how subjective clustering aids affinity diagram in...

How Subjective Clustering aids Affinity Diagram in grouping Customer

needs in consumer products

SANDHEEP KUMAR VURUKKARA BOOPAL

Master of Science Thesis Stockholm, Sweden 2016

How Subjective Clustering aids Affinity Diagram in grouping Customer needs in consumer products

industry

Sandheep Kumar Vurukkara Boopal

Master of Science Thesis MMK 2016 MF228x KTH Industrial Engineering and Management

Machine Design SE-100 44 STOCKHOLM

Examensarbete MMK 2016 MF 228x

How Subjective Clustering aids Affinity Diagram in grouping customer needs in consumer products


Godkänt

Examinator

Anders Berglund

Handledare

Susanne Nilsson Uppdragsgivare

Creaffective GmbH Kontaktperson

Florian Rustler

Sammanfattning Insamling och analys av kundernas behov är viktiga delar i produktutvecklings- och innovationsprocesser. Dessa kundbehov måste vara i en form som lätt kan kommuniceras och förstås särskilt avproduktutvecklare i ett företag. Affinity Diagram är ett vanligt använt verktyg för att strukturera kundbehov. På grund av att metoden bygger på gruppdiskussioner, finns det risk för att enskilda individers åsikter inte tas tillvara. En metod som tar hänsyn till de individuella bedömningarna är Subjective clustering, vilkenhar utvecklats för att stödja Affinity Diagram.

Tidigare forskare har tillämpat båda dessa metoder i ett vetenskapligt och industriellt sammanhang och har funnit att det finns 92,5% av koppling mellan Affinity Diagram och Subjective clustering och drog slutsatsen att Subjective clustering stödjer Affinity Diagram. Det saknas forskning om huruvida Subjective clustering stödjer Affinity Diagram för konsumentprodukter. För att undersöka detta har en fallstudie på konsumentprodukter i ett produktutvecklingsprojekt i Creaffective GmbH genomförts.

Studien har undersökt hur kundbehov stuktureras både från produktutvecklarensoch från kundens perspektiv. Affinity Diagram och Subjective clustering utfördes i var och en av grupperna och jämfördes. Det konstaterades att det fanns 70% av association mellan Affinity Diagram och Subjective clustering i produktutvecklingsgruppenoch 58% av association mellan Affinity Diagram och Subjective clustering med kunderna som fokusgrupp. Från analysen framgår att Affinity Diagram ensam utgör är lämplig metod för att strukturera kundernas behov för konsumentprodukter. Bakomliggande skäl till detta diskuteras i rapporten samt förslag på fortsatta studier.

Master of Science Thesis MMK 2016 MF228x

How Subjective Clustering aids Affinity Diagram in grouping customer needs in consumer products


Approved

Examiner

Anders Berglund Supervisor

Susanne Nilsson Commissioner

Creaffective GmbH Contact person

Florian Rustler

Abstract Collection and analysis of customer needs are important parts of product development and innovation processes. These customer needs must be in a form that can be easily communicated and easily understood especially by the R&D personnel. Affinity Diagram is one such tool to structure these data. Because of the nature of the Affinity diagram method, it is prone to biases. An alternative method that exists is Subjective clustering. It has been developed as an aid to support affinity diagram.

Previous researcher has applied both these methods in a scientific and industrial context and has found that there is 92.5% of association between affinity diagram and subjective clustering and concluded that Subjective clustering aids affinity diagram. However there has been no research on whether subjective clustering aids affinity diagram in consumer products context. Taking this as a research gap, this thesis is performed, taking the Innovation project at Creaffective GmbH, as a case study.

The research is conducted from both Product Development Team’s and Customer’s perspective. Affinity Diagram and Subjective clustering were separately performed with each of the group and then compared. It was found that there was 70% of association between Affinity Diagram and Subjective clustering by Product Development Team and 58% of association between Affinity Diagram and Subjective clustering by customers. It was concluded from the analysis that Affinity Diagram is the only suitable method to structure the customer needs for consumer products. Underlying reasons to the result is discussed in the thesis and further studied suggested,

FOREWORD

This Master thesis concludes my Master study of Integrated Product Design (Product Innovation Management) at KTH Royal Institute of Technology. Over this process of this Master thesis, there has been a number of people who have been a very important part of this Master thesis, and the reason as to why the study has been completed and stands in its place.

First and foremost, I would like to heartily thank Mr. Florian Rustler, founder of Creaffective GmbH, Munich for proposing the Innovation project that served as the right platform to conduct this study and also shepherding the project, guiding me at the right and crucial stages and providing the right contacts. I would also acknowledge the participation of Isabela Plambeck and Daniel Barth in the study and also assisting in the various stages of the project.

The study would not have been possible without the participation of Mr. Eashwara Krishnan, Mrs. Subashini Eashwar, Mrs. Supriya Satish, Mr Sarma, Mr. Velpari and Mrs. Bhuvaneswari who are trained trainers at Junior Chamber International (JCI), and also JCI without which I would not have known them. Their participation is definitely invaluable.

I must definitely acknowledge the participation of several people who have been the source for various inputs for the study. To start with, Mr. Subramanian, Past National President of JCI India; Mr. Mohammed Nassar, Founder E2E Excite; Baalachandran Gopinath, HRD Trainer, India; Dhananjaya Hettiararchi, HR Trainer, Sri Lanka and word champion in public speaking 2014; Ankur Grover, Founder, Tinker Lab, India; Mr. Harsha, Freelance Trainer, India; Mr. Sivakumar Palaniappan, Career Coach, Masteringmind Academy, India; Mr. Jayaraman Umashankar, founder, Karna communication academy, India; Mr. Jim Clark, Design Thinking trainer, Innogreat, Taiwan; Mr. Chendil Kumar, CK consultants, India; Mr. Randy J Harvey, Keynote speaker, Bassinger & Harvey, US; Dali Han, Bosch China; Ms. Sandhya Sridhar, Mercedes Benz, India; Mr. Naveen Ramkumar, Robert Bosch, India; Mr. Satish Ramachandran, SME Head, Vodafone, India; Mr. Arjun Murali, General Electricals, India; Mr. Krishna Devarajulu, US Bank, US; Mr. Nithin Joseph, Manager, Taj Vivanta, India; Mrs. Grace Meng, Jingling Hotel Nanjing, China.

Finally, I would like to express my sincere gratitude to both my supervisors Mats Magnusson and Susanne Nilsson who especially guided me during the thick and thin. My gratitude to my alma mater, KTH Royal Institute of Technology for providing me the required knowledge and making me who I am to make this happen.


Stockholm, December 2016

NOMENCLATURE

Abbreviations

AD Affinity Diagram

B2B Business to Business

B2C Business to Consumer

HC Hierarchical Clustering

JCI Junior Chamber International, an International organisation

JTBD Jobs To Be Done

KJ Method Also called as Affinity Diagram, founded by Jiro Kawakita

OEM Original Equipment Manufacturer

PDT Product Development Team

PET Poly Ethylene Terephthalate

QFD Quality Function Deployment

SC Subjective Clustering

TELCO Telecommunication

TMR Traditional Marketing Research

VOC Voice of the customer

TABLE OF CONTENTS

FOREWORD ........................................................................................................................... 7

NOMENCLATURE ................................................................................................................... 9

TABLE OF CONTENTS .............................................................................................................. 2

1 INTRODUCTION ................................................................................................................... 4 1.1 Background ............................................................................................................................. 4

1.1.1 Previous Work..............................................................................................................................4 1.2 Purpose ................................................................................................................................... 5 1.3 Method ................................................................................................................................... 6 1.4 Delimitations ........................................................................................................................... 7

2 FRAME OF REFERENCE ........................................................................................................ 8 2.1 What are Customer needs and why should it be structured? ..................................................... 8 2.2 Consumer products – What is it different when it comes to anlaysing customer needs .............. 9 2.3 Affinity Diagram method........................................................................................................ 10 2.4 Subjective clustering method ................................................................................................. 11 2.5 How Subjective Clustering (SC) helps in supporting affinity diagram? ........... Error! Bookmark not defined. 2.6 Clustering .............................................................................................................................. 12

2.6.1 Hierarchical Clustering .............................................................................................................. 12

3 RESEARCH STUDY DESIGN ................................................................................................. 15 3.1 Defining the aim of the project ............................................................................................... 16 3.2 Brainstorming and filtering questions to get relevant answers ................................................ 16 3.3 Customer Interviews .............................................................................................................. 16 3.4 Cleaning the data and deducing the list of customer needs ..................................................... 17 3.5 Subjective Clustering and Affinity Diagram ............................................................................. 18 3.6 Comparison and Analysis ....................................................................................................... 19

3.6.1 Comparison of SC and AD of respective groups ....................................................................... 19 3.6.2 Comparison of AD results of customers and PDT; Comparison of SC results of customers and PDT ..................................................................................................................................................... 19

4 RESULTS ............................................................................................................................ 21 4.1 List of customer needs ........................................................................................................... 21 4.2 Comparison of grouped needs by AD and SC by Product Development Team ........................... 21 4.3 Comparison of grouped needs by AD and SC by Customers ..................................................... 23 4.4 Comparison of grouped needs of both AD and SC by Product Development Team and Customers ................................................................................................................................... 25 4.5 Importance of needs .............................................................................................................. 27

5 ANALYSIS AND DISCUSSION .............................................................................................. 29 5.1 Discussion ............................................................................................................................. 29

5.1.1 Comparison of grouped needs by AD & SC by PDT .................................................................. 30 5.1.2 Comparison of grouped needs by AD & SC by Customers ....................................................... 30 5.1.3 Comparison of grouped needs by AD by PDT and customers .................................................. 31 5.1.4 Comparison of grouped needs by SC by PDT and customers ................................................... 31

6 CONCLUSION .................................................................................................................... 34

7 RECOMMENDATIONS AND FUTURE WORK ........................................................................ 35

8 REFERENCES ...................................................................................................................... 36

APPENDIX A : BRAINSTORMED QUESTIONS .......................................................................... 38

APPENDIX B: INTERVIEW GUIDE WITH SAMPLE ANSWER ...................................................... 43

APPENDIX C : List of Customer needs ................................................................................... 47

APPENDIX D : AD AND sC RESULTS OF PDT and customers .................................................... 49

APPENDIX E: R PROGRAM PACKAGE DESCRIPTION ............................................................... 52

APPENDIX F: R PROGRAM for creation of dendrogram ......................................................... 57

APPENDIX G: r program code for cluster analysis .................................................................. 59

APPENDIX H: INDIVIDUAL GROUPING OF CUSTOMER NEEDS BY PDT .................................... 66

APPENDIX I: INDIVIDUAL GROUPING OF CUSTOMER NEEDS BY CUSTOMERS ........................ 69

1 INTRODUCTION This chapter describes the background, the purpose, the limitations and the method(s) used in the presented project.

