Summary Journal of Business Research

Summary Journal of Business Research

This research article provides a comprehensive analysis of consumer data in marketing and proposes a research agenda for further exploration. The authors conducted a systematic literature review and utilized scient metrics and keyword analysis to identify major topics and connections in consumer data research. They categorized consumer data based on forms of disclosure and usage by companies, distinguishing between declarative and non-declarative data, as well as primary and secondary usage. The authors also emphasized the role of technology in accessing and managing consumer data.

The key dimensions for categorizing consumer data in marketing research are data disclosure and usage by companies. Data disclosure refers to the forms in which consumers disclose their data, while data usage refers to how companies utilize consumer data for their marketing activities.

In terms of data disclosure, there are two main categories: declarative data and non-declarative data. Declarative data refers to data that consumers actively disclose through traditional market research techniques such as surveys, interviews, and focus groups. This type of data includes information about consumer preferences and consumption habits. Non-declarative data, on the other hand, are data that are generated as a by-product of consumers' activities during their customer journey. This includes unstructured behavioral data that consumers create in either declarative form (e.g., social media activity, word of mouth) or non-declarative form (e.g., location, clickstream data).

In terms of data usage by companies, there are also two main categories: primary usage and secondary usage. Primary usage of consumer data refers to the activities that companies undertake to gain marketing intelligence through market research. This includes traditional market research techniques such as surveys and interviews, as well as newer techniques like neuroscience and neuromarketing. Secondary usage of consumer data, on the other hand, refers to the utilization of consumer data that is not actively collected for market research purposes but is instead a by-product of consumers' activities during their customer journey. This includes data that companies can access from digital exchanges and new data-tracking devices in physical spaces, which provide insights into consumer behavior and preferences.

In summary, the key dimensions for categorizing consumer data in marketing research are data disclosure (declarative vs. non-declarative) and data usage by companies (primary usage vs. secondary usage). Declarative data is actively disclosed by consumers through market research techniques, while non-declarative data is generated as a by-product of consumers' activities. Primary usage of consumer data involves traditional and innovative market research techniques, while secondary usage involves utilizing data that is not actively collected for market research purposes but is available through digital exchanges and data-tracking devices.

The analysis of consumer data literature reveals several major themes and topics that have emerged over time. These themes and topics have evolved as new technologies and data sources have become available.

One major theme that has consistently emerged is consumer behavior. This theme encompasses topics such as consumer choice, decision-making processes, and shopping and purchase behaviors. Within this theme, there has been a focus on understanding both conscious and unconscious mental processing in decision-making, including the influence of factors like price and product categories. Price analysis has been a particularly prominent topic within this theme.

Another major theme is social media and user-generated content. This theme includes topics such as user-generated content, word-of-mouth, and social media analysis. Studies within this theme have explored the consequences of social media activities, such as their impact on brand value, sales, and product engagement. Social media usage in the tourism industry has also been a notable focus within this theme.

A third major theme is market research and customer satisfaction. This theme includes topics such as market research, consumer surveys, and customer satisfaction. These topics involve the collection and analysis of declarative data through traditional market research techniques.

The analysis also reveals the evolution of these themes and topics over time. There has been a shift in focus from consumer data categories to platform-specific data sources. Social media platforms like Facebook and Twitter have gained significance as data sources due to their popularity and the availability of data extraction. Additionally, there has been an increase in research on non-declarative, company secondary usage of consumer data.

In summary, the major themes and topics that emerge from the analysis of consumer data literature include consumer behavior, social media and user-generated content, and market research and customer satisfaction. These themes have evolved over time, with a shift towards platform-specific data sources and an increased focus on non-declarative data usage by companies.

The study found that research on consumer data has been steadily growing, with a particular increase in the use of declarative, company secondary usage data. The most prolific journals in this field include Journal of Marketing Research, Marketing Science, Journal of Retailing, and Management Science. The analysis of keywords revealed a wide array of topics, with a focus on consumer behavior, pricing, advertising, brand choice, influencer marketing, choice models, retailing, and engagement. Various analysis methods such as sentiment analysis, machine learning, content analysis, and netnography were frequently mentioned.

The reading mentions several analysis methods used in consumer data research. These methods include:

1. Social network analysis: This approach involves analyzing the structure and dynamics of social networks to understand information dissemination and influence within these networks.

2. Sentiment analysis: This method utilizes text mining and machine learning techniques to analyze and measure the sentiment expressed in consumer-generated content, such as online reviews and social media posts.

3. Text mining: Text mining involves extracting valuable information and insights from large volumes of unstructured text data, such as customer reviews, social media posts, and online forums.

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