Influencing factors of online furniture purchase behavior based on analytic hierarchy process

Yu, C., Liu, W., Fei, Y., Chen, J., and Hu, Z. (2023). “Influencing factors of online furniture purchase behavior based on analytic hierarchy process,” BioResources 18(2), 2857-2873.

Abstract

With the continuous development of the Internet economy, online furniture sales are increasingly becoming an important channel for consumers to purchase furniture. In order to investigate the influencing factors of consumers’ preference for purchasing furniture online, this paper aims to find out the influencing factors of online furniture consumption and their weight relations by using the literature research method and Delphi method to clarify the influencing factors of online furniture consumption, followed by the Analytic Hierarchy Process (AHP), from the Target Level, Criterion Level, and Sub-criterion Level. Weight analysis was carried out on the influencing factors of online furniture consumption. The three primary indicators included Personal Factor, Product Factor, and Service Factor. The 16 secondary indicators included Personality Traits, Revenue, Occupation, etc. An index system model was established for online furniture consumption based on the above factors. It was concluded that consumers are most influenced by the price of furniture among Product factors when choosing online furniture. The findings were consistent with the AHP model data when tested using a grey prediction model. The data from this study can therefore provide an important reference for online furniture product development and marketing promotion.

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Influencing Factors of Online Furniture Purchase Behavior Based on Analytic Hierarchy Process

Changlong Yu, Wei Liu,* Yinan Fei, Jiaqi Chen, and Ziyan Hu

DOI: 10.15376/biores.18.2.2857-2873

Keywords: Online furniture; Influencing factors; AHP; Weightings; Grey Forecasting Model

Contact information: College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China; *Corresponding author: liuwei@njfu.edu.cn

INTRODUCTION

Online shopping has been considered one of the influential factors in the development of the internet, initially led by the United States, where companies used internet platforms to post information about their products and thus attract customers to their physical shops. This approach allowed consumers to choose products online and then go offline (physical shops) to experience the process of purchase and thus reduce the risk associated with consumption (Rampell 2010). With the improvement of logistics level, the rise of a new generation of online payment represented by Alipay in China (Ge et al. 2018), which has also led to the transformation of online shopping, coupled with the global outbreak of COVID-19, the wood processing industry and furniture manufacturing enterprises in various countries have fallen into a downturn of varying degrees. Taking central European countries as an example, Croatia and Slovenia have experienced huge disruptions in the market lockdown under the influence of the epidemic (Kaputa et al. 2018), prompting a large number of local small and medium-sized furniture enterprises to go bankrupt, and as a result, online furniture sales began to grow rapidly (Barcic et al. 2018). “Online Shopping” has become one of the main means for consumers to choose how to shop (Adibfar et al. 2022). According to the survey, before 2019, online shopping accounted for 10 to 15% of the overall retail sector in the US, with overall mostly offline shopping. However, since March 2020, consumers on Amazon in the United States have spent more than 35% in the same period compared with the amount in the previous year, and Statista data from Germany in the same year shows that online furniture sales in the United States have accounted for 27% of this market, of which Amazon has become the world’s largest e-commerce furniture trading platform. This has accounted for 33% of cross-border e-commerce (Fig. 1) (National Bureau of Statistics of China). As the Internet economy continues to mature and the logistics system continues to improve, the furniture industry has also gradually adapted to the e-commerce marketing model, with furniture as the representative of large durable goods. In sum, by launching a new model of online shopping, Internet businesses provide consumers with standardized goods and services through an online platform, while the traditional sales-oriented physical shops are upgraded to a set of product display, consumer experience, pre-sales, and after-sales service as one of the new Internet shops (Ge et al. 2018).

