Earticle

This study aims to segment perceived risks of MICE destination and to analyze if there are differences among segmented groups in terms of MICE destination switching behavior, visiting satisfaction and loyalty. MICE industry has been grown and expected to expand more in the future. In order to effectively attract and manage MICE industry, it is important to understand what risks affect MICE destination choices and decision. From the literature review, the measurement of perceived risks of MICE destination have been developed and measured from on-site survey. Factor and cluster analysis with perceived risks were conducted and showed five underlying dimensions of perceived risks(function, time consumption, physical and environmental, social and psychological, economical risk) and three different segmented groups(highly, moderate, low perceived risk group). MANOVA analysis showed that there were differences among segmented groups in terms of visiting satisfaction and loyalty.

Purpose ? This study examines why consumers in Myanmar prefer Facebook commerce (F-commerce) over traditional e-commerce platforms. Specifically, it investigates how the social and technological characteristics of F-commerce influence consumers’ relationship quality, users’ inertia, and resistance to switching to e-commerce. Design/Methodology/Approach ?­ Data were collected from 242 consumers in Myanmar who had experience with F-commerce. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the proposed hypotheses. A hierarchical component model was specified in which social support and relationship quality were modelled as reflective higher-order constructs. Findings ? Social support, social presence, system quality, and information quality significantly enhanced relationship quality, whereas interactivity did not. Relationship quality significantly increased users’ inertia and resistance to switching to e-commerce. Users’ inertia significantly strengthened resistance to switching. Therefore, resistance to traditional e-commerce is shaped by strong relational attachment to the F-commerce environment and reliance on familiar purchasing routines. Research Implications ? This study contributes to the literature on social commerce, relationship quality, and resistance to switching by showing how F-commerce characteristics shape consumers’ internal evaluations and behavioural responses in an emerging market context. It highlights relationship quality as a key mechanism that shapes consumers’ continued reliance on F-commerce and their resistance to adopting traditional e-commerce platforms. The findings also provide practical guidance for e-commerce platforms and policymakers seeking to encourage the adoption of traditional e-commerce in emerging markets such as Myanmar.

본 연구는 OTA(Online Travel Agency) 플랫폼을 중심으로, 디지털 락인을 시간 속에서 변화하는 행동적 과정으로 파악하고 사용자의 행동 데이터 기반으로 분석하였다. 특히 본 연구의 핵심 기여는 ‘락인의 역설’이라는 개념을 경험적으로 입증하여, 사용자가 특정 플랫폼에 구조적으로 고착되어 있어도 실제 행동적 충성도는 낮을 수 있음을 규명하는 데 있다. 이를 위해 2024년 1월부터 6월까지 6개월간의 엠브레인 패널 로그 데이터를 활용하여, OTA 앱 사용 기록, 위치 로그, 구매 맥락을 통합 분석하였다. 데이터는 월 단위로 구조화되어 사용자 유형별 숙박 행동, 플랫폼 점유율 변화를 시계열적으로 추적할 수 있도록 구성되었다. 분석 결과, 첫째, 단일 플랫폼 사용자(SPU)는 특정 OTA에 장기적으로 머물지만, 다중 플랫폼 사용자(MPU)에 비해 실제 숙박일수가 유의하게 낮았다. 이는 락인이 반드시 충성의 결과가 아니라, 습관적 이용이나 행동적 관성의 결과일 수 있음을 시사한다. 둘째, 사용자 유형은 시점에 따라 강화･이탈･전환을 반복하며, 다중 이용자일수록 탐색적 행동을 통해 구매 실현 가능성을 높이는 경향을 보였다. 셋째, 동일 유형을 장기간 유지한 사용자가 반드시 높은 숙박 빈도를 보이지 않았으며, 오히려 다중 이용자가 더 높은 행동 실현성을 보였다. 이러한 결과를 통해 본 연구는 디지털 락인을 충성의 정태적 결과가 아닌, 탐색성과 효율성의 균형 속에서 나타나는 행동적 현상으로 재정의하였다. 이론적으로는 락인의 개념을 시간･행동 기반 프레임으로 확장하였으며, 방법론적으로는 실제 이용 로그를 활용하여 충성의 질적 측면을 측정한 점에서 기존 연구를 보완하였다. 실무적으로는 플랫폼 기업이 단순 체류율보다 실질적 전환 품질을 관리해야 함을, 정책적으로는 데이터 이동성과 전환비용 완화가 경쟁적 혁신을 촉진할 수 있음을 제시한다.

