application of improved cfi model on attributes of ...

APPLICATION OF IMPROVED CFI MODEL ON ATTRIBUTES OF CUSTOMER SATISFACTION Alemu Moges Belay, University of Vaasa, Finland [email protected] Josu Takala, University of Vaasa, Finland [email protected] ABSTRACT The purpose of the study is to develop and apply improved model of Critical Factor Index (CCFI) on customer satisfaction in one of service provider in Finland. CCFI method is selected because it is comprehensive, practical, and considers the standard deviation and sees also the gap between the customer expectation and experiences. After the description of the method the current pitfalls of the model will be explained by comparing the existing CFI to the newly developed CCFI (corrected critical factor index). From the result of the study, the company identified the area to be improved based on the criticality of the attributes and give insight to balance the resource allocations accordingly. The methodology is Questionnaire/survey with relevant attributes and 150 responses are gathered with required information (15 are analyzed in this paper). The main motive of the paper is that, the old model doesn’t consider the influence of number of responses whereas the new model takes in to consideration and bring some non-zero results. This helps managers to see the relative gap when one of the standard deviation (experience and/or expectation) becomes zero. Besides to that this paper introduces how control chart could help on identifying green, yellow, and red section based on the criticality of the attributes. Finally emphasized implementation index and its correlation will be presented. Keywords: Corrected Critical Factor Index; CCFI, Customer satisfaction; Control chart, Service. INTRODUCTION No matter the type of services/products you are providing, decision making is done at any stage in the process. So, strategic service management of an organisation ought to have a sagacious view about the current situation and future improvement and development possibilities. Nowadays, service engineering has become an increasingly important part field of study to contribute to global economy. Enhancing service productivity become crucial as service sector becomes more knowledge based and service based. Substantial studies show that excellent service is widely recognized as a decisive factor on different business environment (Voss et al., 2004a; Vilares and Coehlo, 2003). According to (Newman and Cowling, 1996), service sector and quality is “essential to corporate fitability pro and survival” and Rosen et al., (2003) described as “not just a corporate offering, but a competitive weapon”. Voss et al., (2002) and Johnston, (1959) indicated the complexity of service quality concept in service sector and there is no clear picture on the drivers for delivering in effective way.

Due to globalization and other factors the nature of competition in services is changing and unpredictable in some cases because the forces of deregulation and new technologies have restructured service industries in recent years. Therefore managers are always looking for the better way of doing things by implementing different approaches to tackle these challenges. For instance applying decision making indicator like CCFI can be taken as example. This added flexibility would help to ensure that the product or service delivered meets the customer’s requirements and specifications. Another tool which is helpful for decision making is CFI which finally improved to CCFI. CFI method is a t tool to indicate which attribute of a business process is critical and which is not, based on the experience and expectations of the company’s employees, customers or business partners (Ranta, JuhaMatti & Josu Takala, 2007). In order to make the appropriate decision managers has been using several decision supporting tools. That is why the CFI (Critical Factor Index) has been developed to make a decision making in comprehensive way of current performances of business processes based on expectation, experience, competitor and other attributes. This tool indicates which attribute of a business processes or activities are critical and which are not. The CFI was developed on the basis of the Gap analysis and the implementation index (IMPL). The original idea, behind these measurement tools, was to develop a fast and reliable method for management purposes to sense and respond to customer satisfaction. Even though the method helps and applied on many different cases on identifying which attributes are critical within the business process and supports the management to make decisions on identifying which attributes should be improved. Having well defined attributes, the method can be used to measure all business processes and activities. As a weakness, CFI indicator is highly influenced by the standard deviation and it doesn’t show the magnitude of the attributes if one of the standard deviation (expectation and/or experience) is zero. (Ranta, Juha-Matti & Josu Takala, 2007) also proposed in their case study about further development of the CFI in order to increase the reliability of the findings. This paper tries to avoid zero CFI and introduce a formal way of identifying three distinct areas (green yellow red). Then the method will be improved from CFI to CCFI that will avoid the non-zero deviation by integrating the sample error correction. DRIVER FOR SERVICE ATTRIBUTES Recently all type of businesses including service have become under extreme pressure to provide services to the client/customer quicker than ever before. With such an extreme pressure being applied to respond and implement services faster than ever before, companies that provide service can no longer afford or survive to perform work with limited resources or in non-reactive/responsive way to customers. For the last few decades, three factors Speed, quality, low cost is universal goals as long as there has been competition in any service and other business. There are many resource wastes in service sectors that directly affect the customers’ attitude towards service provider. Minimizing the major resource wastes of the services and reducing complains of customers’ need continuous and consistent endeavor. This is because seeing service through ones eye is not as an easy task as we expect in order to be a customer centered service organization. The big challenge in service is to identify, analyze and developing capability to understand service resource waste in systematic and organized way. Some of the wastes could be categorized in few