1.1 Background Identifying the right customer needs is a very crucial task for any company involved in New Product Development. Research shows that companies spending more resources and giving more importance to this activity in the initial phase of innovation, performs better in terms of many attributes such as customer satisfaction, market share and profitability (Cooper & Kleinschmidt, 1993; Rothwell, 1992). Further, Beyer & Holtzblatt (1999) showed that the best product design results when the product designers are involved in collecting and interpreting customer data and appreciate what real needs of the customers are. However, this is not an easy task. A number of methods have been designed and proposed for this task in the literature, by various academics and researchers. Structuring the data collected is an important phase of the need analysis phase, as this is the phase that gives the direction for the product developers to create the product (Griffin & Hauser, 1993). Affinity Diagram (AD) is a popular tool in organizing the chunks of data to move from an abstract level to concrete understanding of the data, as they organize ideas into categories based on their underlying similarity (Shafer, Smith, & Linder, 2005).

1.1.1 Previous Work Many researchers agree that AD is prone to bias. Since it involves the discussion of many people (usually around 4-5) and each adds their own point of view, usually there are situations where-in some of the members dominate others and it is usually agreed, although there would not be any real agreement. For example, Takai & Ishii (2010), (p.102). say that “few participants’ opinion may skew the results”. To overcome this drawback, , it was demonstrated that the use of Subjective Clustering (SC) may aid to support affinity diagram(Takai & Ishii, 2010). Subjective Clustering (SC) is an alternative grouping method which is based on statistical analysis of individual’s grouping.

Professor Shun Takai (2010), has in his research used an example of customer needs of a next generation linear collider (that collides electron and positron beams with energies significantly higher than the existing linear collider to discover new particle physics phenomena) by interviewing scientists from labs. His research showed that Affinity diagram and subjective clustering are 92.5% similar i.e., results from comparing the needs by individually grouping them and the needs by collective grouping were found to match to an extent of 92.5%. It is assumed that this high level of match was possible and hence Subjective Clustering complement Affinity Diagram, because of the reason that the context of the study was in a scientific (or B2B) scenario, where not many customers were involved and hence not many diverse needs were present.

1.2 Purpose The existing literature of comparison of Affinity Diagrams and Subjective clustering in grouping customer needs is very limited. In addition, the research performed was conducted in a highly scientific context. The result may hence not be applicable in consumer products context as, the industry and target customer genre is completely different. For e.g., it may be dubious to use the results of the previous research in grouping customer needs in consumer products such as mobile phones or daily durables. Hence, there is a research gap, which is to verify if SC supports AD in grouping customer needs in consumer products industry. Or in other words, to verify if there is a good match between the grouping results of AD and SC. Consumer products industry is specifically an interesting context to perform this study in , as this is an industry that is characterized by continuously changing demographics and consumer preferences (Renner, 2016). Hence there arises a need to find if SC aids AD in consumer products industry segment.

In addition, in the previous research, the grouping of the needs was done by the product development team. Any bias in this process, multiplies in the forthcoming stages of the product development, finally leading to products that become inappropriate for the customers (Takai & Ishii, 2010). Many researchers agree that participation of customers in the product development process adds more value and provides positive benefits (Morgan & Obal, 2016). This provides a motivation to verify the usage of SC as an aid for AD, by comparing both, Product development team’s and customer’s results.

The study presented in this thesis is being carried out in a project at Creaffective GmbH, an innovation consulting company, based in Munich, Germany. The project is an explorative attempt to find if there are opportunities to create better training aids and equipment (such as Flipcharts, White boards etc.) for innovation workshop facilitators, trainers in the South Asian markets such as India and China. It hence, provides an opportunity to understand how the tools to handle the structuring of customer need is influenced when used in a consumer product context.

Hence this thesis is aiming to answer the following research questions,

1. To what extent is Subjective Clustering aiding Affinity Diagram in grouping customer needs in the context of consumer products?

2. What could be learnt by comparing the results of Affinity Diagrams of Product Development Team and customers; Subjective clustering of Product Development Team and customers?

Fig 1 explains the skeleton of how the research questions will be answered.

Fig 1 Research Design

1.3 Method To answer the research questions, it seems logical to use the same procedure as followed by the previous researcher, so that the results are more consistent and there wouldn’t be any bias because of the methods that would be used. A mix of both qualitative and quantitative methods was used.

The first step was to collect the condensed customer needs from the relevant customer segment. This formed the qualitative part of the study research and it involved interviewing customers about their needs. This seemed to be the best method as it allowed for exploration on what the underlying needs of the customers were.

Secondly, the needs were first grouped by the members of the product development team and the target customers individually, i.e. a Subjective clustering was performed.

The third step was to group the needs as a team i.e using Affinity Diagram. So the product development team grouped the needs together and as did the target customers.

The fourth step was to compare the results of AD and SC, by the product development team and compare this results of AD and SC, to that of the target customers. This was achieved by utilizing a parameter called Goodman-Kruskal’s lambda, which is the same parameter utilized in previous research studies (Takai & Ishii, 2010)

The fifth step was to compare the AD results of the product development team and the group of target customers. Due to the nature of the way AD is carried out, the author participated in the process by taking a neutral stand and observed the Affinity Diagram process.

In the final and sixth step the SC results of the product development team and target customers was made from a quantitative perspective. Since the SC is a statistical method of grouping customer needs, the entire data (in the form of matrix, which contains information about clusters) was analyzed using standard packages in statistical software. In this case, R program was used and, “cluster.stats” was used as the cluster analysis package.

1.4 Delimitations This study is based on a single Innovation project, limiting the number of data analysed. Also, the product development team consisted of three Innovation coaches and one student (the author). The members of the team have hence large differences in their prior experience in real product development which may have influenced the result of the understanding of the customer needs.

The customer needs are collected through a traditional marketing research technique (TMR), which is speaking to customers. One drawback about this technique is the unreliable memory of the customers. The customers do not completely remember their past experiences during the interview (Price, Wrigley, & Straker, 2015).

All the interviews were done through telephone conversation because of the geographical limitation of the thesis project. Hence there were no opportunities to observe other important drivers of the quality of the research such as participant behavior, body language and their facial expressions.

Only because of the later developed clarity of the thesis title and also since the project with Creaffective had started much earlier, the product development team had encountered the affinity diagram phase, even before they were individually grouped. However, after some sufficient time period, the subjective clustering was performed first and then again a collective grouping was performed through a telephone conference.

2 FRAME OF REFERENCE The reference frame is a summary of the existing knowledge and former performed research on the subject. This chapter presents the theoretical reference frame that is necessary for the performed research, design or product development.

For a clear understanding, some of the basics are explained in this chapter, such as what customer needs are and what is unique with the consumer segment, followed by literature related to the importance of structuring customer needs. Literature concerning the specific tools in use for the study, Affinity diagram and Subjective clustering is presented. Since the Subjective Clustering deals with clustering aspects, some light is thrown on these aspects, with special reference to Hierarchical Clustering.

2.1 What are Customer needs and why should it be structured?

There has always been a debate on what the meaning of customer needs are and what exactly defines a customer need statement. In simple words, customer buys various products and services to “get their job done”. This leads to the concept of “Jobs to be done” (JTBD), which can be associated with customer needs. JTBD are not products or services or a specific solution but a higher purpose as to why a customer shall buy a particular product or service (Silverstein, Samuel, & DeCarlo, 2013). When looking at the markets through the “jobs to be done” lens, a customer need statement is best defined as what the customer measures as the success and value when getting a job done (Ulwick, 2016). The better the customer needs are understood and documented, the better the product developers will be able to make informed decisions in their design work (Patnaik, 2009).

Any JTBD has a main job to be done and a related job to be done (see Figure 2. Each of the type has a functional aspect and an emotional aspect attached, and every emotional aspect has a personal dimension (as to how the customer feels about the product or service) and a social dimension (as how the customer thinks he or she is perceived by the people around them) (Silverstein et al., 2013).

Fig 2. Jobs to be done Breakdown (Silverstein et al., 2013)

Experts agree that customer’s need is the core of the product and value offering to customers. Satisfying them is the primary task of the executives (Fan & Cheng, 2006). Several studies have been conducted in proving the importance of understanding the customer needs in the early phase of new product development and innovation. Voice of the customer (VOC) is a term used in business and Information Technology to describe the in-depth process of capturing customer’s expectations, preferences and aversions. One famous study done by Cooper(2001), suggests that capturing the VOC, doubles the success rate of the new products and have more than 70% higher market share compared to the ones who have a poor approach.

Importance of customer needs can also be seen in the area of Quality Function Deployment (QFD). Griffin & Hauser (1993) mention that there are three steps in QFD’s customer input: 1. Identifying customer needs 2. Structuring customer needs 3. Setting priorities for customer needs. The overall goal of QFD is to help the product development team understand how to satisfy the customer. It was noted that the usage of QFD results in a 60% reduction in design costs and 40% reduction in design time (Hauser & Clausing, 1988).

The collected customer needs and information is usually unstructured and contains more than what is necessary. So it becomes important to reduce the information to a manageable amount of data. The data from one customer gives depth regarding the subject, however only analyzing a group of customer’s needs, shows a pattern of the bigger picture (Vanalli & Cziulik, 2003). Need assessment also refers to structuring and analysing customer needs for the reason of developing new products and services. The information on customer needs, to be useful, must be in a form that can be easily communicated especially to the persons in R&D who need it. (Adams, Day, & Dougherty, 1998; Gupta & Wilemon, 1988). The grouping and structuring of collected needs can be done by three methods, viz., Affinity Diagram, Relationship diagram and tree diagram (Shillito, 2000; Burchill & Brodie, 1997; Mazur, 1997).

2.2 Consumer products – What is different when it comes to analysing customer needs

Two very basic types in marketing is B2B (Business to Business) (industrial market) and B2C (Business to Consumer) (consumer market). Often the methods and tools that are used in consumer markets are not being used in industrial markets as the nature of the needs and wants of the end customer differ (Elfvengren, Kärkkäinen, Torkkeli, & Tuominen, 2004). According to (Kotler, Saliba, Turner, & Wrenn, 1995), industrial markets is made up of individuals and organizations that acquire goods and services for use in the production of other products or services that are sold, rented or supplied to other individuals and organizations. Consumer products differ in many ways compared to industrial customers. For consumer products customer-product developer cooperation is more iterative, has back and forth communications rather than a direct and more intense cooperation as the one between industrial customer-developer cooperation (Elfvengren et al., 2004). For example, let’s consider a company that has plastic injection molding facility that wants to develop PET bottles. They have two options such as developing PET bottles in B2C (water bottles for

consumers) segment and B2B segment (for example glucose bottles for drips in hospitals). If it is the case of B2C, they might have to test their prototypes with many customers with respect to demography, age or aesthetic aspects. For e.g., water bottles designed for children would be different from that of the adults and would again be different from that of old people. The company might have to create prototypes after each feedback loop to test them again. However in case of B2B, the company might just have to follow certain standards and norms (may be in terms of material, sizes etc), a fixed blueprint and meet them. In this case, the customer shall be a glucose bottle OEM.

