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Based on the Construal Level Theory, we aim to study a most favorable fit among the advertising model, media type, and message construals, which are important factors in an advertisement. A two (social distance of the ad model in an ad: distal (low similarity) vs proximal (high similarity) by two (social distance of a media type: distal (portal) vs. proximal (SNS)) by two (message construal: abstract vs concrete) laboratory experiment was conducted to examine attitude changes on ad messages. The results show that abstract messages were more effective in attitude toward advertisement and purchase intention under the distal social distance (i.e. advertising model in low-similarity and portal media type) while concrete messages were so under the proximal social distance and SNS media type.

The stock market changes continuously as new information emerges, affecting the judgments of investors. Online news articles are valued as a traditional window to inform investors about various information that affects the stock market. This paper proposed new ways to utilize online news articles with technical indicators. The suggested hybrid model consists of three models. First, a self-attention-based convolutional neural network (CNN) model, considered to be better in interpreting the semantics of long texts, uses news content as inputs. Second, a self-attention-based, bi-long short-term memory (bi-LSTM) neural network model for short texts utilizes news titles as inputs. Third, a bi-LSTM model, considered to be better in analyzing context information and time-series models, uses 19 technical indicators as inputs. We used news articles from the previous day and technical indicators from the past seven days to predict the share price of the next day. An experiment was performed with Korean stock market data and news articles from 33 top companies over three years. Through this experiment, our proposed model showed better performance than previous approaches, which have mainly focused on news titles. This paper demonstrated that news titles and content should be treated in different ways for superior stock price prediction.

The outbreak of COVID-19 has changed our lifestyle. People spend much more time on YouTube, SNS and online shopping than before. Accordingly, the number of product review videos are steeply increasing in YouTube platform. When people watched the review videos, they might search additional information if they liked the videos. This study aims to investigate how the informativeness and the degree of attention gathering of product review videos influence on the product information sourcing intention and persuasion knowledge. We also try to find whether prior YouTube experience affects the relationship between the degree of attention gathering and persuasion knowledge. We conducted an online survey on 499 participants and analyzed using partial least square methods. Results show that 1) informativeness and the degree of attention gathering towards product review videos influence on the product information sourcing intention and user’s persuasion knowledge. 2) Viewers’ YouTube experiences moderate the increase of the viewers’ persuasion knowledge caused by increasing the degree of viewers’ attention gathering. This study implies that YouTube product review videos could be created in strategic manners. Also, it could be inferred that consumers’ prior YouTube experiences may reduce negative potentials of the degree of attention gathering onto persuasion knowledge.

In today’s dynamic and hypercompetitive business environment, knowledge and innovation have emerged as bases for sustained competitive advantage. This paper addresses two specific research questions. First, we ask, “What is the effect that firm interactivity has on various types of innovation?” As we address this question, we explain that interactivity helps firms create knowledge, which then promotes and enables innovation. Second, we ask, “How do the various types of innovation impact firm performance?” We develop a research model and a set of hypotheses from the basis of organizational knowledge creation theory and the knowledge-based view of the firm. We test this model using survey data, and find that interactivity is positively associated with innovation. We also find that several types of innovation, including service innovation, process innovation, and organizational innovation have a positive impact on firm performance.

The novel Coronavirus disease 2019 (COVID-19) is unprecedentedly changing the world since its outbreak in late 2019. Using the collected the data related to COVID-19 and the social media user data from a mobile application market research agency from January 25 to April 7, this study empirically examines the effect of the number of confirmed COVID-19 cases worldwide, the number news COVID-19, and the enforcement of social distancing measures on the daily active users (DAU) of two social media services – Twitter and Facebook – in South Korea. There are three important findings from the results of econometric analysis. First, the number of confirmed COVID-19 cases worldwide has a negative effect on the DAU of social media. Second, the number of COVID-19 news is negatively associated with the DAU of social media. Finally, the implementation of social distancing measures has no significant effect on the DAU of the social media. Theoretical implications and managerial guidelines are also discussed.