major areas like, over processing, transportation, motion, inventory, waiting time, defect, over-production, and information. One of the motives of this paper is to introduce the possibility of reducing resource waste linked with resource allocation based on the critical level of each attributes. In order to see and analyze in detail, these major attributes could be broken down in to several attributes and treated individually and based on the criticality it would be possible to balance the resources distribution. Having well defined attributes for each specific companies and situations, CFI would lead to right decision for optimum utilization of resources. CCFI DETERMINATION IN SERVICE GIVING COMPANY The case company is one of a strategic partner that provides mainly services and products to some extent. Its customers for long term services are from both domestic and international industries. The company is ISO certified and with a capability of peculiar products based on customers requirements and specifications. To carry out this research, questionnaires (sample table 1) with relevant

attributes are used to gather the relevant information. As a matter of fact, each services and processes have its own attributes and it is difficult to have a single standardized questionnaire. Usually this is done in two phases. On the first phase the current performance of the services is investigated, personnel interviews and discussion will be made to come to common agreement on defining attributes which are going to be set. To achieve the overall objective, proposing development needs for certain attributes, the choice for them should be in line with the company’s own strategy, vision, mission and values. In the second phase, all information that has been gathered will be analysed and critical factor index (CFI) measurement tools will be applied to determine the critical attributes that needs improvement. This will give an idea for managers to decide on balanced resource allocation in various activities of services. Table 1: Sample questionnaire.

Attribute 1 Attribute 2 ● ●

Expectations

Experiences

(1-10) [ ] [ ] [ ]

( 1-10) [ ] [ ] [ ]

[

]

[

]

Compared with competitors (Please tick your option) Worse Same better          





Direction of development (Please tick your option) Worse Same better          





Expectation = what is the expectation to the attribute Experience = what is the experience of the attribute Compared with competitors = Compare experienced value to the values of all other targets Direction of development = Direction of the experienced values of the sample during the last three years. Standard dev. of expectation * Standard deviation of experience CFI = Importance index * Gap index * Direction of development index Average of the experience - Average of the expectation Gap index = -1 10 Dir. of Devt. Better% - Dir. of Devt. Worse% Direction of Development index= -1 10

Importance index =

Average of expectation 10

In the CFI model was applied in many applications on Quality, Maintenance, production, Knowledge management and etc. But the existing model would be a good critical factor identifying tool and appropriate when one of the standard deviations or both are non-zero. In order to improve the situation the sampling error correction can be taken from the model which is developed by miller (1955) and Basharin (1959) who derived an approximation for the expectation of a sampled uncertainty, AE(Hnb), that would be good for large sample n. The new developed model critical factor index with sample error correction (CCFI) can be rewrite as follows: AE (Hnb) =Hg-

s −1 2 ln(2)n

CFI = CCFI - SEC s −1 CCFI= CFI + , 2 ln(2)n Where s is the minimum number of sample in our case 3, and n is the number of sample (responses) actually analysed. CCFI =

Standard dev. of expectation * Standard deviation of experience s −1 + Importance index * Gap index * Direction of development index 2 ln(2)n DATA ANALYSIS, RESULTS AND INTERPRETATION

The physical meaning of adding sampling error is to avoid zero CCFI. That is because with certain amount of input (resource) there must be some output in all extremes i.e. high critical or low critical. Unfortunately in our data analysis we did not get a zero STD on experiences and/or expectation. In such situation it may not be visible the significance of sampling error correction as all attributes have its own critical values. The essence of incorporating sampling error is to tackle the probability of getting zero CCFI. As we know the probability of having zero STD deviation is high as sample size goes lesser and lesser. A person collecting 100 responses and a person with 3 should not apply the same parameters and analysis. So, in this research the gap will be filled by balancing CFI by adding some value that takes the number of responses in to account. Control charts have been used for several applications and here we have used to identify the critical level or area. These help managers to clearly observe and answer which attribute, at what level and how much they are critical. Besides to that by varying different attributes level it could be possible to adjust the other attributes. For example, by reducing or increasing some values on most critical ones it would be possible to see the overall critical level change. In our case, if the company reduce the critical level of attribute 18 and 25 into 2, the lowest critical could become in to the green area. That means it could be possible to have significant influence on attribute 2, 3, 6 and 13 that they become in green area. In general out of 43 attributes, 6 each were in both extremes and the rest were in green areas.

