| Literature DB >> 35735389 |
Abstract
This study examined changes in consumer perceptions of product types and purchase intentions when a subscription model is introduced for products normally sold on a one-time basis. It then proposed product types likely to affect consumers' purchasing intentions in the subscription economy and product categories best suited for the subscription economy. To this end, an experimental study was conducted with experts and general consumers using 99 subscription business cases. It was found that a regular delivery of products on a subscription basis gradually changes consumer perceptions of the products from utilitarian to hedonic and from search to experience ones. It was also found that consumption motivation is an important predictor of consumer purchase intentions in the subscription economy. In addition, experience-utilitarian and search-utilitarian products were associated with the highest purchase intentions among experts and general consumers, respectively. This suggests that a company's strategy should be adjusted in line with consumers' understanding of the subscription model. Therefore, suppliers need to understand the full implications of the new model, such as changed consumer perceptions and purchasing intentions, and strive to design a subscription model that is suitable for the target segments and product selections.Entities:
Keywords: context effects; experience goods; hedonic goods; search goods; subscription model; utilitarian goods
Year: 2022 PMID: 35735389 PMCID: PMC9220096 DOI: 10.3390/bs12060179
Source DB: PubMed Journal: Behav Sci (Basel) ISSN: 2076-328X
Figure 1Experimental design.
Research design.
| Survey | Expert ( | General Consumer ( | |
|---|---|---|---|
| Pre | (One group) | (Group 1) | (Group 2) |
| Post | (Group 3) | (Group 4) | |
Example of survey items (before introduction of subscription model).
| Case 1. Contact Lens | |||
|---|---|---|---|
| Consumption | Utilitarian goods | ①—②—③—④—⑤—⑥ | Hedonic goods |
| Quality | Experience goods | ①—②—③—④—⑤—⑥ | Search goods |
| Purchase | No | ①—②—③—④—⑤—⑥ | Yes |
Example of survey items (after introduction of the subscription model).
| Case 1. Hubble—Contact Lens Subscription | |||
|---|---|---|---|
| Consumption | Utilitarian goods | ①—②—③—④—⑤—⑥ | Hedonic goods |
| Quality | Experience goods | ①—②—③—④—⑤—⑥ | Search goods |
| Purchase | No | ①—②—③—④—⑤—⑥ | Yes |
Figure 2Classification of product type categories based on consumption motivation and quality inference.
Subjects of the one-group pretest–posttest design (31 experts).
| 1 | 18 years of experience as the head of digital marketing |
| 2 | 18 years of experience in digital marketing and as a freelance English interpreter and translator |
| 3 | 18 years as a reporter; head of new business/innovation |
| 4 | 8 years on the future business planning team of a Korean steel company, P |
| 5 | Meta-branding strategic planning executive, Company L Economic Research Institute |
| 6 | Head of in-house venture company A, the largest beauty company in Korea; CSO of startup |
| 7 | Founder and CEO of steel recycling startup |
| 8 | 10 years in the futures business of a financial holding company, N |
| 9 | 10 years as the project manager (PM) of a global energy company and heavy industry marketing |
| 10 | 20 years in digital-development system integration (SI) for an IT service company, L |
| 11 | 10 years as a marketer in the beauty industry; reading community partner |
| 12 | Head of new business at a communication company, S; marketer |
| 13 | Power blogger; marketer at large Korean distributor company, E |
| 14 | New business in the financial sector; head of planning at the Korea Undergraduate Association of STEM |
| 15 | Founder of a health social venture; startup marketer |
| 16 | PR manager at an e-commerce & InsurTech company |
| 17 | 20 years as the team leader of a platform business team; financial SI expert; MBA |
| 18 | 10 years in financial SI planning; section manager of platform business team |
| 19 | Market designer, Growth Lab team leader at Company T, a foreign language learning subscription startup |
| 20 | Startup business legal advisor; completed Korean Bar Association Startup Academy |
| 21 | Founder of software education startup company A, recognized as technologically innovative startup and accelerator |
| 22 | 3 years as the manager of new business and big data at a large Korean distribution company, L |
| 23 | COO of a startup; startup and IT headhunter; Silicon Valley Connector |
| 24 | University student, social venture founder, and writer |
| 25 | 15 years of experience in advertising and planning, and as a digital marketer and brand manager |
| 26 | 10 years of experience as an online and offline marketer and in financial big data planning |
| 27 | 15 years of experience in financial enterprise digital and fintech planning |
| 28 | 15 years as a financial SI; PM of 30 projects |
| 29 | Doctor of Business Administration, fields: technology management, product and service innovation |
| 30 | Startup accelerator: 25 years as the CEO of a startup |
| 31 | Head of raw material importing and purchasing team at a large Korean food company; international trade history |
Experts: Demographic profile of respondents in the one-group pretest–posttest design.
