285, Q Emerald Group Publishing Limited, ISSN 1751-5637 j BUSINESS STRATEGY SERIES j PAGE 277

Rosidah Musa is an

Associate Professor at the

Faculty of Business

Management, Universiti

Teknologi MARA, Selangor,

Malaysia. John Pallister and

Matthew Robson are based

at the Cardiff Business

School, Cardiff University,

Cardiff, UK.

Norzaidi Mohd Daud is an

Associate Professor at the

Faculty of Business

Management/Institute of

Business Excellence/

Accounting Research

Institute, Universiti

Teknologi MARA, Selangor,

Malaysia.

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importance and perceived performance respondents attached to these. Specifically, the

informants were asked to rate the importance of the items (attributes) in fulfilling their

satisfaction, while in the performance measurement, respondents were requested to

indicate their level of satisfaction with the items (attributes). Performance evaluation

employed a five-point satisfaction-dissatisfaction scale anchored with (1) ‘‘very dissatisfied’’

to (5) ‘‘not applicable’’. The importance rating is measured on a five-point scale anchored

with (1) ‘‘not at all important’’, to (5) ‘‘very important’’. The IPA grids are visually displayed in

the form quadrant chart for each aspect of the direct sales marketing channel system. The

pool of 34 items for performance measurement were assessed for item-total correlations and

adjusted using exploratory factor analysis and subsequently validated using confirmatory

factor analysis (structural equation modeling technique via AMOS 5 program). The

item-to-total correlation scores were in the range of 0.35 to 0.67, which signifies a reasonably

good item performance. The measures yielded a reliability coefficient (Cronbach’s alpha)

ranging from 0.80 to 0.88, which is greater than the recommended threshold of 0.70

(Nunnally, 1978).

It is widely recognized that more robust statistical tests such as confirmatory factor analysis

(CFA) should be employed to confirm and verify the dimensions underlying the factors

derived from EFA (Gerbing and Anderson, 1988). Parallel to this suggestion, CFA was

performed on the remaining 24 items using structural equation modeling and this resulted in

a deletion of three more items. The estimation of the measurement model yielded

respectable goodness-of-fit index (0.91), comparative fit index (0.93) and incremental fit

index (0.93) values. The Root Mean Square Residuals value of 0.03 confirmed a good fit,

which exceeded the posited cut-off value (,0.05) (Norzaidi et al., 2009, 2007). The Root

Mean Square Error of Approximation value of 0.06 was satisfactory. Overall, the results of the

fit indices indicate that the measurement model adequately fits the data (Norzaidi and Intan

Salwani, 2009). Hence, it is reasonable to believe that the measures utilized in our analysis

have adequately met the unidimensionality, validity and reliability criteria applied.

Results and discussions

The initial analysis performed was that of assessing the attributes performance and degree

of importance and subsequently the satisfaction gaps were computed (performance mean

values minus stated importance mean values). In order to illustrate the differences between

self-stated and derived importance, the rankings of attributes were compared. We compare

importance rating and ranking of individual attribute, which were assessed by self-stated

approach and two derived importance assessment methods (multiple regression and

correlation analysis). Table I displays the summarized results of the analysis. The results of

the comparison of ratings, rankings and satisfaction gap across the three methods are

organized according to the dimensions of direct sales channel.

Importance-performance analysis results

Figures 1, 2 and 3 illustrate an importance – performance chart (grid), which was used to

highlight and demonstrate clearly the four classifications of strategies. More importantly, in

this section we will demonstrate that the outcome of an IPA is very sensitive to the

assessment of attributes importance method employed. Figure 1 exhibits the IPA using the

direct rating scale as a measure for attribute importance. The mean values for overall

importance (4.09) and overall satisfaction (3.70) were used to split the axes, this produced

four strategies. The summarized results of the IPA are illustrated in Table II. Table III

represents items used in the quadrant charts (see Figures 1, 2 and 3).

Figure 2 portrays a quadrant chart which is based on the multiple regression analysis in the

assessment of the attributes importance. Whilst Figure 3 depicts a quadrant chart derived

from attribute importance via correlation analysis. The summarized results of the analysis of

the IPA are presented on Table III. The discussion on the IPA results is illustrated and

interpreted with the aid of four classifications of strategies.