Since May 2020, e-commerce furniture sales have started to grow continuously, and the market share of online furniture brands in China has reached over 80% (Alibaba Research Institute 2020), while the number of physical furniture shops has been declining year by year, from 179 in 2016 to 149 in 2020 (National Bureau of Statistics of China). At the same time the scale of the furniture industry is still expanding (Xiong et al. 2017), and industry clusters and furniture industry features are gradually taking shape (Steinhoff et al. 2019; Kumar et al. 2021). The development of the Internet has become a big trend in the furniture market, which is gradually becoming an important part of the online shopping strategy (McBride 1997; Sadovski et al. 2002). It is predicted that the number of users buying furniture online will increase (Vin et al. 2019; Ariadne Consulting Series Research Report 2022), and that the sales platform of the furniture industry will no longer be limited to a single offline model, but that it will develop more in the direction of “E-commerce” and “Online” (Chen and Yu 2021; He 2022).

Online furniture sales platforms are gradually becoming subdivided into three categories: (1) branded online self-owned platforms: brand manufacturers set up official stores in the form of E-commerce platform to achieve direct access to the official store to sell corporate products to front-line customers, and it is also necessary to pay a specific percentage of the order fee to the platform. The brand manufacturers control the pricing power of goods. Examples include IKEA, Nitori, Herman, Universal Furniture, etc. (2) The brand stationed in the integrated E-commerce platform: this model is the E-commerce platform from the brand factory to buy goods, where there is selling on the platform, with the right to decide the price of products to achieve the platform self-owned sales, such as Amazon, AliExpress, e Bay, Tmall, Suning, etc. (3) Third-party agents sales E-commerce platform: this method is a third-party merchant into the e-commerce platform and set up stores, from the brand manufacturers to buy goods and thus through the store to sell goods. There is still the same need to pay a specific percentage of the order fee to the platform and possess the right to determine the selling price of goods, such as My Home Furniture Purchase APP (Guan 2021).

With increased sales, new problems arise. In the literature on “consumers buying online furniture” that were reviewed in the big data search engines of CNKI and WOS, a large number of articles show that consumers have chosen online furniture platforms to decide whether to purchase, e.g., in the literature report on online furniture purchasing behavior, some studies focus on the effects of gender, age, income, and other variables on those who have chosen to buy (Li et al. 2012) and the analysis of consumer populations more influenced by the segmented group’s intention to have bought (Hakala et al. 2015; Medeiros et al. 2016). However, the literature on which factors influence buyers’ choice of online platforms is scarce, and consumers are faced with the intricacies of furniture E-commerce platforms and online furniture. When facing the intricate furniture E-commerce platforms and online furniture products, they often have difficulties in purchasing and hesitation in choosing, etc. In this regard, to help furniture E-commerce platforms and furniture enterprises better understand consumers’ consumption demand for online furniture, this paper uses Analytic Hierarchy Process (AHP) to construct an index system to systematically study the influencing factors of online furniture consumption and provide scientific references for the optimization of furniture E-commerce business models, furniture E-commerce platforms, and online furniture product development.

The structure of this paper is as follows. Firstly, the preamble background of this paper and the research method is introduced, and the system framework of AHP is determined by the Delphi method. Secondly, it uses questionnaires of experts to collect data. The ranking of weights is also used to provide rigorous data support for the paper. Moreover, optimal suggestions are made based on the framework. Finally, the paper summarizes the conclusions drawn from the review and provides suggestions for further research.

Fig. 1. Share of each e-commerce furniture sales platform in the German Statista database in 2020 (Federal Statistical Office 2020 database)

ONLINE FURNITURE CONSUMPTION INFLUENCE FACTORS INDICATORS

Online Furniture Basic Characteristics

The basic consumer procedure for online furniture is as follows: the consumer uses the platform to place orders, the E-commerce platform is used to take orders, factories supply the furniture, brand warehouses handle delivery, logistics must be coordinated, and after-sales service is needed. Its main characteristics are heavy furniture mass, difficulty to transport, high cost, a wide range of furniture, a wide choice with the possibility of providing individual customization, ease of operation, etc. Therefore, the problems based on the correct handling of the above characteristics have also become an important indicator for consumers to consider whether to have online furniture purchase or not (Li et al. 2020; Zhang et al. 2022).