Table 2: Results of the preliminary analysis. Average of STDEV of Average of STDEV of No. expectation expectation experiences experiences

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

9,25 9,25 9,25 9 9,5 8,25 9,75 9,75 8,75 8,75 8,5 8,5 8,25 8,5 8,5 8,5 8,75 8,75 9 9 8,75 9,25 9 9,5 8,75 8,5 8,5 9 9 9,5 9 9,5 9,25 9,25 9,25 9 9,25 9 9 8,75 8 9 9

0,5 0,5 0,5 0,82 1 0,25 0,5 0,5 0,96 0,96 1 1 0,5 1 1 0,96 0,96 0,96 0,82 0,82 1,26 0,5 0,82 0,58 0,96 1 1 0,82 0,82 0,58 0,82 0,58 0,5 0,96 0,96 1,41 0,96 0,82 0,82 1,5 1,41 1,41 0,82

7 7,5 7,5 7,75 7,25 7,25 7 7,25 7,75 7,25 7 7 7,5 8,5 7,5 8,00 7,25 8,25 7,75 7,75 7,75 7,25 6,75 7,5 7 7 7,5 6,5 7,75 7,5 7,25 7,5 8 8 7,75 7,75 7,5 7 6,75 6,75 7,25 5,75 6,25

1,25 0,58 0,58 1,26 0,96 0,96 1,83 1,71 1,5 1,26 1,15 1,15 0,58 1 1,29 1,15 0,96 1,71 1,26 0,96 0,96 0,5 0,96 1,73 2 1,41 0,58 2,38 0,96 0,58 0,5 1 1,15 0,82 0,96 0,58 1 1,15 0,96 0,96 0,96 1,26 0,96

Gap index 1,2250 1,1750 1,1750 1,1250 1,2250 1,1000 1,2750 1,2500 1,1000 1,1500 1,1500 1,1500 1,0750 1,0000 1,1000 1,0500 1,1500 1,0500 1,1250 1,1250 1,1000 1,2000 1,2250 1,2000 1,1750 1,1500 1,1000 1,2500 1,1250 1,2000 1,1750 1,2000 1,1250 1,1250 1,1500 1,1250 1,1750 1,2000 1,2250 1,2000 1,0750 1,3250 1,2750

Direction of development Importance index index COMP 1,0000 1,0000 1,0000 0,5000 0,5000 1,0000 1,0000 1,0000 1,0000 1,5000 1,0000 1,0000 1,0000 0,5000 1,0000 1,0000 1,0000 0,5000 0,5000 1,0000 1,0000 1,5000 1,5000 0,5000 0,5000 1,0000 1,0000 1,0000 1,0000 1,5000 0,5000 1,0000 0,5000 1,0000 1,0000 0,5000 1,0000 1,5000 1,5000 1,0000 1,0000 2,0000 2,0000

0,9250 0,9250 0,9250 0,9000 0,9500 0,8250 0,9750 0,9750 0,8750 0,8750 0,8500 0,8500 0,8250 0,8500 0,8500 0,8500 0,8750 0,8750 0,9000 0,9000 0,8750 0,9250 0,9000 0,9500 0,8750 0,8500 0,8500 0,9000 0,9000 0,9500 0,9000 0,9500 0,9250 0,9250 0,9250 0,9000 0,9250 0,9000 0,9000 0,8750 0,8000 0,9000 0,9000

0,7 0,75 0,75 0,775 0,725 0,725 0,7 0,725 0,775 0,725 0,7 0,7 0,75 0,85 0,75 0,8 0,725 0,825 0,775 0,775 0,775 0,725 0,675 0,75 0,7 0,7 0,75 0,65 0,775 0,75 0,725 0,75 0,8 0,8 0,775 0,775 0,75 0,7 0,675 0,675 0,725 0,575 0,625

IMPL

1,785714 0,773333 0,773333 1,625806 1,324138 1,324138 2,614286 2,358621 1,935484 1,737931 1,642857 1,642857 0,773333 1,176471 1,72 1,4375 1,324138 2,072727 1,625806 1,23871 1,23871 0,689655 1,422222 2,306667 2,857143 2,014286 0,773333 3,661538 1,23871 0,773333 0,689655 1,333333 1,4375 1,025 1,23871 0,748387 1,333333 1,642857 1,422222 1,422222 1,324138 2,191304 1,536

Figure 2 preliminary result of CCFI (with sample correction)

Though CFI and CCFI are highly dependent on STD, they are good critical attribute indicator in comprehensive way. The Managers can allocate resources based on the level or emphasis of criticality (balance with existing resources, new resources). Addition of sample error correction on CFI will give some insight for managers and avoid zero CCFI (If one or both of STD dev is/are zero). The negative correlation from IMPL vs. COMP indicates that the lesser the priority given, the higher critical the attribute and it needs amendments or remedial action to balance the resources. This was applied in communication and implementation of strategies on culturally influenced company (Takala, et.al 2006). VISUALIZING CRITICALITY AND INTERPRETATION OF THE RESULT Analysis has been made by using critical factor index that is utilizing expectations, experiences, competitor’s performance, gap index, deviations and directions of development. This result is from modified model of CFI that takes in to account the sampling error correction and control limits to clearly see and make balance between high critical factors and lower ones. The managers can get a clue whether they need additional resources or can distribute available fairly from high critical to lower critical (from yellow to red) so as to make green all factors. For instance, assuming we have similar units for different factors e.g. amount of Euros which is allocated for each factor. n

