One Population, Many Labels: Thematic Clusters in Ageing Research Across Business and Management
This study addresses terminological fragmentation in ageing research within business and management by analyzing 3,673 articles to map four thematic clusters, interpret them through diverse theoretical lenses, and propose a standardized search string to improve evidence retrieval consistency.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine you are trying to find every book in a massive library about a specific group of people: older adults. But here's the catch: the librarians can't agree on what to call them. One section is labeled "The Elderly," another says "Seniors," a third shouts "Silver Citizens," and a fourth whispers "Older Workers." If you ask the computer to find books about "Seniors," it might miss the ones about "The Elderly," even though they are talking about the exact same people. This confusion isn't just a minor annoyance; it's like trying to build a puzzle when half the pieces are hidden in different boxes. This is the problem facing researchers in the world of business and management. They study how companies, markets, and workplaces deal with an aging population, but because everyone uses different words, they can't easily see the big picture. To fix this, scientists use a method called a "systematic review," which is like gathering every single piece of evidence to build a complete map. But first, they have to agree on the language to find the pieces.
This is where a new study by Alperen Şahin and his team steps in. They decided to act as the ultimate librarians for the business world. Instead of guessing which words to use, they used a computer program to scan thousands of academic articles and see which words naturally hang out together. Think of it like a social network for words: if "robot" and "nursing home" always appear in the same articles, the computer knows they belong in the same group. By analyzing 3,673 peer-reviewed articles from major databases, the researchers discovered that the messy, confusing world of aging research actually organizes itself into four distinct "clubs" or themes.
The first club is all about Consumer Well-being and Service Quality. This is where researchers look at older adults as active shoppers and travelers. They study how to make cities, hotels, and products friendly for seniors, focusing on happiness, leisure, and making sure older people feel included in the economy. The second club is Labor Markets and Retirement Transitions. This group treats aging as a big economic puzzle, looking at how companies manage older workers, when people should retire, and how to keep the workforce strong as the population gets older. The third club, which is the oldest and most established, is Care Services and Healthcare Delivery. These researchers focus on the practical side of keeping older people healthy, studying nursing homes, hospitals, and how to deliver care effectively. Finally, there is the newest and fastest-growing club: AI, Robotics, and Smart Technologies. This is the futuristic corner where scientists explore how robots, artificial intelligence, and smart homes can help older people live independently and safely.
The study suggests that these four groups are like different lenses through which we can view the same phenomenon. When you look at the big picture, you see that aging in business isn't just one thing; it's a mix of laws and rules (like age discrimination laws), company strategies (like keeping experienced workers), and personal feelings (like wanting to stay happy and connected). The researchers found that while the care services group has been around the longest, the technology group is exploding with new ideas right now. Interestingly, the studies about consumer happiness are currently getting the most attention and citations from other scientists.
To solve the language problem that started it all, the team created a "master key"—a special search string made of 18 different terms. This tool combines all the different ways people say "older person" (like "elderly," "senior," "aging," and "geriatric") into one powerful search command. Now, any researcher can use this key to find every relevant article, no matter which label the original author used. The study doesn't claim to have solved every problem in the field, but it does provide a clear map and a better flashlight for anyone trying to navigate the complex, fragmented world of aging research in business. By bringing these four scattered communities together under one roof, the study helps us understand that whether we are talking about robots, retirement, or retail, we are all studying the same vital shift in our society.
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