Consumption Influence Factor Index Determination Method

The establishment of the online furniture consumption influence factor index system is the prerequisite for this paper’s research. Firstly, the content of online furniture consumption influence factors was comprehensively sorted out through the literature research method, and then the basic index system framework was derived from the analysis of the five purchase factors of potential furniture buyers due to the need identification, information search, alternative evaluation, purchase decision, and post-purchase behaviour by the relevant researchers using the hierarchical analysis method (Oblak et al. 2017). Then the Delphi method was used to collect experts’ opinions and determine the basic indicators (Brady 2015) to understand the basic situation of online purchasing platforms and the constituent factors of online item purchase (Wang 2014; Chen et al. 2022). A further goal was to understand how online and offline consumption will be influenced by its related factors, leading to different choice results (Wieland 2021). The research results were eventually summarized and adjusted according to the literature on the online furniture market (Zhao 2016; Li 2022; Wang and Yang 2022) and the current state of buying by re-planning the influencing factors and using the indicator system of the existing literature as a reference. In this paper, expert users should be sought from marketers, business people, and consumers to make the research results accurate and objective. To summarize the viewpoint the authors interviewed 10 professionals, including 3 people with online furniture sales experience, 2 people from online furniture manufacturing companies, and 5 experienced consumers of online furniture (Table 1).

Table 1. Expert Knowledge Areas

Fig. 2. Competency distribution of the three types of professionals

Subject to the interplay between producer-seller-consumer (Purcarea et al. 2010), relevant integrated areas were identified for the 10 experts, and Fig. 2 shows the distribution of competencies in each category, reflecting their research directions in different areas and their relevant strengths.

Judgment Index Establishment

The above method resulted in 3 Primary Indicators: Personal Factor, Product Factor, and Service Factor; 16 Secondary Indicators: Personality Traits, Revenue, Occupation, Personal Preferences, Furniture Price, Furniture Quality, Furniture Material, Furniture Size, Types of furniture, Furniture Brand, Merchant Credibility, Web Factor, Convenience Factor, Payment Factors, Logistics Factors, and After-sales Factors. The details are shown in Table 2.

Table 2. Judgment Index Establishment

METHOD

The Analytic Hierarchy Process (AHP), proposed by American operations researcher T. L. Saaty of the University of Pittsburgh in the early 1970s, is a simple, flexible, and practical multi-criteria decision-making method for quantitative analysis of qualitative problems (Vaidya et al. 2006). Its main features are a balanced interpretation from many problems (Khaira and Dwivedi 2018), a comprehensive overview of the problem by considering all appropriate criteria and treating the object of study as a system. This is done in accordance with decomposition, comparative judgement, and synthesis of the way of thinking for decision-making. This is becoming an important tool for systems analysis developed after mechanistic analysis and statistical analysis. Secondly, this approach neither pursues mere mathematics nor focuses one-sidedly on behaviour, logic and reasoning, but organically combines qualitative and quantitative methods to decompose complex systems, making the results simple and clear and easily understood and grasped by the reader. Finally, this idea is able to deal with many practical problems that cannot be approached with traditional optimization techniques, and it is a more effective way of modelling the brain’s decision-making process. The approach is illustrated in a flowchart (Fig. 3).

Fig. 3. The AHP flow chart

The AHP method has been widely used in the areas of timber, furniture, sales, and online shopping. For example, Kaushik used this method for identification analysis in his study of online fashion retailing in India to identify factors that influence consumer preference for online fashion retailers (OFR) (Kaushik et al. 2020). Yilmaz also used the AHP method when considering the ranking order of consumer-preferred shopping sites to determine the weighting factors that influence consumers’ choice of website (Yilmaz 2022). Ojurovic analyzed the key factors of competitiveness in wood processing and furniture production (Ojurovic et al. 2013). Singer proposed an interval Pythagorean fuzzy AHP hierarchical process model to prioritize the key factors influencing the choice of wooden outdoor furniture (Singer et al. 2022). Wang in his research on the evaluation indicators of parent-child interactive game furniture design, used this method to carry out quantitative analysis to rank their weights and summarize the design strengths and weaknesses of the relevant products for further optimization (Wang and Pan 2022). Based on this, this method can also be cited for research in this paper.

