The purpose of this paper is to find out how consumers constantly trade off the potential extra cost of mass customisation with the additional time they have to wait to receive their customised products.
The authors examine this issue by using conjoint analysis to estimate the trade-offs using a sample of Australian consumers. The authors use cluster analysis to form market segments in the three product categories examined.
The segments demonstrate that there are groups of customers who are quite willing to trade-off price with waiting time. The results have significant implications for Australian manufacturers who are contemplating moving into mass customisation.
Many researchers have investigated the issue of a customer’s readiness to buy a customised product. In particular, they have examined whether customers are willing to pay extra for a mass-customised product, whether they would spend some time to design it, as well as wait to receive it. There has been no study that has examined all three factors simultaneously. The results of this study can help manufacturers form a better understanding of customer willingness for purchasing mass-customised products.
Kalantari, H. and Johnson, L. (2018), "Australian customer willingness to pay and wait for mass-customised products", Asia Pacific Journal of Marketing and Logistics, Vol. 30 No. 1, pp. 106-120. https://doi.org/10.1108/APJML-01-2017-0006Download as .RIS
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According to the Australian Bureau of Statistics (2013), the Australian manufacturing sector has reached its lowest employment level since 1986. One of the main reasons for this decrease is that almost 96 per cent of the businesses in Australia are small size (Clarck et al., 2011). This is owing to a small customer base and the growing difficulty in competing with large international companies that are using mass production and providing products at low prices. Large companies have moved to Asia because of the cheaper labour and ability to manufacture in a mass production system. Normally in mass production, the variety is very low, while the volume and quality are quite high. However, recent studies indicate that customers are no longer happy with mass produced products. Hence, companies are shifting from mass production to mass customisation/personalisation to satisfy customers’ needs and desires (Daaboul et al., 2011; Jafari et al., 2015; Kuo, 2013). Hatton-Jones and Teach (2015) believe that Australian customers are motivated to engaged in do it yourself tasks. One of the main challenges of mass customisation is the product’s delivery to the customer after its manufacture. For example, at Adidas, the manufacturing of customised shoes starts after a customer places his order online or through one of its retailers. This means customers cannot collect their purchases from the store upon payment; it takes time to manufacture and deliver the product to the customer. Customers may need to wait several days or weeks to receive their customised products. To keep the delivery time as short as possible, manufacturers use express courier services to distribute the finished customised products to the end customer. This delivery costs a lot for the manufacturers, as well as the customers having to wait a long time to receive their customised products. In this situation, a local manufacturer has the advantage of a closer proximity to the customers while also offering customised products. Therefore, local manufacturers are in a good position to grow their business and compete with the international market. To sustain a successful mass customisation system, the whole supplier network should be willing to work towards a mass customisation system. This would mean being situated as close as possible to each other as well as their consumers. That is, they would be able to deliver the raw material and finished product on time and efficiently (Feitzinger and Lee, 1996; Kotha, 1996; Manenti, 2014). Some researchers believe that in the near future manufacturers will move to their origin countries such as Europe and the USA to increase the profit which they earn from domestic sales (Huyett and Viguerie, 2005; Grant Thornton, 2006; Wan and Bullard, 2008; Gownder, 2011; Piller et al., 2012). We believe that manufacturing can also move back to Australia from cheap labour countries if our national and local manufacturers move to mass customisation.
The question here is, are Australian customers ready to purchase customised products? Mass customisation has many benefits for customers such as better fit, having a unique product, the rewarding and enjoyable process of designing the customised products and creating a pride of authorship. On the other hand, its disadvantages are not so slight including longer delivery time, higher price and customers may have to spend some time to design and create the customised product. Mass-customised products, due to their individual design and complicated production process, may cost more than a standard product. Although theoretical debates suggest that mass-customised products can be produced at the same price as mass production products, manufacturers who have implemented mass customisation indicate that mass customisation is a major reason for increasing the cost (Kotha, 1995; Hart, 1995; Huffman and Kahn, 1998; Krueger and Hergeth, 2006; Boucher and Barnett, 2008). Generally, by increasing the level of customisation the final price and the delivery time will be increased (Kahn, 1998; Jiao et al., 2003; Millard, 2006; Moser et al., 2006).
