The impact of digital technology on changing consumer behaviours with special reference to the home furnishing sector in Singapore

Singapore’s retail landscape has rapidly shifted from offline to online, intensified by global competition and accelerated by COVID-19. Over the last decade, online shopping in Singapore increased by 50%, while average purchase value declined, notably for items over S$200—a critical threshold for furniture. Despite the sector’s relevance, local data on online purchasing for home furnishings are limited, creating a gap for stakeholders and industry associations. The study investigates how digital technology and the pandemic influence consumer behaviour and attitudes in the furniture category, compares pros and cons of online vs offline, and evaluates the feasibility of an omnichannel model. Key research questions: (1) Have consumer behaviours and attitudes towards furniture changed, especially in vertical eCommerce? (2) What are the advantages and disadvantages of offline and online furniture stores? (3) Is an omnichannel model the most acceptable approach? Objectives: assess consumer behaviour trends in furniture eCommerce, compare offline vs online store advantages and disadvantages, and examine the future omnichannel business model for home furnishing in Singapore.

The review identifies variables affecting buyer behaviour—features, attitudes, involvement, demographics (age, income, education), lifestyle and environmental concerns—shaped by digital transformation. Emerging technologies (IoT, AR/VR/MR, AI assistants) are transforming customer journeys across pre-, during-, and post-transaction stages. In Singapore, market changes include evolving availability/cost of retail space, a persistent preference for some physical presence to build brand value, and shifts in consumer perceptions of experiential retail. Digital transformation research highlights technology readiness, exploration, and exploitation driving value creation, innovation, market expansion, and supply chain ecosystems. Consumer involvement, perceived value, trust, and convenience influence adoption and channel-switching; omnichannel showrooming and online convenience are key. Evidence suggests rising online furniture interest (e.g., US and Poland), with brand visibility and standalone retailer websites important in consideration. The review exposes a local gap: few Singapore-specific studies on furniture eCommerce behaviours. A conceptual framework posits two major drivers—external technological impacts on digital life and internal consumer attitudes/behaviours—moderated by demographics (age, gender, income, education) and shopping characteristics (online frequency, research time). Six null hypotheses (H1–H6) test relationships between demographics and behaviour/attitudes across channels.

Design: Quantitative, deductive approach grounded in realism philosophy; cross-sectional, descriptive research design. Instrument: Structured questionnaire with four parts—sociodemographics, buying behaviour, behavioural intention for furniture purchase, and external influences. Sampling and participants: Convenience (nonprobability) sampling. N=84 respondents, aged 18–75, mixed income and education levels, varied sex and marital status. Data collection: Online survey via Google Forms distributed on social media; anonymous responses collected October–November 2020 in Singapore. Validity and reliability: Content validity via expert review (two IT professionals, two marketing professionals, two home furnishing experts). Construct validity review by a statistician to ensure appropriate variable types and intervals. Reliability assessed with Cronbach’s alpha = 0.749 (>0.73 threshold), indicating acceptable internal consistency. Analysis: Descriptive statistics and chi-square nonparametric tests to examine associations between categorical variables. Hypothesis testing at α=0.05. Variables tested included: gender vs online/offline furniture purchase mode; gender vs showroom-only vs purely online attitudes; marital status vs showroom-only vs purely online attitudes; income vs online shopping frequency; income vs preference for showroom vs purely online vs omnichannel; education vs online purchasing research time duration. Valid case counts varied by table (typically 80–84).

Overall shopping patterns: Omnichannel and standalone furniture websites together accounted for about two-thirds of shopping behaviour. Figure 2 indicates preference for an online+offline combination (omnichannel) at 35%, followed by new standalone branded furniture websites at 33%. Cross-tabulation on purchase mode (Table 1): 21% purely online, 8% purely offline, 71% purchased on seller’s website after viewing physical items (omnichannel). H1 (gender × purchase mode online/offline): Significant association. Pearson χ2=10.791, df=4, p=0.029 (Table 2). Males showed higher intention to purchase via the omnichannel approach. H2 (gender × showroom-only vs purely online): Significant association. Pearson χ2=6.423, df=2, p=0.040 (Table 4). Offline shopping remained mainstream (90% preferred showroom); males more readily accept purely online than females; females prefer physical stores. H3 (marital status × showroom-only vs purely online): Significant association. Pearson χ2=8.232, df=1, p=0.004; Fisher’s exact p=0.006 (Table 6). Couples exhibit stronger preference and intention for showroom visits than singles. H4 (income × online shopping frequency): Significant association. Pearson χ2=41.942, df=28, p=0.044 (Table 8). Frequency distribution overall: 13% shop online at least every 3 days, 42% at least weekly, 26% monthly, 15% yearly, 4% never (Table 7). Lower-income groups reported more frequent online purchases than higher-income groups. H5 (income × furniture purchase mode: purely online, purely offline, or online after physical view): Significant association. Pearson χ2=32.423, df=14, p=0.003 (Table 10). Higher-income groups showed greater intention to order furniture online, with the most attractive scenario being online purchase after physical viewing (omnichannel), especially among high-income respondents (Table 9). H6 (education × online purchasing research time duration): Significant association. Pearson χ2=32.184, df=16, p=0.009 (Table 12). Higher education correlates with longer online research time; bachelor’s degree holders constituted the largest group (37%) and tended to spend more time researching (Table 11).

Findings indicate that digital technology and pandemic-driven shifts have strengthened consumers’ preference for omnichannel furniture shopping in Singapore. The strong omnichannel preference (71% purchasing online after viewing physically; 35% preference in Figure 2) addresses the research questions by showing consumers value both tactile in-store experiences and online convenience, price, and personalization. Demographic factors significantly shape channel behaviour: males are more accepting of purely online options, whereas females prefer physical stores; couples show stronger showroom preference than singles, highlighting the social and joint decision-making aspects of furniture purchases; higher income is associated with greater online intention within an omnichannel context; higher education correlates with more extensive online research time. These results underscore the need for targeted omnichannel strategies: maintain compelling showroom experiences (experiential validation, consultations, customization) while optimizing digital touchpoints (rich content, AR/VR previews, reviews, seamless checkout). The associations support tailoring engagement by gender, income, and education to enhance conversion and retention across channels.

The study contributes Singapore-specific evidence that furniture shoppers favor an omnichannel model combining online and offline strengths. It evaluates behaviour trends in furniture eCommerce, compares offline vs online advantages and disadvantages, and examines the future omnichannel model, recommending strategies such as improving service convenience to reduce channel switching, deploying engagement tools (e.g., online education, brand engagement, gamification), and strengthening brand visibility through standalone websites plus physical presence. As vertical eCommerce expands post-COVID-19, retailers should pursue differentiated, demographic-informed strategies (by gender, education, income) with enhanced consultation, customization, content marketing, and post-sale service emphasis. Future research should use larger, probability-based samples, longer time frames (longitudinal designs), and deeper category-level analysis, and incorporate factors like branding recognition, reviews, and decision processes, as well as digital engagement (e.g., gamification).

The study used convenience sampling with a relatively small sample (N=84) and cross-sectional design, limiting generalizability and causal inference. Data were collected within a short time window (Oct–Nov 2020), potentially influenced by pandemic-specific conditions. Several chi-square tables had small expected cell counts, which may affect test robustness. Findings pertain to Singapore and may not directly generalize to other markets or to all furniture subcategories. Future work should expand sample size, adopt probability sampling, extend study duration (9–12 months or longitudinal), refine questions for specific home furnishing categories, and include additional variables (branding recognition, reviews, decision-making processes, digital engagement strategies).