Constructing a Hierarchical Structure Model

According to the online furniture consumption influence factors index, an AHP hierarchical structure model was constructed: the top layer (Target Layer) is the online furniture consumption decision-making behavior, and the middle layer is the Criterion Layer and Sub-criterion Layer. The Criterion Layer contains Personal Factors, Product Factors, and Service Factors, which are the three main factors influencing consumers’ online furniture purchasing decision-making behavior; the Sub-criteria Layer further classifies Personal Factors into Personality Characteristics, Revenue, Occupation, and Personal Preferences. Product Factors are further classified into Furniture Price, Furniture Quality, Furniture Material, Furniture Size, Furniture Type, and Furniture Brand. The Service Factors are further divided into Merchant Credibility, Web Page factor, Convenience Factor, Payment Factor, Logistics Factor, and After-sales Factor. The details are shown in Fig. 4.

Fig. 4. Hierarchical analysis model of consumer influence decision

Construction of Judgment Matrix

The construction of the judgment matrix is the information basis of the AHP model approach. Assuming that each vector element in the judgment matrix is a_ij and a_ij >0, then a_ij indicates the importance of the indicator a_i to a_j, and if compared backward, the result is 1/a_ij (ij=1,2, … n), where n is the order of the judgment matrix. A web-based questionnaire was used to evaluate “What Factors Influence Consumers’ choice of Online Furniture”, and 10 people involved in online furniture were used as experts to quantify the scale and conduct a questionnaire survey on the judgment matrix of each level of indicators (scoring criteria: Table 3). The process is shown in Fig. 5.

Table 3. Judgment Matrix Scales

Fig. 5. Flowchart of the Delphi Method

Hierarchical Ranking and Its Consistency Test

According to the evaluation scale in Table 3, a judgment matrix is constructed, and this subject is assigned to 10 experts, and two-by-two comparisons are made at each level. The calculation steps are as follows.

where CI is the consistency index; RI is the random consistency index; CR is the consistency ratio. The weights of each index are shown in Tables 5 through 8.

The closer λ_max is to N, the more consistent the CI is, and the smaller the judgment error derived, and when the matrix judgment CR < 0.1, the judgment matrix is considered to have satisfactory consistency. The consistency test results are shown in Table 4.

Table 4. Consistency Test RI chart

Table 5. Judgment Matrix of the Influence of the Criterion Level Factors on the Target Level

Table 6. Judgment Matrix of the Impact of Sub-Criterion Layers (C1 through C4) on Individual Factor B1

Table 7. Judgment Matrix of the Influence of Sub-Criteria Layers (C5 through C10) on Product Factor B2

Table 8. Judgment Matrix of the Impact of Sub-Criteria Layers (C11 through C16) on Service Factor B3

From this, it can be seen that the CR values of the indicators at all levels in Tables from 5 to 8 are less than 0.1, the matrices can all pass the consistency test, the weights have a high degree of confidence, and the data are more accurate and do not need to be readjusted.

INDICATOR MODEL DATA BUILDING

Table 9. Final Rating Scale for Consumers Choosing Online Furniture

Based on the above calculations, the weight values, λ_max values, and CR values for each indicator factor at the criterion level are derived and the indicators meet the consistency test criteria. The above results were collated, and all indicators were ranked in terms of their weights, and the final rating scale model of factors influencing consumers’ choice of online furniture is shown in Table 9.

TESTING

Grey Forecasting Method (G,M) is a method of predicting grey systems. Grey Forecasting models are effective for predicting small numbers of data series with low completeness and reliability, using differential equations to fully exploit the nature of the data to predict future trends and therefore meet the criteria for testing the data (Xie and Liu 2009). The previous AHP analysis shows that the most important factors influencing consumers’ choice of online furniture are the price of furniture, income, and quality of furniture and that consumers are more likely to be influenced by price and choose online platforms for shopping. Therefore, this grey prediction model will combine the results of the data to justify the AHP model, taking “Gujia Online Furniture” as an example, to predict whether the price of furniture is one of the main factors for consumers to choose the online platform to buy furniture, based on the relationship between the change of residential furniture sales and the price fluctuation. The sales situation between October 30, 2022, and November 12, 2022, was obtained from “Know Tao Data”, as shown in Fig. 6.