| Category | Frequency | Ratio | |
|---|---|---|---|
| Gender | Male | 17 | 54.8 |
| Female | 14 | 45.2 | |
| Age | 20s | 6 | 19.4 |
| 30s | 12 | 38.7 | |
| 40s | 12 | 38.7 | |
| 50s | 1 | 3.2 | |
| 60s or older | 0 | 0.0 | |
| Marital status | Single | 19 | 61.3 |
| Married | 12 | 38.7 | |
| Education | High school graduate | - | - |
| Attending university | 2 | 6.4 | |
| University graduate | 14 | 45.2 | |
| Attending graduate school | 3 | 9.7 | |
| Graduate school graduate | 12 | 38.7 | |
| Monthly expenditures | Less than $858 | 3 | 9.7 |
| $858–$2575 | 15 | 48.4 | |
| $2575–$4293 | 10 | 32.2 | |
| $4.293–$6010 | 3 | 9.7 | |
| $6010 or more | - | - | |
General consumers: demographic profile of respondents in the posttest-only control group design.
| Measurement Item | Pre | Post | |||
|---|---|---|---|---|---|
| 1–49 | 50–99 | 1–49 | 50–99 | ||
| Group classification | Group 1 | Group 2 | Group 3 | Group 4 | |
| N (=152) | 41 | 41 | 31 | 39 | |
| Gender | Man | 12 (29.3) | 30 (73.2) | 15 (48.4) | 19 (48.7) |
| Woman | 29 (70.7) | 11 (26.8) | 16 (51.6) | 20 (51.3) | |
| Age | 20s | 1 (2.4) | 7 (17.1) | 14 (45.2) | 6 (15.4) |
| 30s | 12 (29.3) | 22 (53.7) | 14 (45.2) | 21 (53.8) | |
| 40s | 27 (65.9) | 11 (26.8) | 3 (9.6) | 10 (25.6) | |
| 50s | 1 (2.4) | 1 (2.4) | - | 1 (2.6) | |
| 60s or older | - | - | - | 1 (2.6) | |
| Marital status | Single | 8 (19.5) | 19 (46.3) | 23 (74.2) | 14 (35.9) |
| Married | 33 (80.5) | 22 (53.7) | 8 (25.8) | 25 (64.1) | |
| Education | High school graduate | 1 (2.4) | 1 (2.4) | - | 3 (7.7) |
| Attending university | - | 4 (9.8) | 8 (25.8) | 5 (12.8) | |
| University graduate | 33 (80.5) | 26 (63.4) | 18 (58.1) | 29 (74.3) | |
| Attending graduate school | 3 (7.3) | 1 (2.4) | 1 (3.2) | 1 (2.6) | |
| Graduate school graduate | 4 (9.8) | 9 (22.0) | 4 (12.9) | 1 (2.6) | |
| Monthly expenditures | Less than $858 | 3 (7.3) | 8 (19.5) | 11 (35.5) | 9 (23.1) |
| $858–$2575 | 16 (39.0) | 20 (48.8) | 15 (48.4) | 11 (28.2) | |
| $2575–$4293 | 13 (31.8) | 6 (14.6) | 5 (16.1) | 13 (33.3) | |
| $4293–$6010 | 6 (14.6) | 4 (9.8) | - | 4 (10.3) | |
| $6010 or more | 3 (7.3) | 3 (7.3) | - | 2 (5.1) | |
Experts: paired t-test results for one-group pretest–posttest design.