The objective our research is to evaluate customer readiness to buy customised products and also assess consumer preferences. This research looked at the three inconveniences of mass customisation simultaneously. We examined three different customised products (jeans, running shoes and sunglasses), aiming to find answers to the following questions:
Are Australian consumers willing to pay extra for purchasing customised goods?
Are they willing wait longer to receive their customised products?
Are they influenced by the degree of customisation?
Are they willing to trade off price and delivery time?
Are there group of customers for whom different factors are more important?
Customers are growing less happy with mass produced products. To satisfy customers’ needs and desires, companies are shifting from mass production to mass customisation/personalisation production (Daaboul et al., 2011; Jafari et al., 2015; Kuo, 2013). Before manufacturers move to mass customisation, they should consider customers’ readiness to purchase customised products. Customer readiness can be examined by considering three main inconveniences of mass customisation: higher price; longer delivery time; and the time that customers need to spend to design the product (Bardakci and Whitelock, 2004).
The extra real or perceived value from mass-customised goods leads to a higher price for the products (Kotha, 1995; Hart, 1995; Huffman and Kahn, 1998; Krueger and Hergeth, 2006; Boucher and Barnett, 2008). However, customers’ willingness to pay a higher price is confined; in particular, depending on how much consumers’ perceive value from the mass-customised product (Huffman and Kahn, 1998; Lihra et al., 2008). As a result, the premium price should be realistic and in balance with the supposed value added by consumers. In addition to price, manufacturers should consider the importance that customers attach to the delivery time of their expected goods. In other words, for many customers, there is a limitation on the delivery time of their customised products (Duray et al., 2000; Da Silveira et al., 2001; Tkindt, 2002; Du et al., 2006). Furthermore, the time that customers are willing to spend on designing their own customised product is also limited (Jiao et al., 2003; Kahn, 1998).
Customer readiness to purchase a customised product has received some attention from researchers. Franke and Piller (2004), Schreier (2006), Endo and Kincade (2008), Franke et al. (2010), Kuo and Cranage (2012) and Li and Unger (2012) have focussed on customers’ willingness to pay a premium. Schoder et al. (2006) studied the customers’ willingness to pay and also the time spent to designing the individualised product. Aichner and Coletti (2013), Spaulding and Perry (2013) and Mehra et al. (2015) researched the customers’ willingness to pay a premium and wait longer for customised products. Frank et al. (2002), Kamali and Loker (2002), Bardakci and Whitelock (2004) and Lee et al. (2012) considered all three elements. However, their studies considered the three elements separately and they did not evaluate the trade-off between price and delivery time. To the best of our knowledge, there has been no study that has examined willingness to pay and wait simultaneously. We examined relative trade-off between price and waiting time for a range of mass-customised products categories. We used conjoint analysis and found the interaction between price premium and delivery time on customer willingness to buy customised products. The potential to use conjoint analysis to determine customers’ preferences and relative importance of variables is significant (Kelley et al., 2015). The study adds to the existing knowledge on customer-choice behaviour by capturing the complicated trade-off in which consumers have to choose among customised and standard products. Our research not only reveals the importance of price, delivery time and type of products, but also shows the most preferred levels as part of a customer’s actual decisions. In our study, to decrease the number of the attributes in the conjoint analysis, we decided to include the “design time” as one of the characteristics of the customised and full customised products.
To examine the customer readiness for mass customisation, three different product categories were chosen. Based on Aichner and Coletti’s (2013) study, the top three categories in which customers are willing to buy customised products are presents, clothing and footwear. We have chosen jeans, running shoes and sunglasses due to their popularity and previous research. Jeans were chosen from the clothing category. Some researchers such as Shin and Istook (2007), Goldsberry et al. (1996), Ashdown and Loker (2010) and Mason et al. (2008) have been working on the fit of apparel, especially jeans. They all believed that the current systems of apparel fit do not cover a full range of body types. Running shoes have been selected from the footwear category. Endo and Kincade (2008) have been working on mass-customised shoes and they contend that customers are willing to pay a premium price for mass-customised shoes. Frank et al. (2002) in the EuroShoe project have considered premium price, longer delivery time and design time; yet, as mentioned earlier, the interaction between them has not been considered. Moon et al. (2008) have worked on customised sunglasses and they found out that consumers are willing to buy customised sunglasses online paying up to 30 per cent higher than the price of standard ones. The delivery time and effort for designing the product have not been considered in this research.