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The final importance-performance analysis model

Figure 4 portrays the resulting items that fall in the same zone when three methods were

employed to assess attributes importance. The results demonstrate that item 1

(effectiveness of product) and item 8 (knowledge of products and services) fall in the

Table I Summarized results of performance mean and mean differences, stated importance and derived importance

Derived importance

Items/factors(A) Performance

mean(B) Stated

importance meanC (A-B) meandifferences

Regressionweight (beta)

Correlationcoefficient

Product satisfaction a ¼ 0.80 3.77 4.12 20.35 0.22 0.421. Effectiveness of product 3.78 4.32 (1) 20.55 0.31 (1) 0.55 (1)2. Product guarantee 3.66 4.24 (2) 20.58 NS 0.38 (4)3. Product information adequacy 3.86 4.17 (3) 20.31 NS 0.35 (6)4. Product performed as claimed 3.67 4.16 (4) 20.49 0.20 (3) 0.53 (2)5. Product catalogue/brochure 4.00 4.02 (5) 20.02 0.12 (4) 0.33 (7)6. Innovative and unique product 3.72 3.97 (6) 20.25 0.23 (2) 0.45 (3)7. Product is multifunctional/multipurpose 3.67 3.94 (7) 20.25 NS 0.37 (5)

Direct seller satisfaction a ¼ 0.88 3.68 4.05 20.37 0.15 0.548. Knowledge of products and services 3.81 4.21 (1) 20.40 0.19 (2) 0.55 (4)9. Have customer interest at heart 3.59 4.09 (2) 20.50 0.12 (3) 0.56 (3)

10. Being consistently courteous 3.78 4.08 (3) 20.30 0.08 (6) 0.51 (6)11. Availability of direct seller 3.68 4.07 (4) 20.39 0.10 (5) 0.53 (5)12. Giving personal advice and attention 3.64 4.05 (5) 20.41 NS 0.48 (8)13. Effectiveness of sales demonstration 3.67 4.02 (6) 20.35 0.30 (1) 0.61 (1)14. Maintaining a professional appearance 3.65 3.85 (7) 20.30 0.11 (4) 0.58 (2)15. Continuity of contact 3.59 3.92 (8) 20.33 NS 0.50 (7)

Company satisfaction a ¼ 0.84 3.66 4.09 20.43 0.21 0.5216. Company’s reputation 3.94 4.19 (1) 20.25 0.17 (3) 0.45 (6)17. Concern about customers 3.65 4.18 (2) 20.53 0.29 (1) 0.59 (1)18. Handle complaints promptly 3.44 4.12 (3) 20.68 NS 0.51 (3)19. Product return/adjustment policy 3.59 4.08 (4) 20.49 0.24 (2) 0.55 (2)20. Sales campaigns/promotion) 3.71 3.99 (5) 20.28 NS 0.47 (5)21. Corporate information/publicity 3.63 3.98 (6) 20.34 0.13 (4) 0.49 (4)Overall Satisfaction 3.70 4.09 20.39 0.19 0.50

Notes: The numbers in parentheses represent individual item ranking based on the dimension level; NS denotes not significant at 0.05significant levels

Figure 1 Quadrant chart based on stated importance

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‘‘keep up the good work’’ grid. This suggests that they are the key drivers of customer

satisfaction with the direct sales channel – in other words these two attributes could be

viewed as sources of significant competitive advantage. Item 4 (product performed as

claimed) and item 17 (concern about customers) are located in ‘‘concentrate here’’ zone.

This indicates that these areas demand immediate actions. By prioritizing improvement on

these areas, practitioners might enhance overall customer satisfaction. However, if they are

ignored this might pose a serious threat to the direct sales firms. Item 21 (corporate

information/publicity) was revealed to be a ‘‘low priority’’ area, hence it is not necessary for

the management to focus additional effort here. Finally, it was identified that resources

committed to item 5 (product catalogue), which is deemed a ‘‘possible overkill’’ should be

channeled to attributes located in the ‘‘concentrate here’’ zone. In essence, the combination

of the three methods could identify the key drivers of customer satisfaction with the direct

sales channel and areas need improvements with more confident rather than just relying on

the self-stated approach. In addition, since the results of the analysis are based on attribute

level, they could generate precise action plans to the direct sales practitioners.