Fig. 6. Sales figures for the residential furniture category

Test Results

To exclude the interference of the month-end event and the “Double 11” event, this study intercepted the online furniture sales data from November 2, 2022, to November 5, 2022, and forecasted the daily sales and prices of residential furniture from November 6, 2022, to November 8, 2022, using the grey forecasting model GM(1,1) algorithm in SPASSPRO software. From Fig. 7, it is possible to build a grey model of sales from November 2, 2022, to November 8, 2022, where the unit price of bed furniture is listed as [1332, 1332, 1332, 699, 599, 598] (unit: ¥) , and the sales on that day are [50, 100, 150, 200, 250] (unit: pieces), based on the forecast of historical period data, we can know that in November. Therefore, when the price of a single product increases, the sales volume of that day will decrease. Alternatively, when the price of a single product starts to fall, the sales volume of that day will increase positively. It can be inferred from this that when consumers shop for furniture online, they will be influenced by the price of the furniture to choose to buy. This conclusion is in line with the conclusion drawn from the AHP model and has a certain test effect on this study.

Fig. 7. Residential furniture forecast sales chart

RESULTS AND DISCUSSION

This study focuses on the collection of Internet online furniture sales data after 2020 and the investigation of online furniture markets around the world in the context of the AHP and Grey Prediction Model combination. It considers the objective factors affecting consumers’ choice of online furniture, and subjective factors for streamlining and refining, through refinement and stratification to determine the hierarchical indicators between the elements. The AHP method is used to model and analyze the weights between the elements, and then the Grey prediction model is used to test the results.

Among the three Primary Indicators that influence consumers’ choice of online furniture, the Product Factor was found to have the highest target weight among the three factors. It was followed by the Personal Factor with the second highest weight; and finally the Service Factor had the least influence among the other factors. This shows that consumers at different stages of choosing online furniture are more likely to focus on the product factor of the furniture, and the demand for its use and function still dominates, which is largely in line with the findings of the popular psychology (Lihra et al. 2012). However, due to the large size of the furniture itself and the high transport costs, manufacturers should be more stringent in their own manufacturing requirements, innovative, and optimized in terms of materials, size and style, such as the use of new technologies in furniture manufacturing to reduce the production of large mass objects; the use of modular and series design in furniture styles to facilitate disassembly and transport; and the increase of sensory technology (such as VR technology, etc.) in online displays, thus bring new product experiences to consumers and promote shopping enthusiasm to drive the online furniture market. According to the 16 Secondary Indicators of the factors influencing consumers’ choice of furniture online, the factors in descending order of influence are Revenue, Furniture price, Furniture quality, Personality Traits, Occupation, Furniture brand, Merchant Credibility, Furniture material, Convenience Factor, Types of furniture, Personal preference, Web Factor, Furniture size, After-sales Factor, Logistics Factor, and Payment Factor. From the Revenue situation and Furniture price, it is known that consumers will prioritize their own consumption level while pursuing the quality of furniture online, and they will not ignore the price because of the outstanding quality of furniture. This can be achieved by making relative policies on price to attract purchases and increase sales, such as using their price incentives to launch other peripheral products to stimulate consumers’ potential desires or increasing online furniture membership mechanisms to provide better prices and better service to discover long-term users. Price information can also be visualized to attract consumers’ attention and increase the likelihood of purchase. The relationship between the Furniture quality and the Furniture prices shows that most consumers not only value the price of furniture, but also the quality of the product, so low prices, and high-quality products are favored by customers. In addition to this, Personality Traits and Occupation are prominent in the top-weighted factors. It can be understood that some consumers may choose to shop online because of their personalities, such as social fears and love of web surfing. In terms of service factors, a good service system is also the core driver of an industry’s development, so it is important to pay more attention to the quality of after-sales service while ensuring credibility, and the attitude of online service is particularly important. In addition, a more efficient delivery experience, more convenient and fast payment methods, and more attractive advertising slogans will likely enhance the online furniture shopping experience and thus enhance sales.

