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Drawing upon the third-person effect (TPE) theory, this study focuses on two types of misinformation countering intentions (i.e. simple correction and correction with justification). Accordingly, it aims to (1) assess the tenability of the third-person perception (TPP) in the face of misinformation on social media, (2) explore the antecedents of TPP and its relationship with individual-level misinformation countering intentions and (3) examine whether the mediating process is contingent on different social media usage conditions.

An online survey was conducted with 1,000 representative respondents recruited in Mainland China in January 2022 using quota sampling. Paired t-test, multiple linear regression and moderated mediation analysis were employed to examine the proposed hypotheses.

Results bolster the fundamental proposition of TPP that individuals perceive others as more susceptible to social media misinformation than they are. The self-other perceptual bias served as a mediator between the perceived consequence of misinformation and misinformation countering (i.e. simple correction and correction with justification) intentions. Furthermore, intensive social media users were likely to be motivated to counter social media misinformation derived from the indirect mechanism.

The findings provide further evidence for the role of TPE in explaining misinformation countering intention as prosocial and altruistic behavior rather than self-serving behavior. Practically, promising ways to combat rampant misinformation on social media include promoting the prosocial aspects and beneficial outcomes of misinformation countering efforts to others, as well as reconfiguring the strategies by impelling intensive social media users to participate in enacting countering actions

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2022-0507.

This study aims to examine the roles of advertising (ADV) and sales promotion (SP) in creating airline brand equity (BE) in the UAE, and to test the mediational effect of customer perceived value on the proposed relationships. Additionally, the study examines the outcomes of airline BE (i.e. trust, satisfaction and loyalty).

A structured and self-administered survey was used targeting 234 passengers, from which 197 were valid for the analysis. Exploratory and confirmatory factor analyses were used to assess the research constructs unidimensionality, validity and composite reliability.

The empirical findings support the direct relationship between ADV and BE. Perceived value fully mediates the effect of SP on BE. In addition, BE positively affects trust, satisfaction and loyalty.

Future research could investigate BE and perceived value as multidimensional constructs. Additionally, future research should expand the effect of other variables such as airline type, airline class and other marketing mix elements.

The findings from this research highlight the importance of both ADV and sale promotion in enhancing airline BE, and the pivotal role of perceived value.

The originality of this research is highlighted in conceptualizing a parsimonious model of the preceding constructs (i.e. ADV, SP, customer perceived quality) and subsequent constructs of BE (i.e. customer trust, customer satisfaction, customer loyalty), which has never been investigated collectively in one model in the literature particularly in the airline’ context of emerging economies such as UAE.

This study aims to examine the influence of training and development (TAD), including performance appraisal (PFA), on the performance of microfinance institutions in a developing economy.

A random sampling drew 100 microfinance institutions in Ghana's Greater Accra Region. Then, a purposive sampling approach selected a cross-section of employees in these institutions. Finally, the data were collected from a sample of 367 respondents, such as managers, utilizing a survey questionnaire. Structural equation modeling (SEM) was used to test hypothesized relationships.

The study results indicate that PFA has a statistically significant positive relationship with organizational performance, and this relationship is partially mediated by job satisfaction (JBS). Interestingly, the TAD process does not have a statistically significant positive relationship with organizational performance when JBS is present in the model. In fact, JBS fully mediates the relationship between TAD and organizational performance.

The study is limited to microfinance in tiers two and three in Greater Accra city of Ghana and did not include the entire country. Although the city of Accra provides a generalized representation of the research, which can be replicated, some variables and results may be impacted if other tiers of microfinance organizations are incorporated.

TAD, as well as PFA, enhance the performance of microfinance and can be utilized as tools for competitive advantage in small and medium-sized enterprises (SMEs) (e.g. microfinance institutions). The study accentuates the value of TAD, PFA and JBS in microfinance in a developing country like Ghana.

This is an original study investigating the effect of TAD and PFA practices on the performance of SMEs in a developing country like Ghana. Also, the study analyses JBS as a mediation variable to performance using SEM, which advances the research methodology in this research field.

The purpose of this article is to identify how the organisations are able to improve their business value through acquisition of business analytics capabilities and by improving their performance.

With the help of literature survey, along with standard resource-based view framework, a conceptual model has been developed. These have been statistically tested by collecting the data using the survey questionnaire from 306 selected respondents from various service sector- and product-based organisations in India. To analyse the data we have used partial least square–based structural equation modelling.