The business world has been undergoing a digital transformation. The adoption of multi-sided digital platform across the world has sped up this transformation. Multi-sided digital platforms create value by mediating interactions and transactions of distinct groups of users. A platform and its stakeholders need to be considered as a business ecosystem. Elements or components in the ecosystem exchange values and together form a network of exchange values. The objective of this paper is to construct a framework for crafting and observing digital business ecosystems. The foundation theories used to construct the framework are transaction cost economy (TCE), multi-sided markets, and value network. This paper uses Go-Jek, a growing ride-hailing platform from Indonesia, as a case to discuss how the framework works in mapping Go-Jek’s digital business ecosystem, and then explain its expansion strategy. This paper has both theoretical and managerial contributions. It provides a formal definition of digital business ecosystems as a network of exchange values. The framework does not only help studies the existing business ecosystems but also can be used to craft a new business ecosystem. It can also be used to study value exchanges within the ecosystem, assessing or crafting ecosystem expansion strategies.

Service organizations increasingly adopt data-based intelligent engines called chatbots in support of the interaction between customers and the companies. Two different types of chatbots have been suggested and introduced by companies leading the adoption of this emerging technology: rule-based chatbots and natural language processing-based chatbots. While the differences between these two types of technologies look relatively clear, the organizational and practical impacts of the differences have not been systematically explored. This study performed an experiment to compare the use of the two different types of chatbots used in practice by two comparable organizations. These two types of actual chatbots were used by Korean on-line shopping malls with similar business models (mobile shopping), length of history, size and reputation. The comparison was made based on such dimensions as usability, searchability, reliability and attractiveness. Contraty to conventional expectation that the superiority in technology will produce superior usability, the results show mixed superiority. The discussion on the reasons is presented.

In the era of disruptive change, a data-driven approach is vital to Human Resource Management (HRM) of any leading organization, for it is used to gain a competitive advantage. HR analytics (HRA) has emerged as innovative technologies since advanced analytics, i.e., predictive or prescriptive analytics, were widely used in the High Performing Organizations (HPOs). Therefore, many organizations elevate themselves to become HPOs through Data Science on the “people side.” This paper proposes a systematic literature review using the Literature Weighted Scoring (LWS) to develop a conceptual framework based on three adoption theories, which are the Technology-Organization-Environment (TOE), Diffusion of Innovation (DOI), and Unified Theory of Acceptance and Use of Technology (UTAUT). The results show that a total of 13 theory-derived factors are determined as influential factors affecting HRA adoption, and the top three factors are “Quantitative Self-Efficacy,” “Top Management Support,” and “Data Availability.” The conceptual framework with hypotheses is proposed to provide a foundation for further studies on organizational HRA adoption.

One of the challenges faced by Talent Acquisition teams today pertains to the acquisition of human resources by matching job descriptions and skillsets desired. It is more so in the case of competitive sectors like the Indian IT sector. There can be various channels for Talent Acquisition and accordingly, the cost and benefits might vary. However, the consequences of a mismatch have an impact on the quality of deliverables, high recruitment expenses and loss of revenue for the organization. With increased and diverse sources of data that are available to organizations today, there is ample opportunity to apply analytics for informed decision making in this field. This paper reveals useful insights that help streamline the Talent Acquisition process in the Indian IT Industry. The paper adopts a data-centric approach to examine the critical determinants for efficient and effective Talent Acquisition process in IT organizations. Selected supervised machine learning algorithms are applied for the analysis of the dataset. The study is likely to help organizations in reassessing their talent acquisition strategy with respect to key parameters like expected cost to company (CTC), candidate sourcing channels and optimal joining period.

Social media platforms such as Instagram and Facebook lead to potential security risks, which consequently raise public concerns about privacy. However, most people rarely make active efforts to protect their personal data, even though they have shown increasing concerns about privacy. Therefore, this study examines the factors that determine social media users’ behavior of using privacy settings and testifies the existence of privacy paradox in such a context. In addition, it investigates the mediating effects of implementation intentions on the relationship between intentions and behaviors. In the study, we collected data through questionnaires, and the respondents were undergraduate and graduate students in South Korea. After a pilot test (n = 92) and a set of face-to-face interviews, 266 usable responses were retrieved for data analysis finally. The results confirmed the existence of the privacy paradox regarding the use of social media privacy settings. And the implication intention did positively mediate the relationship between intention and behavior in the context of social media privacy settings. To the best of our knowledge, our study is the first in the information privacy literature to introduce the notion of implementation intention which is a much more powerful explanation and prediction of actual behavior than the (behavioral) intention.