According to Day, Schoemaker, & Gunther (2004), the emerging technologies and competition is forcing to reduce the product development time than it was earlier in consumer product industry. In consumer product industry, the developers cannot afford to lose contact with the customers, and the customers always play the central part of the product development process.

Fig 3. Two paradigms in industrial products and consumer products (Von Hippel, 1979)

Von Hippel, (1979), who is considered to be the father of user centered innovation, places a lot of importance in the user being the main catalyst in the innovation and product development in the consumer product segment. These days, the majority of the companies have realized the importance of having a customer in the product development team, and developing new products along with them. Fig 3 shows the two paradigm differences between consumer and industrial products. In consumer products, the customer has a major say on the idea screening, selection and analysis.

Provided these differences and contrasting features between new product development processes in consumer products and industrial products, it is a clear argument that the research work done by (Takai & Ishii, 2010) cannot be directly extended to concern also consumer products.

2.3 Affinity Diagram method In section 2.1, customer needs were discussed along with their importance and why they must be structured.

Affinity Diagram (AD) is a very popular method used by product development teams in finding the representative needs of the customers(Takai & Ishii, 2010;(King, 1987).

It was developed by Kawakita Jiro, and is also referred as the KJ method. It was developed to group data by brainstorming or verbal analysis from surveys. The

various steps involved in AD as adapted from ( Awasthi & Chauhan, 2012; Burge, 2011) is as follows:

1. Make the problem clear and transparent to all the team members

2. Each team member should write one idea per post-it.

3. These post-its must be placed in a way, which should be visible to all the team members.

4. Each team member comes to a consensus for grouping each idea by brainstorming if the chosen need (idea) matches with any of the other ideas. Once everyone agrees, the grouping process is stopped.

5. A header title is provided to each group and a final structured affinity diagram is created.

AD is a very useful tool in many applications. It is a consensus based approach. As helpful as it may sound, there are a few down sides to the method. Situations sometimes arise when there will be few group members who are dominant and hence subsiding the contributions of the rest of the group members. The way AD is designed does not accommodate a solution to overcome this bias. So there is clearly a need of a method where everyone contributes equally (Takai & Ishii, 2010).

2.4 Subjective clustering method Subjective clustering is one solution to this problem. It is a statistical tool that ensures that all team member’s knowledge is equally considered. ((Griffin & Hauser, 1993; Takai & Ishii, 2010). It is based on the statistical analysis of the grouped results of the individuals of the team. With reference to Fig 4, the various steps involved in Subjective clustering as adapted from (Green et al., 1969) is listed below:

1. Each individual group the needs based on similarity

2. A similarity matrix is constructed based on the individual’s groupings. In a similarity matrix, 1 is assigned to an mth row and nth column when ideas m and n are grouped together. Otherwise 0 is assigned to all the positions. The diagonal elements of the similarity matrix are always 1 because each customer need is grouped with itself.

3. A co-occurrence matrix is constructed by adding all the similarity matrices. An mth row and nth column in a co-occurrence matrix tells how many individuals have grouped the m and n ideas together. So the larger the number, it means that more are the people who have grouped those elements together. The entire diagonal element in a co-occurrence matrix is the same and it tells the total number of team members.

4. A Dendrogram is constructed using a Hierarchical Clustering procedure (HC) Johnson (1967). In Dendrogram, the more similar needs are grouped at the lower level and the less similar ones are grouped at the higher level.

5. The final step in SC method is to cut the Dendrogram at a certain height, which results in a certain number of clusters.

Fig 4. Subjective Clustering (Takai & Ishii, 2010)

2.6 Clustering Clustering is the backbone of SC. Hence some background to understand what it means, what kinds of clustering methods are available and which method is used in this study is discussed in this section.

Cluster analysis or clustering as it is called is the task of statistically grouping a set of objects in such a way that the members of one group are more similar to each other than the members of the other groups. There are a number of clustering algorithms available such as K-means, Hierarchical clustering, Fuzzy C-means and mixture of Gaussians. Hierarchical clustering is used in this particular thesis . The reason is that in other methods such K-means or Fuzzy C-means, the number of clusters must be mentioned in the beginning, which basically takes away the freedom of analyzing the dendrogram (tree structure) and manually finding the optimal number of clusters. Hierarchical clustering is one method which gives these benefits and completes the meaning of using a dendrogram. 2.6.1 Hierarchical Clustering Hierarchical clustering technique is used for partitioning objects into optimally homogenous groups on the basis of empirical measures of similarity among those objects (Johnson, 1967). Hierarchical clustering produce nested sequence of clusters with a single all-inclusive cluster at the top and single point clusters at the bottom. (Karypis, Han, & Kumar, 1999). Hierarchical clustering works in such a way that each data point is considered in its own singleton group and repeated iteratively so that two closest groups merge together, until all the data are merged into a single cluster.

In general, all the clustering algorithms fall under two types of clustering namely, agglomerative clustering and divisive clustering. In agglomerative clustering, each observation is assigned to its own cluster. Then the similarity (or distance) is computed between each of the clusters and the two most similar clusters are joined. These steps are repeated until one single cluster is left. However the divisive method is the opposite of agglomerative clustering where all the data points are associated with one single cluster and then it is partitioned to two least similar clusters. These steps are repeated until there is one cluster for each observation. Fig 5 shows the pictorial representation of the two types of hierarchical clustering.

Fig 5. Agglomerative and Divisive hierarchical clustering (Sayad, 2016)

The clustering algorithm works based on the distance function, characterized by the proximity or distance matrix. There are various types depending on how the distance is measured between two points. Some of them are Single linkage, complete linkage and average linkage, referring to fig 6. In single linkage, the distance between two points are the shortest distance between two points in the cluster. In complete linkage, the distance between two clusters is the longest distance between two points in each cluster. In average linkage, distance between two clusters is the average of distance between each point in a cluster to every other point in the other cluster. For the purpose of this thesis, complete linkage method is chosen for the reason that, the clusters formed shall be more distinct and be more useful in comparison than the other methods (Rashid, 2012).

Fig 6 Single Linkage, Complete Linkage and Average Linkage

3 RESEARCH STUDY DESIGN In this chapter the working process is described. A structured process is often called a method and its purpose is to help the researcher/developer/designer to reach the goals for the project. This thesis followed a chronological process as displayed in Fig 7.

Fig 7. Methods Flow chart

3.1 The innovation project studied The study is being carried out in a project at Creaffective GmbH, an innovation consulting company, based in Munich, Germany. The project is an explorative attempt to find if there are opportunities to create better training aids and equipment (such as Flipcharts, White boards etc.) for innovation workshop facilitators, trainers in the South Asian markets such as India and China. Although this project is more extensive and goes to the extent of developing ideas and realizing them, this master thesis makes use of the data collected in the first phase in the project.

There are five core team members involved in the product development. Three being innovation coaches and facilitators and one Industrial Designer and one being a student of KTH Royal Institute of Technology (the author). Four of these, excluding the Industrial Designer were involved in the research project. These four are involved in the initial grouping of needs (AD) and later individually (SC). The author also performed the customer need collection.

3.2 Brainstorming and filtering questions to get relevant answers

Griffin & Hauser (1993) recommends based on their findings that for identifying the needs in consumer products, one-on-one interviews are more cost effective than focus groups. They suggest that 20 to 30 interviews (customers) are necessary to get 90-95% of the customer needs and multiple analysts or team members should read and interpret the raw transcripts. These suggestions were followed in the case study of this thesis. The Product Development team at Creaffective GmbH brainstormed a total of 78 questions to ask the various customer profiles. The team later came to a consensus to ask the most relevant questions (see the sample interview guide and the response in the Appendix). Shortly describing, the questions contain categories such as problems the trainers face and what they do on such events. On an average each respondent were asked around 20 questions and the interview was held for around 30 minutes. Notes were taken while simultaneously interviewing the respondents.

3.3 Customer Interviews For collecting the needs, the project started out by interviewing trainers and workshop facilitators, as they are the main beneficiaries of the new products. However, in the process various stakeholders were interviewed such as Training program coordinator, planners, Hotel banquet managers and even participants. All the trainers that were interviewed were experienced trainers with experience ranging from 10-30 years. These trainers include Innovation and creativity experts and coaches, Leadership, Strategy and change management trainers, Career experts and soft skills trainers. All the trainers interviewed were directly involved in delivering training programs to South Asian region such as India and China. In total, in the first phase of the need collection, there were a total of 28 participants interviewed. 15 of them being trainers, 5 training participants, 4 hotel managers and 4 training planners and coordinators. Most of the interviews happened through audio calls while very few of them were able to make a video call. In such a situation, it was easier to understand what they meant. For e.g., in Fig 8, one of the respondents was able to demonstrate in live as to

what he meant, the problems he faced and how usually he fixes them with temporary solutions.

Fig 8. One interviewee demonstrating the issues he has during a training program

3.4 Cleaning the data and deducing the list of customer needs

The product development team (Four people) at Creaffective GmbH analyzed the customer needs. Firstly, all the interview dialogues, were categorized according to the target profile and were printed out and stuck on boards. Each member of the team was given a post-it note and a pen. Everyone read through all the interview dialogues in a time interval of two hours. As and when every team member came across any insight, it was written down and stuck on the wall. Refer Fig 9. The process was stopped when everyone had gone through the entire conversation and no more insights were generated. Close to 200 insights were generated. Everyone got the opportunity to quickly brief the rest of the insights of the team, as to why they chose the insight and then grouped the needs when they came across the next similar insight. The repeated ones were removed. This process took one day to complete.

Fig 9. All the collected needs are laid on an open space to get a first-hand look

3.5 Subjective Clustering and Affinity Diagram “Customers” in this thesis means the end customers who are likely to use the new products that are expected outcomes from the project at Creaffective GmbH. For the study, a different set of customers from whom the customer needs were collected, but with the same customer profile is being used, for the simple reason being, to avoid the bias of grouping their own needs. The customers for this part of the study are chosen from the organization, Junior Chamber International India, an organization dedicated to self-improvement and training. A total of six participants were considered under the category of customers. All the participants are national level graduates from the organization, with about five to ten years of experience in training. To get more buy-in from the participants, an amazon gift card of worth $10 was provided to each of the participant.

Customers were first asked to individually group the customer needs. A similarity matrix was constructed, followed by co-occurrence matrix (referring to section 2.6). By performing hierarchical clustering, and through generation of dendrogram, the needs are grouped (SC). Through a TELCO (Tele-Communication), all the six members grouped the needs together by following the AD principle and this process was performed in about an hour.

In the similar way, all the members of the PDT were asked to send their individual responses to the author, by grouping those needs which they thought would be similar to each other. Following the construction of similarity and co-occurrence matrices, through a TELCO, AD procedure was carried out to group the needs as a team.

Fig 10. Final grouped needs

3.6 Comparison and Analysis 3.6.1 Comparison of SC and AD of respective groups

The first research question relates to the supporting role of SC in consumer products. The answer shall be found in the comparison of SC and AD by both customer and PDT groups separately. So Goodman and Kruskal’s lambda is being used for this purpose. The motivation to use this parameter is that, this is the same parameter that was used in the previous research, and hence there will be compatibility in comparing the previous results and the new results. Goodman-Kruskal lambda is used to measure association of the cross tabulation of nominal level variables.