Conjoint analysis has been used in marketing research since 1971 (Green and Rao, 1971). Conjoint analysis is about measuring systematic utility to better understand which kind of products customers prefer and what it is about those products that makes people prefer them. The term “conjoint” in conjoint analysis is used to show that in this method different attributes are considered jointly; they need to be considered all at the same time. Conjoint analysis is “a multivariate technique that estimates the relative importance consumers place on different attributes of product or service, as well as the utility or value they attach to the various levels of each attribute” (Hair et al., 2008, p. 587). In conjoint analysis, the researcher defines different attributes with their levels for a product.
In the marketing area, most strategies are based on consumer segmentation. Hence, we use cluster analysis to form market segments that help identify the groups of customers for whom different factors carry varying importance. Cluster analysis has been employed broadly in segmentations based on psychographics, benefit-seeking or conjoint part-worth utilities (PWU) (Krieger and Green, 1996). The segments demonstrate that there are groups of customers who are quite willing to trade-off price for waiting time. A hierarchical method called Ward’s procedure combined with the squared Euclidean distance measure has been employed to determine the number of clusters in cluster analysis.
Combining these two methods has multiple benefits including the questions resembling real-life purchasing situations and reducing the number of socially desirable answers. Based on this, we can define different market segments, thereby increasing the comparability of outcomes of different research. We can furthermore use this approach in planning new product launches and within emerging and developing markets (Djokic et al., 2013).
Attribute selection is the first step in conjoint analysis. Attributes in this study have been chosen according to customer readiness for mass customisation. Since the aim of this research is to compare customised products with standard products (an already existing product in the market), it should be considered in the product attributes. As a result, we use three attributes for this study: price, delivery time and the degree of customisation (none (standard), customised and full customised). As the time for designing the customised and full customised products is constant and for each product is different, we define a designing time for each product in the questionnaire. By doing that we consider that the respondents are aware of the designing time and they are willing to spend that amount of time for designing their product.
After selecting the attributes for this study, we need to choose the levels for each attribute. By using brainstorming, literature review and internet search, we were able to provide the most relevant levels for each attribute. Table I shows the attributes and levels for this conjoint study.
SPSS/PASW Conjoint 24 was used to design the conjoint experiment. Our conjoint studies contain three attributes. One of these attributes has three levels, and the other two have four levels, creating 48 possible profiles to be rated by participants which is no easy task. One way to reduce the number of the profiles is to use a fractional factorial design, which uses a fraction of all possible profiles. This fractional factorial design (an orthogonal design) can be designed by computer software such as SPSS. Orthogonal designs consider only the main effects of the attribute levels and consider their interactions unimportant. By using an orthogonal design, the number of profiles that respondents need to rate is significantly reduced (16 in this case). In addition, three more profiles were added to the profiles as holdouts. Holdouts help to determine the predictive power of the model and also to validate the result of the conjoint analysis (Rao, 2014).
4.2 Data collection
An online questionnaire was developed for capturing the participants’ ratings for each product separately. A short introduction put participants in the context of purchasing each of the three product. Since the target participants for this research were Australian adults 18 years old or older who have been wearing or using each of the products, the first three questions inquired about participants age, locations and whether they have experience using the products. Later, a short introduction explained the term and concept of the survey. Then, four questions were asked to address the following socio-demographic information: gender, age, salary and state that they are living.
Before conducting the main study, pilot studies were conducted to pre-test the questionnaire. The survey was sent to 40 students in the faculty of engineering. In the pre-test, on average it took less than 6 minutes for participants to complete the survey as expected. The importance of each attribute varied among the participants, which is consistent with what we expected from conjoint analysis results. The pre-test also provided confidence that the questions and instructions were clear and understandable. The feedback from participants also showed that the survey was easy to complete and follow. In conclusion, there were no major changes to the survey, its questions and instructions. The main survey was conducted during a week in March, 2016. Australian participants who were 18 years old or more participated in the survey through an online panel website (Researchnow). We collected 430 completed surveys. The socio-demographic information of the participants are shown in Table II.
4.3 Data analysis
SPSS/PASW Conjoint 24 was used to calculate the PWU for each attribute level. Then, relative importance of each attribute was calculated as a function of these PWU.
