Guest editorial: Digital transformation and consumer experience

Citation

Koo, D.-M., Kim, J. and Kim, T. (2022), "Guest editorial: Digital transformation and consumer experience", Internet Research, Vol. 32 No. 3, pp. 967-970. https://doi.org/10.1108/INTR-04-2022-684

Digital marketing is currently significantly influenced by two factors: recent developments in technology and COVID-19 environmental factors.

First, COVID-19 has dramatically changed the market position and size of the digital (vs traditional offline) market (Donthu and Gustafsson, 2020; Lewnes, 2021; Kim et al., 2021c). Experts expect that digital marketing will continue to be considered one of the most important marketing tools in the new normal after the COVID-19 pandemic.

Second, recent advances in digital transformation, such as artificial intelligence, machine learning and big data, have fueled the proliferation of digital marketing practices. These advances have not only affected how consumers live but also changed how firms do business and interact with consumers, including retailing (Kim et al., 2021a; Reinartz et al., 2019), business strategy (Huang and Rust, 2021; Verhoef et al., 2021), service (Zaki, 2019) and personal engagement (Kumar et al., 2019). The leading global and local firms must prioritize “digital transformation” for their businesses.

In light of these drastic changes, our research team launched a special issue focusing on “Digital transformation and consumer experience” with the cooperation of the International Conference of Asian Marketing Associations in Seoul, Korea. After a rigorous review with the help of our generous and experienced reviewers, the guest editorial team has finalized four articles for this special issue. The following is a summary of each article.

First, Xu et al. (2022) focus on the critical differences in consumer behaviors between the traditional offline and online channels. For example, when buying in an offline store, consumers can physically inspect products and obtain tactile information before purchasing. By contrast, when shopping online, consumers rely on visual and textual information about the product, without the benefit of direct experience. Given the fundamental differences between online and offline shopping channels, the authors examine how shopping channels affect consumers' psychological processes and shopping behaviors. Based on construal level theory, the authors propose that, in an online channel, consumers inspect a target product by taking a high-level construal, focusing on abstract, essential and outcome-related aspects. Alternatively, when shopping offline, consumers are physically closer to the product. Thus, they tend to assess the product at a low-level construal, focusing on concrete, auxiliary and process-related aspects. Three empirical studies support these predictions. Despite substantial research on multiple shopping channels, research comparing consumers' psychological processes and purchase behaviors across channels is limited. The current study fills the gap by deepening the understanding of how shopping channels, online versus offline, influence consumers' mental construal and purchase processes.

Using emojis is quite common, particularly in online social media. Baek et al. (2022) focus on the role of emojis in digital marketing. Specifically, they investigate how emojis in social media influence media engagement and cooperation intention in environmental campaigns. Based on the emotional theory of the social information model, the authors suggest that emojis have a positive effect on social media engagement in sustainable behaviors. They further propose an underlying mechanism for this argument (i.e. positive emotion through emojis) and the boundary condition for this effect (type of environmental message – assertive [vs non-assertive]). Two experimental studies provide empirical support for the argument. The current study makes a new contribution to our understanding of the unique online experience with the use of emojis.

Third, Kim et al. (2022b) focus on an empirical model for predicting box office revenue by using a text mining approach and extracting insights from movie scripts. Their modeling approach provides superior prediction performance compared with previous benchmarks (Eliashberg et al., 2007). Specifically, the authors use latent Dirichlet allocation to determine the hidden textual structure in movie scripts by extracting topic probabilities as predictors for classification. They use the extracted topic probabilities to predict box office performance. Using a variety of classification models, such as logistic classification, decision trees, random forests, k-nearest neighbor algorithms, support vector machines and artificial neural networks, the authors compare their relative performances in predicting movies' market performance. Following Eliashberg et al. (2007), the authors provide superior prediction power by using topic modeling to extract rich information from movie scripts even before movie production begins. This research is meaningful because the authors not only use the latest advanced approaches for processing textual information to capture the hidden textual structure but also provide a scientific approach for predicting the future performance of a new product, such as movie scripts, at a very early stage.

Lastly, Oh and Yi (2022) focus on measuring consumer sentiments at the feature level and show their asymmetric impacts on overall product ratings. Using 49,130 online customer reviews for 14 wireless earbud products, they categorize the key quality dimensions of wireless earbuds into basic, excitement and performance factors. The authors apply text mining techniques to online consumer review data to analyze feature-level sentiment, combined with bigram natural language processing analysis to identify word combinations of the major quality dimensions and related sentiment words. To assess the asymmetric effect on positive and negative feedback, the authors use sentiment dictionaries to classify adjectives by polarity. The authors investigate the asymmetric relationship between feature-level sentiments measured from text reviews and star ratings. Specifically, they find that the positive and negative contributions of product attribute performance can change over time. Thus, for the same amount of change, the positive and negative reviews of specific attributes will have different degrees of influence on the overall product evaluation. It is worth checking whether this time-varying feature-level sentiment relating to a new product can be explained by heterogeneity among consumer groups, innovators or early adopters, and the early majority and laggards, who appreciate different aspects of the new product, particularly innovative or new technology-related new products.

Although this issue covers some important issues regarding the recent trends in digital transformation, our team also provides directions for future research, including:

1. Improving consumer experience and customer relationship management (CRM)

2. Digital transformation of marketing communications: Search engine marketing, search engine optimization, display advertising and programmatic advertising

3. Marketing and advertising technology: New technologies in marketing

4. Social media marketing: Social media optimization and CRM through social media

5. Search engine advertising: Search engine optimization, cross-media optimization and competition in search advertising

6. Artificial intelligence, machine learning and deep learning in marketing

7. Personalized advertising and marketing practices

8. Mobile marketing: Location-based promotion and impact of mobile technology on retail industry

9. Marketing in the metaverse

Enhancing consumers' experiences in digital marketing is one of the critical factors for future business success. This special issue attempts to propose some directions for advancement in theory and practice. We strongly believe that every business agent should have an ongoing understanding of digital transformation. This requires a fundamental change in business, specifically because of the advancements in recent technology and the new normal owing to the global pandemic. Academically, we hope that researchers will continue to provide practitioners with actionable insights and accumulate academic theories about digital transformation.