λ = (S-R)/(N-R)

where,

λ is Goodman-Kruskal’s lambda

S is the sum of the highest cell count for each row

R is the highest row total

N is the total of all cell counts (Minitab, 2016) 3.6.2 Comparison of AD results of customers and PDT; Comparison of SC results of customers and PDT

The second research question of the thesis is related to comparing the AD and SC results of both PDT and customers. Comparing the ADs is mostly qualitative. The

discussion styles, thinking style based on the categorical names given by the participants were analyzed.

Comparison of SC of both PDT and customers is however done through quantitative comparison. A special package called “cluster.stats” was used to analyse the individual clustering. The package provides results based on various parameters (refer Appendix E) and then they are analysed.

For the purpose of comparison, information on how the customer needs were valued by the target customers was also collected.

4 RESULTS In the results chapter the results that are obtained with the process/methods described in the previous chapter are compiled, analysed and compared with the existing knowledge and/or theory presented in the frame of reference chapter.

4.1 List of customer needs Appendix C displays the final summary of customer needs collected through the various interviews in the study.

4.2 Comparison of grouped needs by AD and SC by Product Development Team

In appendix H the individual groupings of the customer needs by the product developers is presented. All the individual responses were converted into similarity matrix, using the rules described in section 2.4. Fig. 11, shows the construction of co-occurrence matrix. Adding all the similarity matrices gives rise to co-occurrence matrix.

Fig 11. Co-Occurrence Matrix of PDT

Using R program, (see Appendix F for the code in use) a distance matrix is constructed and using hierarchical clustering, a Dendrogram is generated. Arbitrarily, the dendrogram is cut at a certain height (in this case where it gives four clusters). Refer fig 12.

Fig 12. Dendrogram from SC by PDT

Refer Appendix D for the tabulated groups of customer needs from SC and AD.

Table 1 is created by comparing and contrasting the needs of AD and SC. Four comparisons are obtained by best fit (Since there were ten groups formed under AD and only four under SC, the nearest groups are combined together to compare and contrast with the four groups of SC). The colors indicate the respective customer needs that do not fall under the same comparison group.

Table 1. Comparison of AD and SC by PDT

Ambience issues and Venue rules

Training logistics, format and

participant learning issues

Technical, equipment issues and wishes

Workarounds, trainer preferences and client’s

needs

AD SC AD SC AD SC AD SC 1 1 2 2 6 6 12 20 9 9 32 32 7 12 14 14

11 11 38 38 17 17 21 21 13 13 39 39 23 23 42 7 15 15 40 40 33 48 48 18 18 4 4 37 37 16 16 22 22 20 42 41 10 10 27 27 26 26 45 45 44 8

30 30 3 3 25 25 33 35 35 44 28 41 36 36 5 5 43 43 8 47 47 19 19 31 31 24 24

28 29 29 34 34 46 46

For the same reason, to measure the degree of similarity or association, Goodman and Kruskal’s λ is calculated by using the formula (refer section 3.6.1). Refer Table 2, The elements common among both AD and SC are filled along the diagonal and the un-matched elements are associated with that particular category. For e.g., in the first group, there are 14 elements that are common to both AD and SC, hence 14 is filled in the first row and first column. The need number 28 (green) is an un-matched element in SC in the first comparison group and an un-matched element in AD in third group. So in the table, since there is only one un-matched element, it is given to first group under SC and third group under AD. In the similar way, the entire table is constructed and the index is calculated to be 0.709

Table 2. Association between AD and SC grouping results of PDT

SC

A B C D

AD

A 14 14 B 8 1 9 C 1 12 4 17 D 2 1 5 8

15 10 13 10

4.3 Comparison of grouped needs by AD and SC by Customers

See Appendix I for the individual grouping results of the customer needs by all the six customers. All the individual responses were converted into similarity matrix, using the rules described in section 2.4. Fig 13, shows the construction of co-occurrence matrix. Adding all the similarity matrices gives rise to co-occurrence matrix.

Fig 13. Construction of Co-Occurrence matrix for customers

Using R program, (refer Appendix F) a distance matrix is constructed and using hierarchical clustering, a Dendrogram is generated. Arbitrarily, the dendrogram is cut at a certain height (in this case where it gives four clusters). Refer fig 14.

Fig 14. Dendrogram from SC by Customers

Refer Appendix D for the tabulated groups of customer needs from SC and AD.

Table 4 is created by comparing and contrasting the needs of AD and SC. Four comparisons are obtained by best fit (Since there were nine groups formed under AD and only four under SC, the nearest groups are combined together to compare and contrast with the four groups of SC). The colors indicate the respective customer needs. There are some needs that do not fall under the same comparison group.

Table 4. Comparison of AD and SC by Customers

Clients attitude, participant’s attitude

and trainer’s technique

Training planning, aids, setup and

Trainer’s attitude

Ambience

Training technology aids

AD SC AD SC AD SC AD SC 2 2 3 3 1 36 5 5 7 32 4 4 9 8 8

10 10 6 6 11 11 15 15 21 21 12 28 13 17 38 38 14 14 18 18 19 19

40 40 16 16 22 23 12 42 42 20 20 27 27 24 24 44 44 25 25 28 29 29

39 26 26 35 35 34 34 30 30 43 43 46 46 31 31 47 47

32 9 33 33 36 1 37 37 39 13 41 41 45 45 48 48 7 17 23 22

For the same reason to measure the degree of similarity or association, Goodman and Kruskal’s λ is obtained by using the formula (refer section 3.6.1). The elements common among both AD and SC are filled along the diagonal and the un-matched elements are associated with that particular category. For e.g., there are 7 elements that are common to AD and SC in the first group and hence 7 is written at the first row and first column. The need number 36 (blue) is an un-matched element in SC in the third comparison group and an un-matched element in AD in second group. So in the table, since there is only one un-matched element, it is given to third group under SC and second group under AD. In the similar way, the entire table is constructed and the index is 0.58

Table 5. Association between AD and SC grouping results of customers

SC A B C D

AD

A 7 1 8 B 2 15 1 1 19 C 5 6 11 D 2 8 10

9 23 7 9

4.4 Comparison of grouped needs of both AD and SC by Product Development Team and Customers Table 6 provides the comparison of all needs clustered through AD and table 7 provides the analysis of the clustering through SC. The latter is created using R program. (see, Appendix G)

Table 6. Comparison of AD by PDT and customers

AD

PDT Customers

9 32 28 27 24 2 24 20 43 21 30 35 15 40 37 21 1 39 31 47 5 39 12 7

35 10 6 11 34 32 46 16 47 40 16 43 8 38 45 8 36 42 9 18 29 42 34 14 27 2 1 36 46 3 17 10 22 38 13 12 4 29 33 18 44 14 19 26 19 41 11 20 44 6 48 13 7 23 15 48 5 31 4 25 28 26 30 17

23 33 3 37 41 45 22 25

Table 7. Comparison of SC by PDT and Customers

Parameters PDT Customers

Number of cases 48 48 Number of clusters 4 4

Vector of cluster sizes (number of points) [15 10 13 10] [23 9 9 7]

Size of smallest cluster 10 7 Number of noise points 0 0

Vector of cluster diameters [5.86 8.10 8.72 9.47] [13.97 9.58 9.02 7.36] Within cluster average distances [2.91 5.18 4.67 6.13] [9.44 6.80 6.72 5.13] Within cluster distance medians [3.53 5.50 4.56 6.29] [9.58 7.33 6.54 5.30]

Separation [7.29 6.37 3.95 3.95] [6.21 6.21 6.85 6.62] Average toother 14.47 11.91 12.43 10.57 [12.43 13.16 12.91 12.44]

Separation Matrix

0.00 8.36 8.48 7.29 8.36 0.00 7.97 6.37 8.48 7.97 0.00 3.95 7.29 6.37 3.95 0.00

0.00 6.21 6.85 6.62 6.21 0.00 7.84 10.66 6.85 7.84 0.00 8.48 6.62 10.66 8.48 0.00

Matrix of mean dissimilarities between points of every pair of

clusters

0.00 13.28 15.98 13.69

13.28 0.00 11.59 9.79

0.00 12.75 12.95 11.34 12.75 0.00 12.88 14.89 12.95 12.88 0.00 12.82

15.98 11.95 0.00 7.58 13.69 9.79 7.58 0.00

11.34 14.89 12.82 0.00

Average distance between clusters 12.49 12.70

Average distance within clusters 4.31 8.62 Number of distances between

clusters 855 782

Number of distances within clusters 273 346

Maximum cluster diameter 9.47 13.96 Minimum cluster separation 3.95 6.21

Within cluster sum of squares 585.290 1509.59 Cluster average silhouette

widths 0.76 0.45 0.37 0.16 0.07 0.43 0.44 0.53

Average silhouette width 0.47 0.28 Goodman and Kruskal’s

Gamma coefficient 0.95 0.75

G3 coefficient NULL NULL Pearsongamma 0.74 0.60

Dunn index 0.41 0.44 Minimum average dissimilarity

between two clusters 1.23 1.20

Entropy of the distribution of cluster memberships 1.37 1.26

Ratio of average distance within clusters and average distance

between clusters 0.34 0.67

Calinski and Harabasz index 63.51 17.51 Vector of widest within-cluster

gaps 4.79 4.45 4.56 5.10 7.50 6.77 6.29 5.40

Widest within-cluster gap 5.10 7.50 Separation index 4.05 6.39

Corrected Rand Index NULL NULL Variation of Information Index NULL NULL

4.5 Importance of needs A simple rating on the customer needs was obtained to help in the comparison analysis of grouped needs of AD and SC by customers. Fig 15 shows the chart of importance of customer needs.

Fig 15. Importance of Customer needs

5 ANALYSIS AND DISCUSSION A discussion of the results that the author has drawn during the Master of Science thesis is presented in this chapter.

5.1 Discussion The United States Consumer Product Safety Act (CPSA), enacted in 1972 by Congress, has an extensive definition of consumer product: "any article, or component part thereof, produced or distributed (i) for sale to a consumer for use in or around a permanent or temporary household or residence, a school, in recreation, or otherwise, or (ii) for the personal use, consumption or enjoyment of a consumer in or around a permanent or temporary household or residence, a school, in recreation, or otherwise; but such term does not include— (A) any article which is not customarily produced or distributed for sale to, or use or consumption by, or enjoyment of, a consumer". As per the above definition, the products that are studied in this thesis can be classified as consumer products. Further it can be noticed that the customer needs collected from the customers had resemblances with the format of JTBD. For e.g., consider customer need #24, “I want a better clicker, as the current ones are bulky and when I place them on the pocket, it bulges out”. The social dimension of the related jobs to be done can be seen. On the other hand, consider customer need #29 which says, “I wish to have a high tech transparent screen and voice control”. Here, the personal dimension of the main jobs to be done can be seen. Customer need #15 says, “I wish I was able to quickly control the brightness of the lighting in the training hall”, which describes the functional aspects of the related jobs to be done. Customer need #45 touches on the emotional aspect of the main jobs to be done, which says, “Sometimes the laptops and projectors are not compatible at all and it is very frustrating”. So in this way it can be seen that in reality, the needs vary differently from customer to customer and this could be once again taken as a strong motivation as to why we need a similar research in the consumer products industry.