| Variable | N | Response Difference |
|
| ||
|---|---|---|---|---|---|---|
| Mean | SD | |||||
| Response 1 | Consumption motivation | 3069 | −0.306 | 1.656 | −10.238 | 0.000 |
| Response 2 | Quality inference | 3069 | 0.117 | 2.140 | 3.037 | 0.002 |
| Response 3 | Purchase intention | 3069 | 0.910 | 2.210 | 22.805 | 0.000 |
General consumers: independent two-sample t-test results for posttest-only control group design.
| Variable | Category | Survey Questions | |||||
|---|---|---|---|---|---|---|---|
| Total (1–99) | Product Group 1 (1–49) | Product Group 2 (50–99) | |||||
| Pre | Post | Pre | Post | Pre | Post | ||
| Consumption | N | 4059 | 3469 | 2009 | 1519 | 2050 | 1950 |
| Mean | 3.29 | 3.79 | 3.33 | 3.77 | 3.25 | 3.80 | |
| SD | 1.954 | 1.850 | 2.008 | 1.907 | 1.900 | 1.805 | |
| DOF | 7526 | 3526 | 3998 | ||||
|
| −11.276 | −6.559 | −9.413 | ||||
|
| 0.000 | 0.000 | 0.000 | ||||
| Quality | N | 4059 | 3469 | 2009 | 1519 | 2050 | 1950 |
| Mean | 3.19 | 3.04 | 3.21 | 3.04 | 3.23 | 3.10 | |
| SD | 1.789 | 1.770 | 1.812 | 1.775 | 1.785 | 1.779 | |
| DOF | 7526 | 3.526 | 3998 | ||||
|
| 3.627 | 2.668 | 2.233 | ||||
|
| 0.000 | 0.008 | 0.026 | ||||
| Purchase | N | 4059 | 3469 | 2009 | 1519 | 2050 | 1950 |
| Mean | 3.98 | 2.91 | 4.15 | 2.91 | 3.81 | 2.91 | |
| SD | 1.852 | 1.687 | 1.860 | 1.690 | 1.829 | 1.686 | |
| DOF | 7526 | 3526 | 3998 | ||||
|
| 26.046 | 20.386 | 16.247 | ||||
|
| 0.000 | 0.000 | 0.000 | ||||
(N = number of samples, SD = Standard deviation, DOF = Degree of Freedom, t = t-test statistic, p = significance probability).
Figure 3Items with significant t-test results in both experimental designs: consumption motivation.
Figure 4Items with significant t-test results in both experimental designs: quality inference.
Figure 5Items with significant t-test results in both experimental designs: purchase intention.
Results of the correlation analysis between the main variables.
| Variables | Consumption Motivation | Quality Inference | Purchase Intention | |
|---|---|---|---|---|
| Experts | Consumption motivation | 1 | ||
| Quality inference | 0.055 (0.002) | 1 | ||
| Purchase intention | −0.212 (0.000) | 0.058 (0.001) | 1 | |
| General consumers | Consumption motivation | 1 | ||
| Quality inference | 0.151 (0.000) | 1 | ||
| Purchase intention | −0.207 (0.000) | −0.044 (0.009) | 1 |
Experts: Results of multiple regression analysis to identify factors influencing purchase intention.
| Dependent Variable | Independent | B |
|
|
| VIF |
|---|---|---|---|---|---|---|
| Purchase intention | (Constant) | 4.413 | ||||
| Consumption motivation | −0.147 | −0.147 | −8.178 | 0.000 | 1.006 | |
| Quality inference | 0.026 | 0.023 | 1.300 | 0.194 | 1.006 | |
(B = estimates, β = Standardized estimates, t = t-test statistic, p = significance probability, VIF = Variance Inflation Factor).