Importance scores show the influence of the attributes. The higher importance scores show the higher influence in the participant’s decision-making process. Utility scores, which are also produced in conjoint analysis, provide details about a participant’s desirability of the levels.
One common way to assess validity and reliability of conjoint analysis is evaluating the goodness of fit of the estimated model (Malhotra, 2008). Pearson’s r and Kendall’s τ statistic have been used to measure goodness of fit between the data and the model (Malhotra, 2008). After testing the data by using Kendall’s τ, 367 responses for jeans, 353 responses for running shoes and 341 responses for sunglasses were eligible for this study. After removing the unusable data (data which had a weak goodness of fit), the PWU were used to simulate the market and find out the market share of each simulated profile. Later, the PWU for each participant were used for cluster analysis (Green and Krieger, 1991).
In this study, cluster analysis is combined with conjoint analysis results to help answer one of the research questions:
Are there groups of customers for whom different factors are more important?
Conjoint analysis reveals participants’ preferences and cluster analysis allows those participants to be segmented based on their preferences. Knowing the existence of different segments and also their preferences can helps marketers and researchers develop and use a suitable marketing strategy. A hierarchical method called Ward’s procedure combined with the squared Euclidean distance measure has been used to determine the number of clusters in cluster analysis. This method was suggested by Hair et al. (2010) and Perera (2008). After finding the number of clusters, each cluster’s membership needs to be determined. The K-means method was used to allocate each participant to a cluster.
5.1 Conjoint analysis
We conducted three rating-based conjoint analyses to investigate consumer’s preference for three attributes in the context of purchasing three different products: jeans, running shoes and sunglasses. Simulation was run, first, to show the predictive power of the model, and second, to estimate the market shares of the simulated profiles.
PWU of each level and relative importance of each attribute were computed by using SPSS/PASW Conjoint 24 and are reported in Table III.
As reported in Table II, the majority of PWU have a high standard deviation which indicates lack of consensus among the participants. Based on Green and Rao (1971), the existence of segments among participants is one of the main reasons for having a high standard deviation. Cluster analysis can be used to address this issue. The results of the jeans survey show that price is the most important attribute followed by degree of customisation and delivery time. The PWU increase along with the degree of customisation. Full customised has the highest PWU. For both the price and delivery time attribute, by increasing the price and delivery time, the PWU decreases. In the running shoes survey result, degree of customisation has the highest relative importance. Full customised has the highest PWU, followed by customised and standard. By increasing the delivery time and price, the PWU decreases. In the sunglasses survey, price is the most important attribute among the participants. The increasing PWU and increasing the degree of customisation clearly show that the participants prefer a customised product. Furthermore, this validates the relationship between the customisation and consumer’s preferences. Hence, manufacturers can see that there is a high preference for the customised products.
5.2 Cluster analysis
According to the conjoint analysis results, the standard deviations of the PWU of the levels seemed high. This may be owing to the number of clusters in the sample. To confirm this, a cluster analysis was conducted (Green and Krieger, 1991). The results of the cluster analysis show that there are three clusters in the jeans and sunglasses survey sample and four clusters in the running shoes survey sample. After identifying the number of clusters, K-mean cluster analysis was conducted to identify the members of each cluster. The PWU and relative importance of each cluster are reported in Tables IV-VI.
After the cluster analysis, the average standard deviation of the PWU decreased by more than 50 per cent for all of the products:
Jeans: based on results of the cluster analysis, Segments 1, 2 and 3 represent 42, 39 and 19 per cent of the total participants, respectively. Participants in Segment 1 placed the greatest importance on degree of customisation (39.0 per cent). Attribute price (36.6 per cent) is the second most important attribute for the first segment. The relative importance of the degree of customisation and price are very close to each other. Although delivery time was the third most important attribute, it still has a high relative importance of 24.4 per cent. Cluster 1 could be labelled as “Rational, Value for Money” as the participants in this segment consider all of the attributes when they make their decisions. Cluster 2 represent “Patient and Rational Buyers” with a relative importance of 34.2 per cent for attribute degree of customisation. It also reported 49.4 per cent for the attribute price and a low relative importance of 16.4 per cent for the delivery time attribute. This indicates that in this group, participants are influenced by the price and degree of customisation and care much less about the delivery time. Cluster 3 was labelled “Budget Conscious Shoppers”, since they placed price as the most important attribute by a very high relative importance of 69.7 per cent. The other two attributes have a very low relative importance of 17.7 and 12.6 per cent for degree of customisation and delivery time, respectively.