CONCLUSIONS

This paper uses the analytic hierarchy process (AHP) to analyze the weighting of factors influencing online furniture consumption at three levels: Target level, Criterion level, and Sub-criterion level; and then it uses a Grey Prediction Model to validate the results obtained from the AHP method, which can ultimately reduce the anxiety and difficulties arising from the problem of choosing online furniture for consumers, enhance the advantages of online furniture itself, and provide an important reference basis for other online furniture product development and marketing promotion.
In the model of online furniture consumption factors, the Product Factor is the most influential factor in consumers’ purchasing decisions on online furniture platforms, with a maximum weight of 0.4428; followed by the Personal Factor with a weight of 0.3573; and the Service Factor with the least weight of 0.1698.
In the secondary indicators, the highest weight of 0.4972 is given to Revenue; the second highest weight of 0.3627 is given to Furniture Price and 0.3484 to Merchant Credibility; and the lowest weight of 0.0409 is given to payment factor.
Through the comprehensive evaluation analysis of indicators at all levels, the total ranking of the 16 influencing factors in the Secondary Indicators, Revenue accounts for the largest proportion, with a weight value of 0.1925; followed by Furniture Price, with a weight of 0.1606; and finally Furniture Quality, with a weight of 0.1059. The above three factors have the greatest impact on consumers’ choice of online furniture. In contrast, the After-sales Factor, Logistics Factor, and Payment Factor have lower relative weights on the total target at 0.0212, 0.0163, and 0.0069, respectively.
The Grey Prediction Model was used to test the model of factors influencing consumer choice of online furniture and it was found that changes in online prices would lead to changes in product sales and that consumers would choose online platforms to purchase furniture because of the price of furniture. Therefore, it was consistent with the AHP model data influencing consumer choice of online furniture and the conclusion was successfully argued.

ACKNOWLEDGMENTS

The authors received the support of the Joint Research Program of Nanjing Forestry University, Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Furnishings and Industrial Design, and College of Art and Design (Nanjing Forestry University, Nanjing 210037, China).

REFERENCES CITED

Adibfar, A., Gulhare, S., Srinivasan, S., and Costin, A. (2022). “Analysis and modeling of changes in online shopping behavior due to COVID-19 pandemic: A Florida case study,” Transport Policy 126, 162-176. DOI: 10.1016/j.tranpol.2022.07.003.

Alibaba Research Institute (2020). “2020 China consumer brand development report,” (https://file.01caijing.com/attachment/202005/1215D966F6CC4F3.pdf), Accessed 31 May 2020.

Brady, S. R. (2015). “Utilizing and adapting the Delphi method for use in qualitative research,” International Journal of Qualitative Methods 14(5), Online. DOI: 10.1177/1609406915621381

Chen, S., Song, Y., and Liu, C. (2022). “Analysis of influencing factors of online customization of knitwear based on hierarchical analysis,” Wool Spinning Technology 50(04), 73-77. DOI: 10.19333/j.mfkj.20210805505

Chen, Y., and Yu, H. X. (2021). “Exploration of digital design technology in custom furniture,” Furniture and Interior Decoration 2021(04), 26-29. DOI: 10.16771/j.cn43-1247/ts.2021.04.008

China Cross-border E-commerce Services Industry Trends Report (2022). “Ariadne consulting series research report,” (https://kns.cnki.net/kcms/detail/detail.aspx?FileName=ASSC202206001007&DbName=CPFD2022), 01 June 2022.

China National Bureau of Statistics (2021). “The number of China’s furniture market in the past five years,” (https://data.stats.gov.cn/easyquery.htm?cn= C01&zb=A0I0901&sj=2020), Accessed 19 Dec 2021.