Figure 2 Quadrant chart based on multiple regression

Figure 3 Quadrant chart based on correlation analysis

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Table II Summarized results of IPA for stated and derived importance (regression and correlation) methods

Derived importanceStated importance Regression (beta values) Correlation (coefficients)

Quadrant 1: keep up the good workItem 1 (Effectiveness of product)a

Item 3 (Product information adequacy)Item 8 (Knowledge of products andservices)a

Item 16 (Company’s reputation)

Item 1 (Effectiveness of product)a

Item 6 (Innovative and unique product)Item 8 (Knowledge of products andservices)a

Item 1 (Effectiveness of product)a

Item 8 (Knowledge of products andservices)a

Item 10 (Being consistently courteous)

Quadrant II: Concentrate hereItem 2 (Product guarantee)Item 4 (Product performed as claimed)a

Item 9 (Have customer interest at heart)c

Item 17 (Concern about customers)a

Item 18 (Handle complaints promptly)c

Item 19 (Product return/adjustment policy)c

Item 4 (Product performed as claimed)a

Item 13 (Effectiveness of salesdemonstration)b

Item 17 (Concern about customers)a

Item 4 (Product performed as claimed)a

Item 9 (Have customer interest at heart)c

Item 11 (Availability of direct seller)Item 13 (Effectiveness of salesdemonstration)b

Item 14 (Maintaining a professionalappearance)Item 17 (Concern about customers)a

Item 18 (Handle complaints promptly)c

Item 19 (Product return/adjustmentpolicy)c

Quadrant III: low priorityItem 7 (Product ismultifunctional/multipurpose)c

Item 12 (Giving personal advice andattention)c

Item 11 (Availability of direct seller)d

Item 13 (Effectiveness of salesdemonstration)Item 14 (Maintaining a professionalappearance)d

Item 15 (Continuity of contact)c

Item 21 (Corporate information/publicity)a

Item 9 (Have customer interest at heart)Item 11 (Availability of direct seller)d

Item 14 (Maintaining a professionalappearance)d

Item 21 (Corporate information/publicity)a

Item 2 (Product guarantee)Item 7 (Product ismultifunctional/multipurpose)c

Item 12 (Giving personal advice andattention)c

Item 15 (Continuity of contact)c

Item 21 (Corporate information/publicity)a

Quadrant IV: possible overkillItem 5 (Product catalogue)a

Item 6 (Innovative and unique product)c

Item 10 (Being consistently courteous)d

Item 20 (Sales campaigns/promotion)

Item 5 (Product catalogue)a

Item 10 (Innovative and unique product)d

Item 16 (Company’s reputation)b

Item 3 (Product information adequacy)Item 5 (Product catalogue)a

Item 6 (Innovative and unique product)c

Item 16 (Company’s reputation)b

Item 20 (Sales campaigns/promotion)

Notes: aItem appears in stated, and derived (regression and correlation) importance approach; bItem appears in both derived(regression and correlation) importance approach; cItem appears in both stated and derived (correlation) approach; dItem appears inboth stated and derived (regression) importance approach

Table III Items represented in Figures 1, 2 and 3

Product satisfaction Direct seller satisfaction Company satisfaction

1. Effectiveness of product2. Product guarantee*3. Product information adequacy*4. Product performed as claimed5. Product catalogue6. Innovative and unique product7. Product is multifunctional/multipurpose*

8. Knowledge of products and services9. Have customer interest at heart10. Being consistently courteous11. Availability of direct seller12. Giving personal advice and attention*13. Effectiveness of sales demonstration14. Maintaining a professional appearance15. Continuity of contact*

16. Company’s reputation17. Concern about customers18. Handle complaints promptly*19. Product return/adjustment policy20. Sales campaigns/promotion*21. Corporate information/publicity

Note: *Item non-significant at the 0.05 level

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Conclusions

The results for the comparison of ratings and rankings across the three methods clearly

demonstrate the significant influence of the methods of determining the attributes

importance. Only item 1 (effectiveness of product) was ranked as the most important

attribute for product satisfaction across all the three methods. Surprisingly, after rigorous

measurements assessment, our investigation revealed that seven items (2, 3, 7, 11, 15, 18

and 20) were not significant drivers of customer satisfaction (see Table I). We suspect that

methodological factors such as social desirability bias and extreme response tendencies

due to lack of ‘‘cognitive sophistication’’ (Rao and Kelkar, 1997, p. 81) resulted in uniformly

high ratings in self-stated importance ratings (see Table I). Thus in this sense, we

recommend that future studies cross-validates the direct rating for assessing the attributes

importance with more rigorous statistical analysis techniques such as multiple regressions

and correlation analysis in the order to achieve more accurate measurement of importance.