Sunglasses: out of 341 participants for the sunglasses survey, 54 (16 per cent) participants were in Cluster 1, 210 (61 per cent) were in Cluster 2 and 77 (23 per cent) were in Segment 3. Delivery time is the most important attribute for Cluster 1 members, followed by degree of customisation and price. Hence, Cluster 1 could be coined “Impatient Customisation Lovers”. This group of customers just want a customised product (preferred full customised) with a short delivery and the price of the product does not influence their decision that much. The second and third clusters of the sunglasses participants are much the same as the first and third clusters for jeans. Hence, these clusters could be also labelled as “Rational, Value for Money” and “Budget Conscious Shoppers”, respectively.
Running shoes: participants in the running shoes survey were grouped to four different clusters. Out of 353 participants for running shoes survey, 120 (34 per cent) participants were in Cluster 1, 64 (18 per cent) were in Cluster 2, 34 (10 per cent) were in Cluster 3, and 136 (0.39 per cent) in Segment 4. Cluster 1 represented the “Customisation Lovers” based on their characteristics. Members of this clusters placed degree of customisation in first place by a quite high relative importance of 50.8 per cent. The profile of Cluster 2 could be labelled “Impatient Buyers” as relative importance of delivery is by far the most important attribute (52.8 per cent). The third and fourth clusters of the running shoe survey have the same trend as the third and first segments of the jeans survey. As a results, these two clusters are labelled as “Budget Conscious Shoppers” and “Rational, Value for Money”, respectively.
5.3 Market share simulation
Simulations have been run twice for two different hypothetical jeans. In the first simulation, full customised jeans (A) with the price of $115 and delivery time of 12 days vs standard (B) jeans with the price of $85 and the delivery time of 4 days. Table VII shows that 41.1 per cent of the customers are willing to buy a full customised product (B) with the above details while the other 59 per cent willing to buy the standard jeans (A). Even though the price of the profile A is 35 per cent more than the profile B and it takes three times longer to deliver product A compared to product B, more than 40 per cent of the participants are willing to purchase the full customised jeans. In the second simulation, the price was increased to $145 and the delivery time of the full customised jeans was decreased to eight days. As a result, only about 6 per cent of the customers change their mind and were willing to buy the standard product. Despite the price increase, the result shows that the percentage did not significantly change. This might be because participants are willing trade-off the increased price for a decreased delivery time.
Two simulations were also run for running shoes for two products. First, a full customised pair of running shoes (C) with the price of $140 and three weeks’ delivery time. Second, a standard product (D) with a price of $120 and four days’ delivery time. The result shows that 45.6 per cent of the customers were willing to buy the full customised running shoes and 54.4 per cent were willing to purchase the standard one. Even though the price of profile C is 16 per cent more than profile D and takes more than five times longer to deliver, more than 45 per cent of the participants were willing to purchase the full customised running shoes. In the second simulation, the price of the full customised running shoes is increased by 30 per cent but the delivery time is halved. The results of the simulation show that the percentage of customers for the full customised running shoes did not change. This not only demonstrates that customers are willing to pay extra for full customised running shoes, they are also willing to trade $20 for 1.5 weeks.
Two simulations were also run for sunglasses. In the first one, full customised sunglasses (E) with the price of $105 and the delivery time of nine days were compared with standard sunglasses (F) with the price of $85 and the delivery time of three days. The result of the simulation shows that 35.8 per cent of the participants are willing to buy the full customised sunglasses. In the second simulation, the price was changed to $125 and delivery time was changed to six days. A decrease of about 7 per cent in customer willingness to buy full customised product can be observed when the price and delivery changed. Based on these results, 27.6 per cent of the participants were still willing to pay extra for the full customised sunglasses.
