Interpreting, whether spoken or signed, plays a crucial role in facilitating cross-cultural communication. The quality of interpretation is a multifaceted concept, influenced by interpreters, speakers, listeners, and clients. Listeners, as the ultimate recipients of interpreted messages, hold particular significance in determining overall quality. Research on listeners' perceptions of interpreting quality has largely focused on either listeners' quality expectations or the impact of paralinguistic features on their perceptions. However, a significant research gap exists regarding the in-depth exploration of listeners' subjective factors and their interactions in shaping perceived quality. This study aims to bridge this gap by investigating the influence of six key listener variables on their perceptions of CI quality in a technology-focused context. These variables—quality expectations, perceived interpreter characteristics, experiences with CI, domain knowledge, perceived dependence on CI, and perceived communicative effect—are chosen based on existing literature and their potential to predict listeners' perceptions. The study's significance lies in understanding how these factors interact to shape listeners' perceptions, thereby informing professional interpreting practice and interpreter training.

Research on interpreting quality can be broadly categorized into product-oriented and interaction-oriented approaches. Product-oriented research focuses on objectively assessing the linguistic features of interpreted output, using various measures like error analysis, checklists, and rating scales. While this approach offers valuable insights into textual aspects, it often neglects the interactive dynamics of interpreting events. Interaction-oriented research, in contrast, emphasizes the subjective perceptions of users, particularly listeners, highlighting the communicative effect and the role of expectations. Existing research on listeners' perceptions has explored quality expectations and the impact of paralinguistic features (accent, fluency, intonation), but it lacks comprehensive exploration of diverse listener variables and their interplay. This study builds upon previous work by focusing on the six listener variables described above, aiming to examine their predictive roles in shaping perceptions of CI quality.

This study employed a case-based survey design, using a simulated interpreter-mediated technical conference on artificial intelligence and smart society. Participants (107 computer science students) were selected using purposive and convenience sampling. Data was collected in two stages: before and after the conference. The pre-conference questionnaire assessed participants' demographic information (age, gender), experiences with CI, quality expectations, and domain knowledge. The post-conference questionnaire measured perceived dependence on CI, perceived interpreter characteristics, perceived communicative effect, and perceived CI quality. All items were measured on a 7-point Likert scale. The questionnaires were translated and back-translated to ensure accuracy. A pilot test with 20 participants was conducted to assess reliability (Cronbach's alpha ranged from 0.833 to 0.945). Data analysis was performed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3, examining both the measurement and structural models. PLS-SEM was selected due to its suitability for small datasets and its ability to handle both reflective and formative variables.

Descriptive analysis revealed that participants had limited prior experience with CI, but held high quality expectations and perceived high communicative effect and dependence on CI during the conference. PLS-SEM analysis confirmed the reliability and validity of the measurement model. The structural model revealed that domain knowledge showed a significant negative relationship with perceived CI quality (supporting H9), indicating that participants with greater domain knowledge had stricter evaluation criteria. The other five variables showed significant positive relationships with perceived CI quality: quality expectations (β = 0.448, p < 0.001), perceived interpreter characteristics (β = 0.320, p < 0.001), perceived communicative effect (β = 0.247, p < 0.05), perceived dependence on CI (β = 0.207, p < 0.05), and experiences with CI (β = 0.109, p < 0.05). Quality expectations significantly and positively predicted perceived interpreter characteristics (β = 0.482, p < 0.001) and perceived communicative effect (β = 0.297, p < 0.05). Domain knowledge and experience with CI positively influenced quality expectations (supporting H6 and H8). Perceived interpreter characteristics positively predicted perceived communicative effect (supporting H5). The Goodness of Fit (GOF) index was 0.563, indicating a good model fit. R-squared values suggested that the model explained a substantial amount of variance in the dependent variables.

The findings highlight the significant role of listeners' subjective factors in shaping their perceptions of CI quality. The negative correlation between domain knowledge and perceived quality confirms that expertise leads to more critical evaluations. The positive relationships between the other five variables and perceived quality underscore the importance of meeting or exceeding listeners' expectations, creating a positive impression of the interpreter, ensuring effective communication, and leveraging listeners' existing experience and dependence on CI. The unexpected positive relationships between quality expectations and perceived quality (despite initial hypotheses of a negative correlation), and the positive prediction of perceived interpreter characteristics and communicative effect by quality expectations suggest that the perceived quality is influenced not solely by the level of expectation, but also the gap between expectations and actual performance. The strong positive influence of quality expectations suggests the importance of listener education and management of expectations. The positive influence of perceived interpreter characteristics reinforces the significance of projecting professionalism and building rapport. The positive impact of communicative effect highlights the importance of achieving communicative goals and meeting listeners' informational needs.

This study contributes to the field of interpreting studies by providing empirical evidence on the interplay of listener-specific factors in shaping perceptions of CI quality. It emphasizes the critical role of quality expectations, perceived interpreter characteristics, and perceived communicative effect. The findings highlight the need for listener education to manage expectations and for interpreter training programs to incorporate modules focusing on listener analysis, image projection, effective communication, and domain knowledge adaptation. Future research should expand the scope to include various interpreting modes and settings and employ mixed methods to enhance the robustness of findings.

This study is limited by its focus on CI in a single technical conference with a homogenous participant group (computer science students). The reliance on self-reported data from a questionnaire survey may introduce bias. The simulated conference setting might not fully replicate the complexities of real-world events. The study also focuses on a limited set of listener variables, and future research could expand this scope. These limitations suggest the need for future research to broaden the scope of investigation, utilize mixed-methods approaches, and conduct studies in more naturalistic settings.