Higher critical (HC) = ∑ Ui − UC Li i =1

n

Low critical (LC) = ∑ Li − LC Li i =1

Where HC= Higher critical LC= Lower critical Ui =Individual factors which are upper than UCL UCL=Upper control limit from the control chart

Li = Individual factors which are lower than LCL LCL= lower critical from the control chart n = number of factors above or lower than UCL or LCL i = individual factors Over emphasized: If HC > LC, the company can adjust or balance the resource distribution with the existing facilities/resources under normal situation. Less emphasized: If HC < LC, the company may face difficulties to adjust or balance the resource distribution with the existing facilities/resources under normal situation. Fairly emphasized: If HC = LC, at this breakeven point the company may not need balancing or adjusting resources but it need a careful follow up because if one of the attributes/factors disturbed the rest could be affected in both extremes. In our case, Higher critical (HC) = (2,2212-2,155331)+(2,5332-2,155331)+(3,7539-2,155331) +(2,2212-2,155331)+ (3,9153-2,155331) = 14,6448-10,776655=3,868145 Similarly, Lower critical (LC) = (0,585231-0,4471)+(0,585231-0,4471)+(0,5852310,4448)+(0,585231-0,5073)+(0,585231-0,3305)+(0,585231-0,377)+(0,585231-0,5233) = 4,096617-3,0771=1,019517 To sum up, HC>LC so, the company can make resource adjustment/balance by the magnitude of 3,868145-1,019517= 2,848628 gap CONCLUSION Several writers indicate service is challenging because of its characteristics like intangibility (cannot be seen and or tasted), inseparability (produced and consumed at the same time), heterogeneity (produced and consumed simultaneously) and perishability (cannot be stored). That is why various technological innovations and its supporting tools and techniques are required on decision making processes. The development of service engineering and service science shows that choosing the clear development targets and creating appropriate indicators on service leads to success. While carrying out service activity assessment using CFI, there should not be too many attributes because the measurement process takes too much time and the target of the development may not come in to existence. In this paper CCFI is used and 6 attributes (red) are found the critical ones (with shortage of resources or under emphasized). On the other hand, 6 attributes were found to be in yellow region that implies the attributes are more than required (over emphasized). So, the manager can have an idea to balance the resources of these attributes by fairly distributing to make all factors in green. The merit of fast, comprehensive and reliable method to identify the critical attributes in order to make managerial decisions at low cost is crucial phenomena and will most probably lead to a further increase of interest of CCFI. However, it still requires further improvement and development and should be tested in various case studies as well. In this paper there are three deliverables, the first one is recognizing the influence of responses’ number and avoid zero CFI by adding sample correction error. Secondly, the applications of control chart to determine the three distinct

areas (green, yellow, and red). The third one is application of IMPL vs. COMP that is analysis of emphasized implementation index and evaluation of importance for each attributes. REFERENCES 1. Johnston, R. (1995), “The determinants of service quality: satis fiers and diss atisfiers”, International Journal of Service Industry Management, 6(5), pp. 53-71. 2. J. Takala, H. Sivusuo, J. Leskinen, J. Hirvelä (2006).’’ how to communicate and implement strategies in a strong organization culture? TEHNIČKI VJESNIK 13,(1,2)49-55 3. Newman, K. and Cowling, A. (1996), “Service quality in retail banking: the experience of two British clearing banks”, International Journal of Bank Marketing, 14(6), pp. 3-11. 4. Ranta, Juha-Matti & Josu Takala (2007). ´´A holistic method for finding out critical features of industry maintenance services’’ International Journal of Services and Standards, 3:3, 312 – 325. 5. Rosen, L.D., Karwan, K.R. and Scribner, L.L. (2003), “Service quality measurement and the disconfirmation model: taking care in interpretation”, Total Quality Management, 14(1), pp. 3-14. 6. Vilares, M.J. and Coehlo, P.S. (2003), “The employee-customer satisfaction chain in the ESCI model”, European Journal of Marketing, 37(11/12), pp. 1703-22. 7. Voss, C., Tsikriktsis, N. and Frohlich, M. (2002), “Case research in operations management”, International Journal of Operations & Production Management, Vol. 22 No. 2, pp. 195-219. 8. Voss, C., Roth, A.V., Rosenzweig, E.D., Blackmon, K. and Chase, R.B. (2004a), “A tale of two countries’ conservatism, service quality, and feedback on customer satisfaction”, Journal of Service Research, 6(3), pp. 212-23.