In mass customisation, customers receive a product or service that fits their individual requirements. Therefore, by employing mass customisation, manufacturers are able to increase the perceived value of their goods and/or services and also the business’s profitability (Davis, 1989; Pine and Victor, 1993; Huyett and Viguerie, 2005; Grant Thornton, 2006; Lihra et al., 2008; Wan and Bullard, 2008; Manenti, 2014). According to Fogliatto et al. (2012), many industries (including food, electronics, large engineered products, mobile phones, personalised nutrition, homebuilding, furniture and foot orthoses production) and many manufacturers (such as Dell computers and NIBC bicycles) have implemented mass customisation successfully. Having said this, prominent researchers (Piller and Tseng, 2010; Daaboul et al., 2011; Salvador et al., 2009; Piller, 2007; Blecker and Friedrich, 2006; Da Silveira et al., 2001) have illustrated that moving to mass customisation from mass production is a challenging task. Hence, one of the most important steps before moving to mass customisation is to assess the consumers and see whether they are ready for mass-customised products. This can be assessed by measuring their willingness to pay a premium price, wait longer to receive the product and also spend some time to design the customised product. Studies by Huffman and Kahn (1998), Hart (1995), Lihra et al. (2008), Schreier (2006) and others have demonstrated that customers are willing to pay a premium and also willing to wait longer, yet they have not examined the customers’ interaction between delivery time and price.
In this study, we show that there are customer segments in the Australian market that are interested in customised products and most importantly, they are willing to pay a premium price and wait longer to receive their customised products. Manufacturers in Australia, therefore, have an opportunity to target the segments that are willing and ready to purchase a customised product. However, manufacturers should not only rely on this research outcome, but also consider other challenges such as manufacturing the customised product, supply chain and their relationship with retailers. Based on the results of the market simulations, participants are willing to trade-off price and delivery time, hence manufacturers are able to balance the price by delivery time based on their customers’ needs.
According to Lihra et al. (2012), Oh et al. (2008) and Franke and Piller (2004), some consumers believe that seeing and touching the actual product is very important to them. Thus, it is an invaluable investment to develop a new framework and advanced systems for mass customisation that consider all aspects from manufacturing to selling the product. The system should be able to collect the information from consumers through a reliable and safe internet server, and manufacture a customised product with a batch size of one with suitable quality control. Furthermore, the system should aim to speed up the manufacture of the customised product, the standardisation and modularisation of the products and process. Whether to postpone the assembly of the customised parts of the products at the end of the manufacturing process is another important consideration.
Our research for the Australian market shows that the consumers’ willingness to pay and wait extra and spend time for designing customised products. Our analyses also indicated that customers are willing to trade-off price and delivery time. Moreover, the study provides a simulation model for manufacturers to simulate the market share of the current standard products vs customised products, with different price and delivery time. Hopefully, the outcome of this research, along combined with similar studies, can help domestic manufacturers to grow and enjoy a profitable market in the future.
7. Summary and conclusions
The goal of this research was to measure customer willingness to purchase customised products (jeans, running shoes and sunglasses) by considering their willingness to pay a premium price, wait for a longer delivery time and also spent some time to design the product. Using conjoint analysis and market simulation, we demonstrate the customer’s trade-off between delivery time and price.
The relative importance of each attribute allowed us to rank the attributes based on their influence on customers. In the jeans and sunglasses cases, price was the most importance attribute followed by degree of customisation and delivery time. In the running shoes case, degree of customisation was the most important attribute. A cluster analysis showed that there are three clusters for jeans and sunglasses and four clusters for running shoes. Different attributes influence each segment differently in the purchase of a customised product. Moreover, the segments were labelled based on their members’ overwhelming shared preference. A segment that highly considers all the attributes when they purchasing a customised product was labelled as “Rational, Value for Money”. A segment in which participants were driven by price was called “Budget Conscious Shoppers”. “Impatient Buyers” was the label given to the segment where delivery time had the high relative importance. “Patient and Rational Buyers” was the cluster that gave high relative importance to price and degree of customisation. The last segment’s profile is “Impatient Customisation Lovers” who attached a very high relative importance to degree of customisation and delivery time, as well as high PWU for a full customised product.