Alternatively, a satisfaction gap score can be interpreted as an area of potential

improvement and requires immediate attention, which is equivalent to the ‘‘concentrate

here’’ strategy in the quadrant chart. Apparently, this indicates that the direct sellers or the

direct selling companies should direct attention to making improvements in this area. By

improving the ‘‘importance’’ attributes that fall short of ‘‘performance’’ judgment by the

customers, the company could achieve the desired outcomes and prevent money from

being spent on improving unimportant attributes. In short, the findings could provide clear

guidelines for direct sellers or companies in allocating and aligning internal resources and

effort in order to maximize customer satisfaction and perhaps subsequently their retention.

Our results, which are illustrated in Table I, revealed that item 2 (product guarantee) had the

largest gap (0.58) falls appropriately in the ‘‘concentrate here’’ zone in the quadrant charts

based on stated importance (see Figure 1). In contrast, it was located in ‘‘low priority’’ zone

in the quadrant chart based on correlation analysis (see Figure 3). Surprisingly, this item was

not a significant driver of product satisfaction. The largest gap for direct seller (have

customer interest at heart) (0.50) was appropriately located in the ‘‘concentrate here’’ zone

for both self-stated and correlation analysis methods for determining attributes importance

but conversely this particular attribute fall in the ‘‘low priority’’ zone in the quadrant model

based on regression method. It should be noted that the largest gap for the direct sales

company dimension (handle complaint promptly) (0.68) was located correctly in the

‘‘concentrate here’’ zone for both self-stated and correlations analysis approaches but this

specific item was not a significant driver of customer satisfaction with the company. The

findings were perplexing but relatively consistent across the three methods employed to

assess attributes importance. Nevertheless, this analysis must be used prudently and the

results should be interpreted with caution.

Figure 4 The final IPA model

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Generally, the results of IPA presented by the current investigation reflect that the matrix is

sensitive to the importance measure used. These empirical findings further support the

similar evidence drawn by previous researchers (see, for example, Matzler et al., 2003).

Clearly, this research has demonstrated that classical IPA could be misleading even when

valid and reliable measures were used. Only one or two attributes fall into the same zone

when the attributes importance was assessed across three methods. Thus, we strongly

encourage users not only to employ direct importance ratings but to cross-validate it with

other methods of assessing the attributes’ importance such as regression and correlation

analysis as presented by our final IPA model (see Figure 4). In addition, we suggest

researchers and practitioners employ valid and reliable measures when conducting IPA, as

our results demonstrate that several items fall into the same zone even though different

methods of assessing the attributes’ importance were utilized supported by findings from

prior studies (see, for example, Bacon, 2003; Matzler et al. 2003). IPA approach as

suggested by our study may not be a simple analytical technique, when compared to its

classical version, however it is still considered applicable and relevant to the practicing

manager. The crucial question is how valid is IPA as a tool that yields prescriptions for

courses of action that a company can pursue in the aim to manage customer satisfaction. In

our view, if IPA is to be employed confidently, this difficulty-accuracy trade-off is certainly

worth strong consideration.

It should be noted that a limitation of this study is that the ratings of the attributes’

performance and importance might be influenced by respondents’ demographic

backgrounds and experiences. It is reasonable to speculate that as a consequence, IPA

yielded mixed results where clear and logical trends could be recognized. In this regard, this

could offer a fruitful future research direction; IPA needs further examination coupled with a

theoretical explanation. Specifically, future research should develop IPA models based on

different customer segments. It is possible and reasonable to suspect that demographic

segments such as gender and age or level of customer experience could produce

significantly different results. Perhaps, precise area of improvement could be determined

and immediate managerial plans and actions might be effectively tailored to these customer

segments.

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Corresponding author

Norzaidi Mohd Daud can be contacted at: zaidiuitm2000@yahoo.com

VOL. 11 NO. 5 2010 jBUSINESS STRATEGY SERIESj PAGE 285

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