It is shown that the number of clusters obtained through the AD and SC vary. So logically it becomes tough to compare if the quantity differs. So in both under the PDT and customer’s comparisons, the number of clusters in AD were matched to the number of clusters in SC, by combining those groups that provided the highest number of common elements. This could be seen as a shortcoming. So for the future researchers, it is suggested that in AD, if there are many groups, then the respondents must be asked to group the similar groups once again, so that it could be easier for the researcher to compare both the methods. Also the results of the SC is based on the dendrogram. The method of the previous researcher was followed in this thesis as well, which is to cut the dendrogram at a certain height. However, there is no particular rule or method to do this. It is insisted that there must be a method so that it would be a fair comparison, as there is every possibility that the height of the dendrogram differs (based on the similarity of the groupings at various levels) and one might cut the dendrogram at a lower level in one method and cut at a higher level in another method.

5.1.1 Comparison of grouped needs by AD & SC by PDT Directly comparing the AD and SC results of PDT through Goodman and Kruskal’s lambda it yields a value of 0.709. In other words, it can be understood that the grouping needs by AD method and is 70% similar to SC or vice versa.

Conversely it can be thought that there is 30% dissimilarity (about one-thirds, which is significant). One argument as to why 70% is a reasonable value is as follows. In B2B in general, it can be regarded, there is just one customer (in fact many but all the customers look for the same solution), but in B2C, there are innumerable number of customers. Which also means that when there is just one customer, the diversity in the needs of the customer is reduced. However, when it comes to B2C, diverse customer needs exist. So what could have likely happened is that every diversified need is interpreted in a different way by every product developer in SC as it is famously said a product developer is to think by putting themselves in the shoes of the customer (Whittle & Foster, 1989). This could be compared and contrasted with the previous research done by (Takai & Ishii, 2010), where 92.5% similarity or association was found which was in a B2B setting or an industrial/scientific context. This could be understood in the light of the reason that in scientific context, because of the nature of the project, all the scientists must be well informed, and be extremely clear about every detail of the project, which gives a high similarity index compared to consumer products segment.

5.1.2 Comparison of grouped needs by AD & SC by Customers As famously pointed out by Steve Jobs once and also being pointed out iteratively by many researchers, customers many times do not know what they need in the first place (Mui, 2011). This has been partially reflected in the comparison made in this study as well. Goodman and Kruskal’s index of 0.58, or in other words that there is only 58% of similarity (or 42% dissimilarity) or association of AD over SC and vice versa. There seems to be a vast difference in the results when customers grouped the data individually and later when they came together to group collectively. When the customers got together to discuss these issues, they obtained more insights of the problems and made them to think further and from different dimensions. See For example Customer 1 (see, Appendix I), separately grouped the needs, categorized them under very generic terms such as “challenge”, “Constraints” and even categorized some of the needs as “Not applicable”, thinking from the customer’s own point of view. However, when all the customers grouped together, the Customer 1 analyzed the needs from a deeper level and joined others in giving even more specific category names. Customer 1 continued further to give her own examples on how she faced certain situations during her training. For example, she motivated others to categorize the need, “Would be nice to find a way to overcome the language barrier during training a different audience” (need #42), alone in a separate group by giving concrete examples on how she faced difficulty in training a particular group of employees in a state in North India. On listening to this example, other customers also said that they experienced the same and went ahead in categorizing just this one single need under one category. Referring to Fig 14 listing the importance of customer needs, it is also found to be a need that was rated most important by all the customers.

On further examination of the individual groupings, it was found that the category names under the AD were more specific than generic like the ones under SC. The names given under individual groupings resembled more like a primary need (for e.g., Attitude, flexibility, comfort, constraint and challenge). However the category names on AD resembles more like a secondary need (for e.g., Client’s attitude, participant’s attitude, Trainer’s attitude), perhaps thanks to some discussion that happens. Griffin & Hauser (1993) defines primary needs as those top needs that give the strategic direction for marketing. Each primary need further elaborates into secondary needs. Each secondary need indicates specifically what the marketing manager should do to satisfy the corresponding primary need. This, in a way is helpful that, later it helps the product developers during the product development phase to be more concise in certain features of the product.

It was observed that having more discussions lead to a more refined and more pragmatic clustering of the needs under appropriate headings. Otherwise, just considering results from SC wouldn’t have given any meaning at all.

As the moderator, the author of this paper felt that there was a methodology missing in moderating an AD discussion. There is a need to identify additional methods to better support the group discussions. 5.1.3 Comparison of grouped needs by AD by PDT and customers There weren’t any significant implications on comparing the AD of PDT and customers except that it was noticed that there is a difference in thinking styles of the product development team compared to that of the customers. For instance, (refer to need #16), where it says, “Sometimes I have to stick two smaller papers to create a bigger paper to stick it on the wall to place participant's work”, it was interpreted as a workaround (Workaround means a method for overcoming a problem or limitation in the system) by the product development team and it was also unanimously agreed to group it alone. In contrast, the customers grouped it under the Training aid category. There was more deliberation between the PDT on whether to categorize it as “Workarounds”, although it was the only category with only one need. This indicates that the product developers are prone to become more solution oriented than the customers. That may not be as surprising as the goal for a product developer is to identify and design solutions for their customers.

5.1.4 Comparison of grouped needs by SC by PDT and customers With reference to Table 7, (Refer to the meaning of the parameters in the appendix E)

Both the categories of PDT and customers contain the same number of cases (customer needs), which is 48, and the number of clusters analyzed is 4 in each case. All the cluster sizes are around the same size of 10 except the first cluster in the customer category (see Vector of cluster sizes). The needs in all the clusters are spaced averagely at a point scale of 5-6 (See within cluster average distances), except in one cluster in PDT category where it is very closely spaced and one cluster in customer category very distantly spaced. Interestingly both these phenomena have happened in the clusters with the highest number of needs and most of the values in PDT are lesser than the values in the customer’s segment. This leads to an

understanding that the Product Developers choose to group needs that are more similar to each other compared to the customers, which in turn hints that there is more similarity in thoughts of the product developers compared to that of customers. Or it can also be interpreted as that the product developers think about the solutions for the problems for customers as a whole and each of the customer thinks and groups the customer needs from their own perspective, causing the points inside the cluster to be further away.

But the average distance within clusters in PDT category is much lesser, in fact exactly half of the customer category. This leads to an understanding that the cluster members in customer segment are further away compared to PDT category. Or in other words, the members of a cluster in PDT are more similar compared to the members in a cluster in customer segment. Perhaps, the PDT has a more unified understanding and are on the same page whereas, the needs are different for every customer, even though they all belong to the same category, re-iterating the first implication made.

Further, the following parameters also re-iterate the same. To an extent, within cluster sum of squares also tell the same, as the PDT category value is only a third of the customer category, where the members of the clusters are more dispersed than the former. This is further backed by the average silhouette width, and Goodman and Kruskal’s gamma coefficient signifying that members of a cluster belonging to PDT category is more cohesive or similar than any cluster belonging to the customer category. Dunn Index which is the ratio between the minimum separation and maximum diameter, is seen to be higher in the PDT category than the customer category and in the same way, the ratio of average distance within clusters and average distance between clusters is seen to be lesser in PDT category and more in customer category. This again re-iterates the previous claim.

Fig 16. Symbolic representation of the spread of clusters and customer needs in SC

Fig 16 simply summarizes the comparison of grouped needs by SC by PDT and customers. The clusters are spaced distantly in PDT, meaning that they are very distinct from each other, while the customer needs are arranged very closely inside one cluster, meaning that the customer needs are more similar. It is however contrary with the customers as the customer needs inside a cluster are distantly spaced, meaning that they are less similar and the clusters are spaced very closely, meaning that the clusters as a whole are less distinct with each other.

6 CONCLUSION The conclusions are based from the analysis with the intention to answer the formulation of questions that is presented in Chapter 1.

In conclusion, this thesis has brought to light the importance of AD and SC in structuring the customer needs, after the work performed by Takai & Ishii ( 2010). It is surprising to see that not much work has been done in this very specific field of research. It is hoped that there shall be more experiments conducted in the days to come to help the academia and the industry to better understand this important task in the product development process.

The first and foremost aim of the thesis was to find to what extent the SC supports AD in consumer products. The answer to this question is given by the comparison between SC and AD of customers and between SC and AD by PDT. In the table 8, the research questions are presented along with the respective conclusion.

Table 8. Conclusions

S.No Research Question Conclusion

1

To what extent does Subjective Clustering aid Affinity Diagram in grouping customer needs in a consumer products context?

There was 70% association between AD and SC of PDT. This is much lesser than the 92.5% association obtained in the previous research. The PDT’s results are higher than the customer’s which stands at 58%. In both the instances it was found that the results were lesser compared to the existing research. This comes at no surprise because of the reason that in consumer products industry, the needs of the consumers are so diversified, that each consumer has different preferences. It can also be seen from the lesser match value among the customers compared to the product developers. Further it was found from the comparison of AD and SC by customers, that having more discussions lead to pragmatic clustering of needs. So more of AD is recommended in consumer products context as they give more specific results. Hence this is a motivation for the Product Development Team to organize more intensive focus group discussions to generate further insights and structure the needs in a better way. Subjective Clustering seems to be more suitable in industrial context, such as the example in previous research, where the customer needs are consistent across all players and not diversified.

2

What could be learnt by comparing results between subjective clustering of Product Development Team and customers?

The customer needs within the same group, grouped by the product developers are more cohesive than the customers and the same groups are more distinct compared to the fellow groups than that of the customers. This means that Product developers think about a solution from a holistic perspective and each of the customer is concerned about his/her own solutions to their problems. This further motivates to consider a focus group that contains both product developers and customers in grouping the needs. It would be a team comprising holistic thinking and pragmatic thinking. This is also an interesting topic for the future work.

7 RECOMMENDATIONS AND FUTURE WORK In this chapter, recommendations on more detailed solutions and/or future work in this field are presented. The one biggest take away from the thesis work would be that, in the consumer products industry, usage of Affinity Diagram methods would lead to better results. So it is recommended for managers in companies to concentrate more on Affinity Diagram methods for structuring the customer needs for consumer products. However, during the course of the research, it was observed that more disciplined and structured rules could be used while performing AD. One widely appreciated tools in the management of creativity and discussion in groups is Edward De Bono’s 6 thinking hats method (De Bono, 1989). Because of the way this tool is structured, it enables the entire team members to think in one direction at a given point of time. What happens in a 6 thinking hats session is that, a particular problem is discussed from one perspective at a given point of time. Because everybody on the team aims to discuss the problem in one direction, there is more collaboration among the team while one person is trying to react emotionally whereas another one reacting in a more rational way. It is perceived that this tool could be more useful during the AD sessions, as it promises the buy-ins of all the team members. So far, there has been no research studies on the impact of utilization of such methods in Affinity Diagram creation sessions and it is hence suggested that further research is performed to understand how it can improve the results and outputs of Affinity Diagram, to get a more cohesion and collaboration between the team members.