This study makes several contributions to the mass customisation literature. The current study complements and extends previous studies by analysing consumer preferences and choices – specifically of a customised product – as well as their readiness to buy a customised product. Customer readiness has not been thoroughly investigated in the Australian context. Although many studies have shown the impact of price and delivery time on customer readiness to purchase a customised product, they have not considered the trade-off between price and delivery time. In this study, we simultaneously looked at the attributes of delivery time and price, which are important in customer readiness for purchasing a mass-customised product. We showed that customers are willing to trade-off price and delivery time. We also found that the decision-making process for different customised products is different. For some products, participants pay more importance on the price rather than delivery time and degree of customisation. Then again, for other products, degree of customisation is the most important attribute.
This study also shows that Australian consumers can be segmented, particularly in the customisation process. Hence, this paper makes a conceptual and empirical contribution by identifying customers with different needs and requirements. The results show that there are groups of customers who are willing to pay extra and wait longer to receive a customised product, while some groups are not willing to pay extra or wait longer or both.
Although we have chosen a wide range of products to be able to generalise the results of this study, there are still some limitations that restrict the generalisation of the outcomes. As such, there is opportunity for future research. For instance, although the sample size of the participants is acceptable, higher numbers of respondents would lead us to more accurate results. Moreover, as we used full-profile conjoint analysis, a large number of attributes would have led us to have more profiles and a large number of profiles make rating harder for the participants. Hence, we have chosen only the three most important attributes. As a result, some attributes, such as time spent on the designing of the product, were merged with another attribute.
Future research can also be conducted by including more attributes. Due to the size of the study, we have considered only three attributes. Researchers may later find and consider more attributes that influence consumers’ decision-making processes when purchasing mass-customised product. Moreover, we defined three to four levels for each attribute. For example, three levels were defined for customisation. In the real world, the degree of customisation can be much broader.
Attributes and levels for conjoint study
|Degree of customisation||None, customised, full customised||None, customised, full customised||None, customised, full customised|
|Price||$85, $115, $145, $175||$120, $140, $160, $180||$85, $105, $125, $145|
|Delivery time||4 Days, 8 Days, 12 Days, 16 Days||4 Days, 1.5 Weeks, 3 Weeks 4.5 Weeks||3 Days, 6 Days, 9 Days, 12 Days|
Participants’ socio-demographic information
|Options||Jeans (%)||Running shoes (%)||Sunglasses (%)|
|Less than 25 k||13||18||20|
|More than 101 k||17||11||12|
Part-worth utilities and relative importance for all three products (aggregate)
|Attribute/levels||RI % (Rank)/PWU||SD||RI % (Rank)/PWU||SD||RI % (Rank)/PWU||SD|
|Degree of customisation||33.19 (2)||–||39 (1)||–||32.4 (2)||–|
|Price||47.81 (1)||–||34.8 (2)||–||44.1 (1)||–|
|Delivery time||19.0 (3)||−||26.2 (3)||−||23.5 (3)||−|
Notes: RI, relative importance; PWU, part-worth utility
Part-worth utilities and relative importance of all clusters for jeans
|Relative importance/part-worth utility|
|Attribute/levels||Cluster 1 (n=154, 42%)||SD||Cluster 2 (n=145, 39%)||SD||Cluster 3 (n=68, 19%)||SD|
|Degree of customisation||39.0%||–||34.2%||–||17.7%||–|
Part-worth utilities and relative importance of all clusters for sunglasses
|Relative importance/part-worth utility|
|Attribute/levels||Cluster 1 (n=54, 15.8%)||SD||Cluster 2 (n=210, 61.6%)||SD||Cluster 3 (n=77, 22.6%)||SD|
|Degree of customisation||33.9%||–||35.7%||–||23.4%||–|
Part-worth utilities and relative importance of all clusters for running shoes
|Relative importance/part-worth utility|
|Attribute/levels||Cluster 1 (n=120, 34%)||SD||Cluster 2 (n=64, 18%)||SD||Cluster 3 (n=34, 10%)||SD||Cluster 4 (n=136, 38%)||SD|
|Degree of customisation||50.8%||–||23.3%||–||21.5%||–||41.5%||–|
Conjoint attributes and preference probabilities of simulation for the aggregate sample
|Simulation 1||Full customised $115
|Full customised $140
|Full customised $105
|Result simulation 1||41.1%||58.9%||45.6%||54.4%||35.8%||64.2%|
|Simulation 2||Full customised $145
|Full customised $160
|Full customised $125
|Result simulation 2||35.4%||64.6%||45.9%||54.1%||27.6%||72.4%|
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