De Medeiros, J. F., Ribeiro J. L. D., and Cortimiglia, M. N. (2016). “Influence of perceived value on purchasing decisions of green products in Brazil,” Journal of Cleaner Production 110, 158-169. DOI: 10.1016/j.jclepro.2015.07.100

Guan, F. (2021). Research on the Factors influencing Brand Manufacturers’ Channel Choice on B2C E-commerce Platforms, Master’s Thesis, Jinan University, Jinan, China. DOI: 10.27167/d.cnki.gjinu.2021.001007

Hakala, I., Autio, M., and Toppinen, A. (2015). “Young Finnish and German consumers’ furniture acquisition–wooden, inherited or just low price?,” International Journal of Consumer Studies 39(5), 445-451. DOI: 10.1111/ijcs.12189

He, M. H. (2022). “Analysis of E-commerce development strategy of Foshan furniture industry in the background of digital economy,” China Business 2022(14), 26-28. DOI: 10.19699/j.cnki.issn2096-0298.2022.14.026

Kaputa, V., Barčić, A. P., Maťová, H., and Motik, D. (2018). “Consumer preferences for wooden furniture in Croatia and Slovakia,” BioResources 13(3), 6280-6299. DOI: 10.15376/biores.13.3.6280-6299

Kaushik, V., Khare, A., Boardman, R., and Cano, M. B. (2020). “Why do online retailers succeed? The identification and prioritization of success factors for Indian fashion retailers,” Electronic Commerce Research and Applications 39, article ID 100906. DOI: 10.1016/j.elerap.2019.100906

Khaira, A., and Dwivedi, R. K. (2018). “A state of the art review of analytical hierarchy process,” Materials Today: Proceedings 5(2), 4029-4035. DOI: 10.1016/j.matpr.2017.11.663

Kumar, S., Lim, W. M., Pandey, N., and Christopher Westland, J. (2021). “20 years of electronic commerce research,” Electronic Commerce Research 21(1), 1-40. DOI: 10.1007/s10660-021-09464-1

Li, S. (2022). “Analysis of the formation and evolution of consumer behavior of furniture products under live E-commerce model,” Journal of Liaoning University of Technology (Social Science Edition) 24(03), 51-53. DOI: 10.15916/j.issn1674-327x.2022.03.012

Li, Y., Li, X., Zhang, Z., Zhang, G., and Gong, M. (2020). “Understanding consumers online furniture purchase behavior: An updated UTAUT perspective,” Journal of Forest Economics 35(4), 267-303. DOI: 10.1561/112.00000516

Lihra, T., Buehlmann, U., and Graf, R. (2012). “Customer preferences for customized household furniture,” Journal of Forest Economics 18(2), 94-112. DOI: 10.1016/j.jfe.2011.11.001

McBride, N. (1997). “Business use of the Internet: Strategic decision or another bandwagon?,” European Management Journal 15(1), 58-67. DOI: 10.1016/S0263-2373(96)00074-6

Ge, M. L., Zhang, L. H., and Huang, Q. F. (2018). “Study of multiple institutional logic theory and organizational ambidexterity under the background of the industry Internet: The case of Suning O2O transformation,” Management Review 30(2), 242.

Oblak, L., Pirc Barčić, A., Klarić, K., Kitek Kuzman, M., and Grošelj, P. (2017). “Evaluation of factors in buying decision process of furniture consumers by applying AHP method,” Drvna industrija 68(1), 37-43. DOI: 10.5552/drind.2017.1625.

Ojurović, R., Moro, M., Šegotić, K., Grladinović, T., and Oblak, L. (2013). “Analysis of the investment in wood processing and furniture manufacturing entities by key factors of competitiveness,” Drvna industrija 64(2), 131-137. DOI: 10.5552/drind.2013.1235.

Purcarea, T. V., Ratiu, M. P., and Negricea, C. I. (2010). “Innovating at the level of the manufacturer-retailer relationship to ensure customer satisfaction and delight,” Metalurgia International 15, 148-151.