In the similar way, the comparison of SC results revealed the potential of groups being composed of both Product Developers and customers. It is suggested that it would be interesting to understand how AD and SC should be used in such settings...

This thesis was based on just one case study, with its own delimitations, some of them being that the study was performed with a consulting firm and the example being taken was educational aids and equipment. It would be compelling to see the results with other consumer products and durables. Nevertheless, due to the lack of research in this domain, it is strongly suggested that more such studies could be conducted in various research settings.

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http://www.saedsayad.com/clustering_hierarchical.htm

APPENDIX A : BRAINSTORMED QUESTIONS

No. Question to ask relevant for whom comments

1

What goals do you try to achieve with your training programs? E.g. build competencies, entertain, create experiences, motivate? All of these? A mix of some?

2,3,4,5

2 How do you achieve those goals? 2,3,4,5

3

What sort of equipement and aids to you use to reach your training / workshop goals?

2,3,4,5 ask why for the things he / she

mentions

4 Have there been situations where you had difficulties

reaching your training goals? 2,3,4,5

5 Why do you think you had those issues? 2,3,4,5 6 When would you use virtual aids and physical aids 2,3,4,5 7 What is the advantage of one over the other 2,3,4,5

8 What kind of trainings and workshops are your

providing? 2,3,4,5

9 How many participants would on average attend one

of your trainings? 2,3,4,5

10 What is the duration of your events? 2,3,4,5

11 What materials / means do you use to communicate

your contents accross? 2,3,4,5

12

What kind of materials and equipment do you require during your training (such as. Flip charts and white

boards)? How many of them do you require?

2,3,4,5 for each ask for what they

require it for

13 Are there specific requirements for your trainings in

terms of venue, space and setup? 2,3,4,5

14

Are specific issues you often run into when it comes to training equipment?

2,3,4,5 why are these issues not being

solved?

15

Are there any work arounds you need to use to make your trainings go the way you want to?

2,3,4,5 such as taping paper onto the wall because there is not

enough space on whiteboards or

flipcharts

16

Is there something that specifically annoys or bugs you when it comes to delivering your trainings (can be participant behavior, venue, equipment, travel,

anything)?

2,3,4,5

17

Can you compare the venue and equipment situation in China / India to other countries you have trained

you? What did you like more or less there?

2,3,4,5 are there specific reasons

why the situation in

China / India is like it is?

18 Was there something in terms of venue and

equipment that once particularly impressed you? 2,3,4,5

19

If you had a magic wand and could create the perfect training environment for your trainings, what would that look like? What kind of equipment would you

have?

2,3,4,5 ask why questions to dig

deeper.

20

Are there any cultural influences that affect the way your training venues look like in the countries you

train?

2,3,4,5

21

How important do you believe training and workshop equipment to be for trainings and workshops as well

as for team collaboration in general?

2,3,4,5

22 What is your expectations out of a training program?

Why do you attend a training? 14

23 Who normally decides which trainings you attend?

You yourself? Somebody the company you work for? 14

24 how many trainings or workshops per year do you

attend on average?

25 Who normally pays for the trainings you attend? 14

26

Is there a difference between the trainings you pay for and the trainings somebody else pays for?

14 according to what criteria do you select which

training to attend

27 Describe the situation where you had really

benefitted the training program 14

28 What sort of tools has the trainer in a typical training

session that you know, uses? 14

29

What sort of equipment and aids (such as flip charts, whiteboards, etc.) are trainers of your trainings

using?

14

30

Describe the situation when you were annoyed/frustrated/not happy with the training aids

that the trainer had used

14

31

Describe a situation when the training equipment made a positive impact on the training and your

learnings

14

32 Describe a situation when you were at the highest of your creativity during a training? What caused that?

14

33

What impact do you think the training venue and training equipment has on the training and the

effectiveness of a training?

14

34

What sort of training aids and equipment (suach as flip charts and white boards) does your hotel provide

?

8.9

35 based on what criteria are these purchased? 8,9,13 36 who decides what material / equipment is purchased? 13

37 how do you know what materials and equipment to

purchase? 8,9,13

38 How much do you usually allocate your budgets for

such training aids 8,9,13

39 How does the storage of these equipments look like? 8,9,13

40 What roles does price play in your purchasing

decisions? What are other criteria? 13

41 what kind of events happen in your hotel? How many

of them are trainings / workshops? 8.9

42

what kind of equipment is normally requested for trainings and workshops? And how many?

8.9 this is important to ask, as I feel

that our trainings require more equipment than most of the other trainings that are held in

hotels. However, I am not sure about

this.

43

Is the material and equipment you have enough to fullfil the requests? Or do you run into situations

where you cannot offer what the trainer or customer wants?

8.9

44

do you get any kind of improvement feedback from customers towards your venue and the equipment in

particuluar?

8.9

45 How important is the quality of your venue and

training equipment for your customers? 8.9

46 would high quality equipment be a positive

differentiator for your hotel? 8.9

47 what are the main decision criteria according to

which customers book a hotel as a training venue? 8.9

48 What do you consider while designing a

training/conference/board room 11

49 What are the design criteria kept in mind while

designing a room for the Trainers 11

50 How do the customers ask for what they want 12

51 What are the other doubts they would ask? For

instance price? 12

52 what kind of trainings does your company organize,

technical, soft skills 6

53 how many participants do you have on average per

training 6

54 do you have your own training facilities or do you

rent external facilities, or both? 6

55 are people also being sent to external trainings, or are

all your trainings internally organized? 6

56

what is the character of the trainings you host? Is it mostly classroom / lecture style trainings with the teacher in front and participants listening or more

interactive trainings with group activities?

6.7

57

are there different venue and equipment requirements for these different types of trainings? If yes, are you

addressing this requirements?

6.7

58 Can you describe your training facilities 7 59 How are your training facilities equipped 7

60 what kind of feedback do you get towards your

trainings facilities from trainers? 7

61 what kind of feedback do you get towards your trainings facilities from training participants?

7

62 according to what criteria do you select external

training or workshop venues? 6.7

63

according to what criteria do you buy / pruchase trainign equipment such as flipcharts and

whiteboards

6.7

64 is there anything missing in terms of equipment that

you would like to have? 6.7

65 are there any challenges, obstacles or limitations that

trainers run into when using your training venues? 6.7

66 if you had all the decision making power, what

would you change regarding your training venue? 6.7

67

have you been to an external training venue (maybe in another company, a hotel, abroad) that you member especially well? If yes, what made it

memorable for you?

6.7

68 what kind of trainings are you offering to your

clients? In terms of topics and content 10

69 how would you describe the character of the

trainings, is it more lecture style or interactive? 10

70 In terms of training character, what are your

customers requesting? 10

71 Any trends you currently see in the training space in

terms of topics but also training formats etc? 10

72 what kind of venue setup and equipment are your

trainers requesting? 10

73 are the trainings held inside corporate venues or in

external venues? 10

74 are your company customers able to fullfil the venue

and equipment requirements? 10

75

have there been cases of very unusal training / workshop venue or equipment requirements? What

were these?

10

76

are you also requested to organize venue and equipment? If yes, what are your criteria for selecting

a venue and equipment?

10

77 how important is the equipment such as flipcharts,

projectors, whiteboards for your overall work? 10

78

In terms of venue equipment, is there anything that would make your life easier or improve the quality of

trainings

10

ID

Types of people to interview / observe

(divergent list) why might we speak to them? relevant for observations

14 Participants of a

training Workshop they know what is eventually needed for

them yes

2,3,4,5

Local or foreign Trainers active in China

or India

they know their needs on site and have seen different training rooms and

facilities yes

6

Company employees responsible for training

planning

they should have an overview over what kinds of trainings and events are

happening and maybe special needs they have encountered yes

7

Company employees responsible for training

logistics they know more details in terms of what

happens onsite yes

8.9

Hotel staff responsible for room bookings and

customers requests Hotel staff responsible

for room logistics they have a broad overview over all kinds of different requests in hotels yes

10 Trainer agencies

they have an overview over all kinds of different training suppliers, styles and needs, as well as customer demands yes

13

Purchase Departments in Hotels / Purchase

Manager they might know the budget constraints

and quantities of supplies needed yes

APPENDIX B: INTERVIEW GUIDE WITH SAMPLE ANSWER

Interview guide for Local or Foreign trainers active in India or China

Name:

Number of years into training profession: from 2005 – 10 years

Special extra Notes:

___________________________________________________________________________________________

What kind of trainings and workshops are your providing?

Behavioural skills development, managerial skills development special reference to leadership and commnunciation

How many participants would on average attend one of your trainings?

30

What is the duration of your events?

90 minutes – snapshot and one day workshop (6 hour) and 12 hours (2 days workshop – in house where they will stay overnight )

What goals do you try to achieve with your training programs? E.g. build competencies, entertain, create experiences, motivate? All of these? A mix of some?

Three different aspects – Knowledge based training (Incorporate – for eg : what are the qualities of leadership), skill based ( How to impart the qualities of a leadership by giving a practical workshop), sometimes, on focus on – they will have skills and knowledge but do not know how to implement – attitude based training – but usually all the three will be present –but percentage varied.

How do you achieve those goals?

Three levels – By observation of the trainer – trainer will have checks (Personal Observations), 2. Response from the participants 3. After training

effects.

What sort of equipment and aids to you use to reach your training / workshop goals?

Based on module, not mandatory to have projector – if the module is skill oriented, cant use projector. If knowledge based – Projector, white board or flip chart ; for skill based – not much of display aids is required, but some learning or play articles – for instance, playing cards or a sheet of paper is used (for e.g: give one sheet to every team, and ask them to do maximum number of items out of it – maximum based on team work – and see how the team does it) – So aids is not always a standard one. (A rope can be used for instance) – For Attitude - Questionnaire, article etc.

Have there been situations where you had difficulties reaching your training goals?

If at all 50 years old participants are listening to a 39 years old trainer – psychological trainer ; language barrier – lingual barriers : goals not achieved ; whenever heterogeneties are present – whenever need assesment not done, difficult. Sometimes, the ambience makes a lot of importance

8. Why do you think you had those issues?

Answered above.

9. When would you use virtual aids and physical aids?

Answered above – 6

10. What is the advantage of one over the other?

Answered above - 6

11. What materials / means do you use to communicate your contents accross? What kind of materials and equipment do you require during your training (such as. Flip charts and white boards)?

12. How many of them do you require?

Not number, it is the requirements – some cases I say flipcharts, somecases, good visuals is required, some cases, I may need an aid like rope or piece of

paper, every module will be divided into expectatioins – and found what I need to achieve. Sometimes, participants will themselves become aids.

