| Literature DB >> 33807054 |
Khaled Alhammadi1, Luna Santos-Roldán1, Luis Javier Cabeza-Ramírez1.
Abstract
The past few years have seen significant demographic changes in most regions, including an increased elderly population. Subsequently, elderly citizens comprise an important market segment of consumers, with the food industry one of the most affected areas in this context. However, food market managers previously believed that elderly consumers' needs were stereotyped in nature. The lack of focus on this sector, therefore, left elderly consumers as an untapped market, without realizing the financial independence of this segment regarding their nutrition. This research will attempt to provide the key determinant factors on elderly consumers' behavior related to food. For that purpose, a complete literature review of more than 123 papers regarding these concepts has been carried out. Once analyzed, we highlight the common insights to give clear guidance for supermarket managers and food manufacturers to have a better knowledge of the reasons behind elderly people's food acquisitions.Entities:
Keywords: buying behavior; consumer behavior; elderly; good purchases; older consumer; purchasing behavior
Year: 2021 PMID: 33807054 PMCID: PMC8004734 DOI: 10.3390/foods10030688
Source DB: PubMed Journal: Foods ISSN: 2304-8158
Figure 1Flow diagram adapted according to the PRISMA protocol.
Software configuration.
| VoSviewer V.1.6.16 | Scimat V.1.1.04 | ||
|---|---|---|---|
| Item | Characteristic/Value | Item | Characteristic/Value |
| Type analysis | Co-occurrence | Select periods | Period (1973–2021) |
| Unit | All Keywords | Unit | Author’s Words |
| Counting method | Full counting | Data reduction | Frequency reduction = 2 |
| Normalization Method | Association Strength | Kind of matrix | Co-occurrence |
| Layout | Attraction = 2/Repulsion = 0 | Network reduction | Minimum value = 1 |
| Clustering | Resolution = 1.00/Min Cluster size = 10 | Normalization Method | Equivalence index |
| Visualization Scale | Network and overlay = 1.27 | Cluster Algorithm | Simple centers (Max network = 12; Min = 3) |
| Weights | Occurrences | Mapper | core mapper |
| Labels size variation | Min. Strength = 0/Max. Lines = 500 | Quality measures | all |
| Minimum numbers of occurrences of a keyword | 2 | Longitudinal map | Equivalence index |
Figure 2Documentary typology and predominant indexing categories.
Thematic cluster detected in global research.
| Cluster/Color/Label | N° Keywords | First Five Keywords (Links, Total Link Strength, Occurrences) |
|---|---|---|
| C1/red/Older Consumer | 36 | Older consumers (108, 255, 37); Food (76, 139, 17); Perception (73, 122, 15); Segmentation (65, 96, 11); Motivation (57,91,11) |
| C2/Green/Elderly | 25 | Elderly (94,188, 23); Protein (56, 87, 10) Quality of life (59, 84, 9); Malnutrition (42, 70, 8); Nutritional Status (32, 42, 5); Energy (26, 29, 4) |
| C3/Blue/Health | 24 | Health (105, 229, 27); Nutrition (103, 227, 26); Diet Quality (40, 56, 7), Older adults (37, 44, 7); Community Dwelling older adults (40, 55, 5) |
| C4/Yellow/Patterns | 22 | Patterns (57, 81, 10); People (52, 84, 9); Risk (39, 58, 7) Food Shopping (32, 37, 6); Aged Related Diseases (33, 39, 4) |
| C5/Purple/Consumption | 21 | Consumption (88, 185, 26); Food Choice (40, 70, 10); Diet (63, 103, 12); Baby Boomers (39, 58, 7); Survey (32, 46, 5) |
| C6/LightBlue/Older adult | 17 | Older adult (89,166, 21); Age Differences (82, 147, 20); Choice Orientation (75, 132, 18); Aging (58, 83, 13); Information (25, 35, 5) |
| C7/Orange/Determinants | 13 | Determinants (57, 101, 11); Consumer Behavior (27, 33, 5); Food Products (18, 23, 4); Mortality (26, 46, 4); Mediterranean diet (18, 19, 3) |
| C8/Brown/Behavior | 12 | Behavior (62, 11, 16); Attitudes (58, 103, 13); Knowledge (42, 54, 6); Functional Food (34, 53, 6); Heterogeneity (21, 27, 3) |
Figure 3Map of thematic clusters detected and density visualization.
Thematic cluster and core documents detected by Scimat.
| Themes | Q | Centrality | Centrality Range | Density | Density Range | Core Documents (Highly Cited) |
|---|---|---|---|---|---|---|
| Countries | 1 | 279.74 | 1 | 41.19 | 0.86 | [ |
| Antecedents | 1 | 132.68 | 0.64 | 73.88 | 1 | [ |
| Meat-consumption | 1 | 131.61 | 0.57 | 59.02 | 0.93 | [ |
| Population | 1 | 148.47 | 0.79 | 27.26 | 0.64 | [ |
| Age-differences | 1 | 164.8 | 0.86 | 23.14 | 0.57 | [ |
| People | 1 | 140.05 | 0.71 | 21.99 | 0.5 | [ |
| Determinants | 2 | 167.87 | 0.93 | 19.1 | 0.43 | [ |
| Senior-marketing | 2 | 131.34 | 0.5 | 15.13 | 0.21 | [ |
| Accessibility | 3 | 56.75 | 0.21 | 18.29 | 0.36 | [ |
| Loneliness | 3 | 124.73 | 0.36 | 18.02 | 0.29 | [ |
| Food-products | 3 | 44.11 | 0.14 | 13.91 | 0.14 | [ |
| Aging-population | 3 | 107.29 | 0.29 | 13.89 | 0.07 | [ |
| Supermarket | 4 | 130.51 | 0.43 | 32.02 | 0.71 | [ |
| Odor | 4 | 31.83 | 0.07 | 33.33 | 0.79 | [ |
Figure 4Matrix diagram depicting the performance of the research themes by the number of documents.
Figure 5Theoretical framework and positioning of thematic clusters obtained with VoSviewer and Scimat.