This study focuses on OTA (Online Travel Agency) platforms, conceptualizing digital lock-in as a behavioural process that evolves over time and analysing it based on user behavioral data. A significant contribution of this research is the empirical validation of the ‘lock-in paradox’, which demonstrates that even when users are structurally locked into a specific platform, their actual behavioral loyalty may remain low. To achieve this objective, a period of six months of Embrain panel log data from January to June 2024 was utilized, incorporating an analysis of OTA app usage records, location logs, and purchase contexts. The data were structured on a monthly basis, thereby enabling the tracking of accommodation behaviors and alterations in platform share by user type over time. The analysis yielded three key findings: Firstly, Single Platform Users (SPUs) demonstrated a long-term commitment to a specific OTA, yet their actual number of lodging nights was significantly lower than that of Multi-Platform Users (MPUs). This finding suggests that lock-in may not necessarily result from loyalty, but could stem from habitual use or behavioral inertia. Secondly, user types repeatedly undergo a process of reinforcement, defection, and switching over time. Multi-platform users have been shown to increase purchase realization probability through exploratory behaviour. Thirdly, it was observed that users who maintained the same type over an extended period did not necessarily exhibit a high frequency of bookings. Conversely, users who utilised multiple platforms demonstrated higher levels of behavioral realization. The present study offers a novel interpretation of digital lock-in, challenging the conventional notion of it as a static consequence of loyalty. Instead, it is proposed that digital lock-in is a behavioral phenomenon that emerges from a dynamic equilibrium between exploration and efficiency. Theoretically, it extends the notion of lock-in to a framework based on time and action. Methodologically, it complements existing research by utilizing actual usage logs to measure the qualitative aspects of loyalty. In practice, this suggests that platform companies should prioritize the management of behavioral conversion over the maintenance of simple retention rates. From a policy perspective, it suggests that the presence of data portability and reduced switching costs can potentially stimulate competitive innovation.

이 논문에서는 한국의 주별 익일물 콜금리를 사용해 국면전환 확산과정모형을 추정했다. 일반적인 비선형 추세 함수와 분산 탄력성이 상수인 변동성 함수를 갖는 확산과정모형에서 모수들이 경제상황에 따라 서로 다른 두 가지 값을 가질 수 있도록 모형을 설정한 후에 최우도추정법을 이용해 모형을 추정했다. 우도를 계산하기 위해 해밀턴의 알고리즘을 이용하고, 확산과정의 전이확률밀도 함수가 알려져 있지 않기 때문에 Aït-Sahalia (2008)의 축소가능하지 않은 방법을 이용해 근사적 로그-전이확률밀도 함수를 구체적인 식으로 구한 후에 Choi (2015a)의 방법으로 전이확률밀도 함수를 구했다. 서로 다른 두 국면 L과 H가 존재한다는 여러 가지 증거들을 확인할 수 있었으며 추세 함수보다는 변동성 함수가 두 국면 모두에서 이자율의 움직임을 설명하는 데 중요한 역할을 한다는 사실을 밝혔다. 또한 국면변수의 변화를 결정하는 전이 확률이 이자율에 의존해 시간에 따라 변하며 이자율이 늘어날수록 다음 기에도 같은 국면 L에 머무를 확률은 줄어들고 같은 국면 H에 머무를 확률은 증가함을 알 수 있었다. 각 데이터 시점에서 추정된 국면 H의 평활확률은 시계열 자료에서 대체로 변동성이 큰 부분은 1과 가깝고 작은 부분은 0과 가까워 뚜렷하게 다른 두 국면이 있다는 증거가 된다. 마지막으로 주요 모형과 이 모형에 포함된 기존의 연구들에서 사용됐던 다른 모형들을 비교했을 때 일반적인 모형이 더 선호됨을 알 수 있었다.

This paper estimates a regime-switching diffusion process model for Korean call rate. Parameters of the diffusion model with nonlinear general drift and volatility function vary depending on the economic states and are estimated using maximum likelihood method. In order to obtain the maximum likelihood estimates, we use the algorithm of Hamilton (1989) and calculate transition probability density function(TPDF) using Choi (2015a)’s method from the approximate log-TPDF obtained by Ait-Sahalia (2008)’s irreducible method. We find evidences that there exist two different regime and that the volatility term of the model explains the movements of the call rate better than the drift term. Also, the model shows that the transition probability varies depending on the level of interest rate. As interest rate increases, the probability of remaining at the same regime “L” next period decreases while the probability for the regime “H” increases. The estimated smoothed probability of regime “H,” which is close to 1 and 0 during the period with high and low volatility respectively, is a clear evidence of the existence of two different regimes. Lastly, comparison with existing models demonstrates that the generalized model in this paper is preferred in explaining the call rate behaviour.