Rampell, A. (2010). Why Online2Offline commerce is a trillion dollar opportunity. Techcrunch, [URL] http://techcrunch. com/2010/08/07/why-online2offline-commerce-is-a-trillion-dollar-opportunity.

Sadowski, B. M., Maitland, C., and van Dongen, J. (2002). “Strategic use of the Internet by small-and medium-sized companies: An exploratory study,” Information Economics and Policy 14(1), 75-93. DOI: 10.1016/S0167-6245(01)00054-3

Singer, H., and Özşahin, Ş. (2022). “Prioritization of laminate flooring selection criteria from experts’ perspectives: a spherical fuzzy AHP-based model,” Architectural Engineering and Design Management 18(6), 911-926. DOI: 10.1080/17452007.2021.1956421.

Steinhoff, L., Arli, D., Weaven, S., and Kozlenkova, I. V. (2019). “Online relationship marketing,” Journal of the Academy of Marketing Science 47(3), 369-393. DOI: 10.1007/s11747-018-0621-6

Sun, L., Zhao, Y., and Ling, B. (2020). “The joint influence of online rating and product price on purchase decision: An EEG study,” Psychology Research and Behavior Management 2020(13), 291-301. DOI: 10.2147/PRBM.S238063

Vaidya, O. S., and Kumar, S. (2006). “Analytic hierarchy process: An overview of applications,” European Journal of Operational Research 169(1), 1-29. DOI: 10.1016/j.ejor.2004.04.028

Vin, C. H., and Chun, B. J. (2019). “Platform and logistics innovation for ecommerce competitiveness: The case of Amazon. Com,” Electric Trade Review 17(4), 1-22.

Wang, J. F., and Yang, S. Y. (2022). “Research on the design of furniture E-commerce platform based on the Kano model,” Furniture and Interior Decoration 29(02), 60-65. DOI: 10.16771/j.cn43-1247/ts.2022.02.011

Wang, J., and Pan, Y. (2022). “Design evaluation of parent-child interactive game furniture based on AHP-TOPSIS method,” Journal of the Korea Convergence Society 13(2), 235-248. DOI: 10.15207/JKCS.2022.13.02.235.

Wang, Q. H. (2014). AHP-Based Study on the Factors Influencing Consumers’ Purchase Intention Under C2C E-Commerce Platform, Master’s Thesis, Northeast Normal University, Dong’bei, China.

Wieland, T. (2021). “A micro‐econometric store choice model incorporating multi‐and omni‐channel shopping: The case of furniture retailing in Germany,” Geographical Analysis (Early View). DOI: 10.1111/gean.12308

Xie, N. M., and Liu, S. F. (2009). “Discrete grey forecasting model and its optimization,” Applied Mathematical Modelling 33(2), 1173-1186. DOI: 10.1016/j.apm.2008.01.011

Xiong, X. Q., Guo, W. J., Fang, L., Zhang, M., Wu, Z. H., Lu, R., and Miyakoshi, T. (2017). “Current state and development trend of Chinese furniture industry,” Journal of Wood Science 63(5), 433-444. DOI: 10.1007/s10086-017-1643-2

Yilmaz, Z. (2022). “Ranking online shopping websites by considering the criteria weights,” Journal of Business Research 144, 497-512. DOI: 10.1016/j.jbusres.2022.02.018

Zhang, S., Zhu, J., Wang, G., Reng, S., and Yan, H. (2022). “Furniture online consumer experience: A literature review,” BioResources 17(1), 1627-1642. DOI: 10.15376/biores.17.1.Zhang

Zhao, L. (2016). Research on the Dynamic Mechanism of Furniture Enterprises’ Evolution from C2C to O2O Business Model, Master’s Thesis, Xi’an University of Technology, Xi’an, China.

Article submitted: November 22, 2022; Peer review completed: December 31, 2022; Revisions accepted: February 19, 2023; Published: February 24, 2023.

DOI: 10.15376/biores.18.2.2857-2873