General consumers: results of hierarchical multiple regression analysis to identify factors influencing purchase intention.
| Model | Independent Variables | B |
|
|
| VIF |
|---|---|---|---|---|---|---|
| 1 | (Constant) | 3.622 | 56.737 | 0.000 | ||
| Consumption motivation | −0.189 | −0.207 | −12.448 | 0.000 | 1.000 | |
| 2 | (Constant) | 3.654 | 48.524 | 0.000 | ||
| Consumption motivation | −0.187 | −0.205 | −12.187 | 0.000 | 1.023 | |
| Quality inference | −0.013 | −0.013 | −0.784 | 0.433 | 1.023 | |
| 3 | (Constant) | 3.652 | 44.735 | 0.000 | ||
| Consumption motivation | −0.187 | −0.205 | −12.185 | 0.000 | 1.023 | |
| Quality inference | −0.013 | −0.013 | −0.784 | 0.433 | 1.023 | |
| Product group | 0.003 | 0.001 | 0.057 | 0.954 | 1.000 | |
(B = estimates, β = Standardized estimates, t = t-test statistic, p = significance probability, VIF = Variance Inflation Factor).
Ninety-nine cases classified according to 4 product type categories.
| Product Type Categories | Experts | General Consumers | ||
|---|---|---|---|---|
| Pre | Post | Pre | Post | |
| a. Search-UT products | 17 | 5 | 14 | 2 |
| b. Search-HED products | 15 | 18 | 20 | 12 |
| c. Ex-UT products | 38 | 39 | 44 | 41 |
| d. Ex-HED products | 29 | 37 | 21 | 44 |
| Total | 99 | 99 | 99 | 99 |
One-way ANOVA results of purchase intention by product type category for the experts.
| Product Type Categories | N | M | SD | F ( | Scheffe | |
|---|---|---|---|---|---|---|
| Pre | (a) Search-UT products | 527 | 4.15 | 1.810 | 23.815 | c > a, d, b |
| (b) Search-HED products | 465 | 3.39 | 1.852 | |||
| (c) Ex-UT products | 1178 | 4.20 | 1.822 | |||
| (d) Ex-HED products | 899 | 3.92 | 1.851 | |||
| Post | (a) Search-UT products | 155 | 3.18 | 1.793 | 9.428 | c > a, b, d |
| (b) Search-HED products | 558 | 2.94 | 1.744 | |||
| (c) Ex-UT products | 1209 | 3.27 | 1.710 | |||
| (d) Ex-HED products | 1147 | 2.93 | 1.637 |
(N = number of samples, M = Mean, SD = Standard deviation, F = F-Value, p = significance probability).
One-way ANOVA results of purchase intention by product type category for the general consumers.
| Product Type Categories | N | M | SD | F ( | Scheffe | |
|---|---|---|---|---|---|---|
| Pre | (a) Search-UT products | 574 | 3.65 | 1.801 | 12.245 | c > d, b, a |
| (b) Search-HED products | 820 | 3.88 | 1.878 | |||
| (c) Ex-UT products | 1804 | 4.15 | 1.835 | |||
| (d) Ex-HED products | 861 | 3.94 | 1.861 | |||
| Post | (a) Search-UT products | 70 | 3.44 | 1.682 | 16.972 | a > c, d, b |
| (b) Search-HED products | 420 | 2.75 | 1.745 | |||
| (c) Ex-UT products | 1423 | 3.12 | 1.687 | |||
| (d) Ex-HED products | 1556 | 2.73 | 1.652 |
(N = number of samples, M = Mean, SD = Standard deviation, F = F-Value, p = significance probability).