13. Are there specific requirements for your trainings in terms of venue, space and setup?

Good spatial ambience – Training mood should be good ; temeprature should be maintained ; logistics – easily movable ; round table – cluster ; they should feel that, there are others who are participating,

14. Are specific issues you often run into when it comes to training equipment?

Whenver something is relied on electronics or gadgets, whenever any power backup is not there, whenver computer is not supporting, aids will become trouble makers, even in flipcharts – markers used on them sometimes will be visible : so check if the right marker is used : for instance white background and black pen, sometimes paper is not held proeprly., sometimes, sketches is not supporting, and a minute gap of 30 seconds/2 mintues will affect the psychology of training. So the aid is to magnify and if the aid is not working, then the result will not come.

15. Are there any work arounds you need to use to make your trainings go the way you want to?

Projector cant be replaced by white board, for instance notepad, pen can be used a display aid..

16. Is there something that specifically annoys or bugs you when it comes to delivering your trainings (can be participant behavior, venue, equipment, travel, anything)?

Most of the time, ambience – When aids are not there, we will have a backup, but when ambience is not good, nothing can be done regarding that.. that pulls down the motivation also, when something is suggested to organsiers, they willl arrange something else.

17. Can you compare the venue and equipment situation in China / India to other countries you have trained you? What did you like more or less there?

A program done in Malaysia – Not much difference there and India, except the participants – concernign to aids, venue, may be quality – a sight difference

18. Was there something in terms of venue and equipment that once particularly impressed you?

Projectors – 2 types : data and multimedia; data – their own limitations, but multimedia – reach is good – good resolution, luminous power and intensity – audinece cant skip : Multimedia projectors really helps – very impressive!

Smart boards – Whatever you write on the board – anything can be displayed – if you get some good ideas, you write on it and immediately displayed on big screen.

19. If you had a magic wand and could create the perfect training environment for your trainings, what would that look like? What kind of equipment would you have?

Want an equipment that gives 3D picturisation : for instance if talking about Taj mahal – then I want something that really shows Taj mahal for instance holograms ; a device that will tell what level the participant’s understanding/focus is there; a voice interactive aids – go to slide 44, open the picture of slanting tower of Pisa, play the beethoven music – light veriosn eg.

Whatever I scribble on the board, it must be saved, whatever I speak, must be recorded, at the end of the day, it must be taken away by the partiicpant.

20. Are there any cultural influences that affect the way your training venues look like in the countries you train?

Certainly! For instance in Malaysia, a team with a mix of male and female not encourage.d.

21. How important do you believe training and workshop equipment to be for trainings and workshops as well as for team collaboration in general?

22. Is there anybody else you recommend me to speak to? That you could possible make an introduction?

APPENDIX C : LIST OF CUSTOMER NEEDS No. Need

1 The training rooms sometimes look like hospital rooms and they are sick 2 Most of the times, the participants do not go back and refer the training materials

3 Sometimes there is very less time in workshops and training programs to cover the entire topic and sections

4 There are a lot of materials to carry during a training program

5 I want something that can block phone signals during the training program, and when I want, I could turn off

6 Equipment failures such as audio problems are big issues

7 3M post-its are expensive and so the clients do not buy them at all, but they are excellent in quality

8 Instead of PPT, I would like to write on a tablet that wirelessly project contents over the projector or smart board

9 There are backups if there are issues with equipment or other devices, but ambience cannot be changed

10 The companies are not serious about the bad equipment and the effect it gives

11 I appreciate the presence of extra spaces during training program, which I could use to alter the seating arrangement

12 I would like to use PPT for larger groups and flipcharts for smaller groups

13 Sometimes the training happens in auditoriums and they have fixed seats. They are so annoying

14 I would like to have some play toys on the participants table, which might improve their creativity

15 I wish I was able to quickly control the brightness of the lighting in the training hall

16 Sometimes I have to stick two smaller papers to create a bigger paper to stick it on the wall to place participant's work

17 Sometimes the projectors project different colors than how it must originally look

18 Lots of natural light creates a nice atmosphere

19 I want an immediate response system - When the participants vote on different ideas, they can be seen on the screen immediately

20 I have to bring big size flipcharts all the way from Germany to China and India

21 It sometimes disappoints me when good quality posts its such as static post-its are thrown away just like that

22 The training rooms are sometimes cramped and lacks facilities such as lunch rooms and snacks.

23 There is always some sort of cable issues while connecting laptops with projectors in different venues

24 I want a better clicker, as the current ones are bulky and when I place them on the pocket, it bulges out

25 Electricity and power cuts during the training programs are frustrating - There need to be a better system

26 I will not trust any hotels on the equipment. So I carry all portable devices such as mic, speaker, projector in a bag

27 I want to have a different training room set up such as a bar in a training room

28 Sometimes the walls in training rooms are soapy and hence it becomes difficult to stick post-its or any workshop materials

29 I wish to have high tech transparent screen and voice control 30 Rural areas do not have any setup at all 31 The hotels, most of the times do not allow sticking anything on the walls. 32 Before the training program begins, I want the participants to have an open mind 33 Sometimes more than two people are needed to move and handle the flipcharts

34 I want a big printer so I can print the worked materials during workshop on the spot and distribute to people

35 Free space wanted for participants to dance sometimes 36 I would like the training room to have different areas for different functions

37 The marker pens for writing in white boards gets dry sometimes and they go unnoticed. So there should be a better solution

38 People get into play mode during the activities, difficult to bring them back and it eliminates the purpose

39 Participants these days are fed up with PowerPoint

40 It is sometimes difficult for the participants to refer to a point and solve a doubt in the old training notes

41 The bad and disfunctioning equipment affect the psychology of training program itself

42 Would be nice to find a way to overcome the language barrier during training a different audience

43 Comfortable venue and seating directly relates to the mood of the participants

44 Sometimes there are no clear communication from the HR department on the outcomes of the training program and workshops

45 Sometimes the laptops and projectors are not compatible at all, and it is very frustrating 46 I wish there were 3D simulations and audience response system 47 I would like the training rooms to have some open spaces

48 I like when there are a lot of colorful stationaries such as markers, stickers for the participants.

APPENDIX D : AD AND SC RESULTS OF PDT AND CUSTOMERS

Grouped Customer Needs by PDT

Grouped needs of PDT by SC

Group 1 Group 2 Group 3 Group 4 1, 9, 11, 13, 15, 18, 22, 27, 28, 30, 31, 35, 36,

43, 47

2, 32, 38, 39, 40, 4, 42, 26, 3, 44

6, 12, 17, 23, 37, 45, 25, 5, 19, 24, 29, 34, 46

20, 14, 21, 7, 48, 16, 10, 8, 33, 41

Grouped needs of PDT by SC

Group 1 Group 2 Group 3 Group 4 2, 32, 10, 21, 38, 40,

42, 44, 39 3, 4, 6, 28, 14, 16, 20,

25, 26, 30, 31, 9, 33, 1, 37, 13, 41, 45, 48, 7,

17, 23, 22

36, 11, 18, 27, 35, 43, 47

5, 8, 15, 19, 12, 24, 29, 34, 46

Grouped Customer needs by Customers

APPENDIX E: R PROGRAM PACKAGE DESCRIPTION

APPENDIX F: R PROGRAM FOR CREATION OF DENDROGRAM

R version 3.2.1 (2015-06-18) -- "World-Famous Astronaut" Copyright (C) 2015 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.4.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. During startup - Warning messages: 1: Setting LC_CTYPE failed, using "C" 2: Setting LC_COLLATE failed, using "C" 3: Setting LC_TIME failed, using "C" 4: Setting LC_MESSAGES failed, using "C" 5: Setting LC_MONETARY failed, using "C" [R.app GUI 1.66 (6956) x86_64-apple-darwin13.4.0] WARNING: You're using a non-UTF8 locale, therefore only ASCII characters will work. Please read R for Mac OS X FAQ (see Help) section 9 and adjust your system preferences accordingly. [Workspace restored from /Users/Sandheep/.RData] [History restored from /Users/Sandheep/.Rapp.history] > a <- read.csv("test1.csv", row.name = 1) > hc <- hclust(dist(a)) > plot(hc, hang = -1) > a <- read.csv("test 1.csv", row.name = 1) > hc <- hclust(dist(a)) > plot(hc, hang = -1) > a <- read.csv("test123.csv", row.name = 1) > hc <- hclust(dist(a)) > plot(hc, hang = -1) > cutree(k = 11) Error in nrow(tree$merge) : argument "tree" is missing, with no default > m <- plot(hc, hang = -1)

> y = cutree(m, k = 11) Error in cutree(m, k = 11) : invalid 'tree' ('merge' component) > plot(cut(hc, k = 11:13)hang = -1) Error: unexpected symbol in "plot(cut(hc, k = 11:13)hang" > plot(cut(hc, k = 11:13), hang = -1) Error in cut.default(hc, k = 11:13) : 'x' must be numeric > plot(cutree(hc, k = 11:13), hang = -1) Warning messages: 1: In plot.window(...) : "hang" is not a graphical parameter 2: In plot.xy(xy, type, ...) : "hang" is not a graphical parameter 3: In axis(side = side, at = at, labels = labels, ...) : "hang" is not a graphical parameter 4: In axis(side = side, at = at, labels = labels, ...) : "hang" is not a graphical parameter 5: In box(...) : "hang" is not a graphical parameter 6: In title(...) : "hang" is not a graphical parameter > plot(hc, hang = -1) > plot(hc, hang = -2) > plot(hc, hang = -3) > plot(hc)

APPENDIX G: R PROGRAM CODE FOR CLUSTER ANALYSIS

Code and Output for PDT R version 3.3.1 (2016-06-21) -- "Bug in Your Hair" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.4.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. [R.app GUI 1.68 (7238) x86_64-apple-darwin13.4.0] [Workspace restored from /Users/Sandheep/.RData] [History restored from /Users/Sandheep/.Rapp.history] > install.packages('fpc') --- Please select a CRAN mirror for use in this session --- trying URL 'https://cran.cnr.berkeley.edu/bin/macosx/mavericks/contrib/3.3/fpc_2.1-10.tgz' Content type 'application/x-gzip' length 441999 bytes (431 KB) ================================================== downloaded 431 KB The downloaded binary packages are in /var/folders/v3/p4sxn1857l76t6mx59kmfmx40000gn/T//Rtmpp99ANU/downloaded_packages > a <- read.csv("test123.csv", row.name = 1) > hc <- dist(a) > cluster.stats(hc) Error: could not find function "cluster.stats" > b <- cutree(hclust(hc), 4) > cluster.stats(hc, b, G2 = TRUE) Error: could not find function "cluster.stats"

trying URL 'https://cran.cnr.berkeley.edu/bin/macosx/mavericks/contrib/3.3/fpc_2.1-10.tgz' Content type 'application/x-gzip' length 441999 bytes (431 KB) ================================================== downloaded 431 KB The downloaded binary packages are in /var/folders/v3/p4sxn1857l76t6mx59kmfmx40000gn/T//Rtmpp99ANU/downloaded_packages > cluster.stats(hc, b, G2 = TRUE) Error: could not find function "cluster.stats" > ??cluster.stats starting httpd help server ... done > c <- cluster.stats(hc, b, G2 = TRUE) Error: could not find function "cluster.stats" > library(fpc) > ??cluster.stats > cluster.stats(hc, b, G2 = TRUE) $n [1] 48 $cluster.number [1] 4 $cluster.size [1] 15 10 13 10 $min.cluster.size [1] 10 $noisen [1] 0 $diameter [1] 5.868116 8.107967 8.727767 9.473074 $average.distance [1] 2.911514 5.181432 4.672653 6.133217 $median.distance [1] 3.538607 5.500988 4.568322 6.296997 $separation [1] 7.295025 6.379314 3.956283 3.956283 $average.toother [1] 14.47300 11.91383 12.43279 10.57812 $separation.matrix