99 Cases of Subscription Business.
| No. | Company | Product | No. | Company | Product |
|---|---|---|---|---|---|
| 1 | Hubble | Contact lenses | 2 | Wisely | Razor blades |
| 3 | MEHISOX | Socks | 4 | Happy | Organic sanitary napkins (Female hygiene) |
| 5 | Kindoh | Diapers | 6 | Monthly Hair | Services in |
| 7 | Forward Healthcare | Medical services | 8 | Charles Schwab | Financial services |
| 9 | The Banchan | Side dishes | 10 | Laundrygo | Laundry service |
| 11 | Porsche Passport | Cars | 12 | Netflix | Media content |
| 13 | Millie’s Library | E-books | 14 | Pinzle | Art publishing |
| 15 | Closet Share | Luxury clothing | 16 | Hooch | Alcoholic beverages |
| 17 | Veluga Brewery | Alcoholic beverages | 18 | Kukka | Flowers |
| 19 | Paffem | Perfume | 20 | MS XBOX | Video games |
| 21 | Bark Box | Dog supplies | 22 | Hyundai Selection | Cars |
| 23 | Loot Crate | Game equipment | 24 | MEZON | Services in |
| 25 | IPSY | Cosmetics | 26 | GUBIT | Alcoholic beverages |
| 27 | Yaro Ramen | Ramen | 28 | Kangeki | Theatrical productions |
| 29 | Fitty | Exercise | 30 | Leisure me | Leisur |
| 31 | Quip | Oral care | 32 | LOLA | Tampons (Female hygiene) |
| 33 | Daily Shot | Alcoholic beverages | 34 | Murung Farm | Agricultural products |
| 35 | Hobby in the Box | Hobby supplies | 36 | Clean Bedding | Bedding |
| 37 | Weekly Shirts | Men’s dress shirts | 38 | PUBLY | Digital content |
| 39 | Laftel | Animation streaming | 40 | Open Gallery | Artwork |
| 41 | Dollar Shave Club | Razor blades | 42 | Blue Apron | Food ingredients |
| 43 | Birchbox | Cosmetics | 44 | Nike Adventure Club | Children’s shoes |
| 45 | SNCF | Train boarding passes | 46 | Inoshave | Shaving supplies |
| 47 | Purple Dog | Wine | 48 | Dolo Box | Pet supplies |
| 49 | Hello Fresh | Food ingredients | 50 | OFFICE PASS | Office supplies |
| 51 | Flybook | Books | 52 | Kyobo SAM | E-books |
| 53 | All the Time MINI | Cars | 54 | Hyundai Genesis | Cars |
| 55 | KIA Flex | Cars | 56 | Socar Pairing | Cars |
| 57 | Noble Made | Towels | 58 | Fritz | Coffee |
| 59 | Sooldamhwa | Traditional liquor | 60 | Mannabox | Fresh food |
| 61 | Doctors Me | Regular specialist consultations | 62 | Deli Shirts | Men’s dress shirts |
| 63 | Wealth front | Financial services | 64 | Walmart | Cosmetics |
| 65 | Graze | Snacks | 66 | Owlcrate | Youth books |
| 67 | Lafeeolla | Cooking pan replacements | 68 | Karitoke | Watches |
| 69 | Peloton | Exercise | 70 | ShoeDazzle | Shoes |
| 71 | Bundle | Washing machines | 72 | Kirin Hometap | Draft beer |
| 73 | Wemakeprice | Coffee | 74 | SERENDIP | Book summaries |
| 75 | Feather | Furniture | 76 | Doctor Noah | Oral care |
| 77 | Flier | Book summaries | 78 | Bean Brothers | Coffee |
| 79 | Nescafe Capsule | Coffee capsules | 80 | SM LYSN | Fanclub service |
| 81 | YouTube | Online video content | 82 | Adore Me | Underwear |
| 83 | Care of | Nutritional supplements | 84 | PORTO | Reference books |
| 85 | Toun28 | Cosmetics | 86 | Snack Nation | Snacks for companies |
| 87 | Farmision | Meat | 88 | The Vegan Kind | Vegan products |
| 89 | Pact | Coffee | 90 | Glossy Box | Cosmetics |
| 91 | The Willoughby Book Club | Books | 92 | Air Closet | Clothing |
| 93 | NINAL | Glasses | 94 | POTLUCK | Food |
| 95 | Handel’s Café | Beverages | 96 | ADDress | Share house |
| 97 | Le Tote | Clothing | 98 | Zwift | Indoor cycling game |
| 99 | Unique Your Nail | Nail art |