[,1] [,2] [,3] [,4] [1,] 0.000000 8.361402 8.485281 7.295025 [2,] 8.361402 0.000000 7.978231 6.379314 [3,] 8.485281 7.978231 0.000000 3.956283 [4,] 7.295025 6.379314 3.956283 0.000000 $ave.between.matrix [,1] [,2] [,3] [,4] [1,] 0.00000 13.289944 15.980096 13.696840 [2,] 13.28994 0.000000 11.959259 9.790595 [3,] 15.98010 11.959259 0.000000 7.585375 [4,] 13.69684 9.790595 7.585375 0.000000 $average.between [1] 12.4959 $average.within [1] 4.319909 $n.between [1] 855 $n.within [1] 273 $max.diameter [1] 9.473074 $min.separation [1] 3.956283 $within.cluster.ss [1] 585.2923 $clus.avg.silwidths 1 2 3 4 0.7693493 0.4567383 0.3788846 0.1601428 $avg.silwidth [1] 0.4715531 $g2 [1] 0.9536213 $g3 NULL $pearsongamma [1] 0.7439036

$dunn [1] 0.4176345 $dunn2 [1] 1.23677 $entropy [1] 1.370851 $wb.ratio [1] 0.345706 $ch [1] 63.51867 $cwidegap [1] 4.791296 4.452649 4.568322 5.107539 $widestgap [1] 5.107539 $sindex [1] 4.050685 $corrected.rand NULL $vi NULL _____________________________________________________________________ Code and Output for Customers R version 3.3.1 (2016-06-21) -- "Bug in Your Hair" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.4.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help.

Type 'q()' to quit R. [R.app GUI 1.68 (7238) x86_64-apple-darwin13.4.0] [Workspace restored from /Users/Sandheep/.RData] [History restored from /Users/Sandheep/.Rapp.history] > library(fpc) 2016-08-29 13:30:23.074 R[44802:9466099] plugin com.teamdrive.teamdrive3.FinderExt invalidated > a <- read.csv("COM_Cust", row.names = 1) Error in file(file, "rt") : cannot open the connection In addition: Warning message: In file(file, "rt") : cannot open file 'COM_Cust': No such file or directory > a <- read.csv("COM_Cust.csv", row.names = 1) > hc <- dist(a) > b <- cutree(hclust(hc), 4) > cluster.stats(hc, b, G2 = TRUE) $n [1] 48 $cluster.number [1] 4 $cluster.size [1] 23 9 9 7 $min.cluster.size [1] 7 $noisen [1] 0 $diameter [1] 13.968910 9.582593 9.021713 7.366198 $average.distance [1] 9.443577 6.799222 6.715849 5.132468 $median.distance [1] 9.582593 7.330611 6.540842 5.307910 $separation [1] 6.213590 6.213590 6.852483 6.620127 $average.toother [1] 12.43261 13.16550 12.91770 12.44892 $separation.matrix

[,1] [,2] [,3] [,4] [1,] 0.000000 6.213590 6.852483 6.620127 [2,] 6.213590 0.000000 7.846351 10.664855 [3,] 6.852483 7.846351 0.000000 8.485281 [4,] 6.620127 10.664855 8.485281 0.000000 $ave.between.matrix [,1] [,2] [,3] [,4] [1,] 0.00000 12.75017 12.95942 11.34701 [2,] 12.75017 0.00000 12.88486 14.89100 [3,] 12.95942 12.88486 0.00000 12.82283 [4,] 11.34701 14.89100 12.82283 0.00000 $average.between [1] 12.70895 $average.within [1] 8.622975 $n.between [1] 782 $n.within [1] 346 $max.diameter [1] 13.96891 $min.separation [1] 6.21359 $within.cluster.ss [1] 1509.591 $clus.avg.silwidths 1 2 3 4 0.07583336 0.43668614 0.44621493 0.53995370 $avg.silwidth [1] 0.280624 $g2 [1] 0.7543253 $g3 NULL $pearsongamma [1] 0.6043712

$dunn [1] 0.4448157 $dunn2 [1] 1.201559 $entropy [1] 1.261039 $wb.ratio [1] 0.6784964 $ch [1] 17.51496 $cwidegap [1] 7.506519 6.775916 6.296997 5.405311 $widestgap [1] 7.506519 $sindex [1] 6.397037 $corrected.rand NULL $vi NULL

APPENDIX H: INDIVIDUAL GROUPING OF CUSTOMER NEEDS BY PDT

ID PD 1 PD 2 PD 3 PD 4

1 effect of venue and ambience

Appearance of space Space/Location Training Environment

2 challenges with participants

Participants reactions Impact of training Participant's problems

3 content issues Timing Time constraints Trainer's problems and wishes

4 content issues Trainer issues Logistical constraints/issues

Trainer's problems and wishes

5 technical and gadget wishes Trainer issues

Commitment of participants

Training aids and equipments

6 technical and gadget wishes Trainer issues Equipment on-site


7 supporting material Material

Quality of materials


8 supporting material Material Equipment on-site



Appearance of space

Equipment on-site; Space/Location Training Environment

10 effect of venue and ambience Trainer issues Equipment on-site

Customer's negligence










Appearance of space Space/Location


16 work arounds to ensure quality Material Equipment on-site


17 technical and gadget wishes Material Equipment on-site




19 technical and gadget wishes Participation Equipment on-site


20 work arounds to ensure quality Material

Logistical constraints/issues


21 supporting material Material





23 technical and gadget wishes Trainer issues Equipment on-site


24 technical and gadget wishes Material



25 technical and gadget wishes Trainer issues Space/Location


26 work arounds to ensure quality

Appearance of space

Space/Location; Logistical constraints/issues




28 effect of venue and ambience Trainer issues Space/Location Training Environment





32 challenges with participants Participation

Commitment of participants Participant's problems

33 effect of venue and ambience Trainer issues Equipment on-site








37 technical and gadget wishes Material



38 challenges with participants Participation


39 challenges with participants

Participants reactions


40

improving the training effect for participants Participation


41 effect of venue and ambience Material Equipment on-site


42 improving the training effect Trainer issues

Commitment of participants


for participants


Appearance of space

Commitment of participants; Space/Location Training Environment

44

custumor arrangement issue

Communication upfront Impact of training

Customer's negligence







48 effect of venue and ambience Material Equipment on-site


APPENDIX I: INDIVIDUAL GROUPING OF CUSTOMER NEEDS BY CUSTOMERS

ID Customer 1 Customer 2 Customer 3 Customer 4 Customer 5 Customer 6

1 Challenge Obstacle Ambience Infrastructure anticipatory Environment

2 Constraint Challenge Attitude Trainee Cooperation expectant Result

3 Challenge Obstacle Trainer Preparation

Time Constraint flexibility Plan

4 Enhance facilities Obstacle

Trainer Preparation Infrastructure obstacle Aids

5 Not required fancy Technology Technology comfortability Technique

6 Challenge Obstacle Technology Infrastructure acoustics Issues

7 Enhance facilities Undesirable Comfort Infrastructure expectant Issues

8 Enhance facilities fancy Technology Infrastructure comfortability Technique

9 Challenge Obstacle Ambience Technology compatibility Environment

10 Constraint Obstacle Attitude Trainee Cooperation expectant Issues

11 Enhance facilities Basic Ambience Infrastructure comfortability Environment

12 Enhance facilities Optional

Trainer Preparation Technology comfortability Technique

13 Challenge Obstacle Ambience Infrastructure anticipatory Environment

14 Not required Desirable

Trainer Preparation Infrastructure compatibility Aids

15 Challenge fancy Comfort Technology acoustics Technique

16 Challenge Obstacle Trainer Preparation Infrastructure flexibility Issues

17 Challenge Obstacle Technology Technology flexibility Issues

18 Enhance facilities Desirable Ambience Infrastructure expectant Environment

19 Enhance facilities fancy Technology Technology expectant Aids

20 Not required Obstacle

Trainer Preparation Technology obstacle Aids

21 Challenge Undesirable Attitude Trainee Cooperation anticipatory Conditions

22 Constraint Obstacle Ambience Infrastructure comfortability Environment 23 Constraint Obstacle Technology Technology obstacle Issues

24 Enhance facilities Desirable Comfort Technology expectant Aids

25 Constraint Obstacle Ambience Infrastructure expectant Issues

26 Required Optional Trainer Preparation Infrastructure anticipatory Issues

27 Not required Undesirable Ambience Infrastructure anticipatory Technique

28 Challenge Obstacle Ambience Infrastructure obstacle Issues

29 Enhance facilities fancy Technology Technology compatibility Technique

30 Challenge Obstacle Ambience Infrastructure flexibility Issues 31 Challenge Obstacle Ambience Infrastructure flexibility Issues

32 Challenge Desirable Comfort Trainee Cooperation comfortability Conditions

33 Challenge Obstacle Comfort Trainee Cooperation compatibility Aids

34 Not required Desirable Technology Technology compatibility Technique

35 Enhance facilities Basic Ambience Infrastructure anticipatory Environment

36 Not required Optional Ambience Infrastructure expectant Environment

37 Challenge Obstacle Trainer Preparation

Trainee Cooperation obstacle Issues

38 Constraint Challenge Attitude Trainee Cooperation obstacle Methodology

39 Challenge Challenge Trainer Preparation

Trainee Cooperation expectant Methodology

40 Constraint Challenge Attitude Trainee Cooperation comfortability Aids

41 Constraint Challenge Technology Infrastructure obstacle Issues

42 Constraint Challenge Comfort Trainee Cooperation comfortability Methodology

43 Enhance facilities Basic Ambience Infrastructure compatibility Environment

44 Constraint Challenge Attitude Trainee Cooperation obstacle Result

45 Constraint Basic Technology Infrastructure flexibility Issues

46 Enhance facilities Fancy Technology Technology anticipatory Technique

47 Enhance facilities Basic Ambience Infrastructure anticipatory Environment

48 Enhance facilities Optional Comfort Infrastructure acoustics Aids

how subjective clustering aids affinity diagram in...

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