The study highlights that by the help of data acquisition and tool acquisition as two vital components the acquisition of business analytics capabilities could improve the business value of the organisation by strengthening its organisational performance. The findings of this research also indicated that acquisition of business analytics capabilities has a significant influence on an organisation's business process performance and business decision, which in turn significantly influences organisational performance. And organisational performance eventually positively influences its business value. The model was found to provide an explanative power of 71%.

The proposed research model can provide effective recommendations to the management of the organisations to realise the importance of acquisition of effective business analytics capabilities to eventually improve the business value of the organisation.

No specific studies, as yet, have analysed the effects of acquisition of business analytics capabilities for improving organisational performance mediated through business process performance and business decision. Therefore, this research has explored the distinctive effort to empirically validate this understanding.

This study focuses on Aaker's Brand Equity Model, to check the effect of brand equity determinants on booking intention (BI) for ridesharing in India. The study also explores the moderation of ecologically conscious consumer behavior (ECCB) on the multiplicative effect of brand awareness (BAw), brand association (BA) and perceived quality (PQ) in influencing the BI.

Responses from 393 Indian ridesharing users were collected using judgmental sampling and were analyzed using Hayes Process macro.

The study found a direct relationship between BAw and BI, BAw and BA, BAw and PQ, BA and PQ, PQ and BI, and BA and BI. Findings revealed mediation of BA in BAw and BI relationship and PQ in BAw and BI relationship. Results revealed that BA and PQ serially mediate BAw and BI relationship. ECCB moderates PQ and BI relationship but not BAw and BI relationship.

Serial mediation and moderated-mediation results draw various theoretical implications for determinants of Aaker's Brand Equity model and ECCB.

The research has several implications for managers in view of brand equity determinants and ECCB. The study also contributes to policy implications.

Study's novel contributions are mediation, serial mediation between brand equity determinants, and moderation of ECCB between BAw and BI for ridesharing.

The study aims to provide insights in the sensemaking process and the use of business analytics (BA) for project selection and prioritisation in start-up settings. A major focus is on the various ways start-ups can understand their data through the analytical process of sensemaking.

This is a comparative case study of two start-ups that use BA in their projects. The authors follow an interpretive approach and draw from the constructivist grounded theory method (GTM) for the purpose of data analysis, whereby the theory of sensemaking functions as the sensitising device that supports the interpretation of the data.

The key findings lie within the scope of project selection and prioritisation, where the sensemaking process is implicitly influenced by each start-up's strategy and business model. BA helps start-ups notice changes within their internal and external environment and focus their attention on the more critical questions along the lines of their processes, operations and business model. However, BA alone cannot support decision-making around less structured problems such as project selection and prioritisation, where intuitive judgement and personal opinion are still heavily used.

This study extends the research on BA applied in organisations as tools for business development. Specifically, the authors draw on the literature of BA tools in support of project management from multiple perspectives. The perspectives include but are not limited to project assessment and prioritisation. The authors view the decision-making process and the path from insight to value, as a sensemaking process, where data become part of the sensemaking roadmap and BA helps start-ups navigate the decision-making process.

Simultaneous localization and map building (SLAM), as a state estimation problem, is a prerequisite for solving the problem of autonomous vehicle motion in unknown environments. Existing algorithms are based on laser or visual odometry; however, the lidar sensing range is small, the amount of data features is small, the camera is vulnerable to external conditions and the localization and map building cannot be performed stably and accurately using a single sensor. This paper aims to propose a laser three dimensions tightly coupled map building method that incorporates visual information, and uses laser point cloud information and image information to complement each other to improve the overall performance of the algorithm.

The visual feature points are first matched at the front end of the method, and the mismatched point pairs are removed using the bidirectional random sample consensus (RANSAC) algorithm. The laser point cloud is then used to obtain its depth information, while the two types of feature points are fed into the pose estimation module for a tightly coupled local bundle adjustment solution using a heuristic simulated annealing algorithm. Finally, the visual bag-of-words model is fused in the laser point cloud information to establish a threshold to construct a loopback framework to further reduce the cumulative drift error of the system over time.

Experiments on publicly available data sets show that the proposed method in this paper can match its real trajectory well. For various scenes, the map can be constructed by using the complementary laser and vision sensors, with high accuracy and robustness. At the same time, the method is verified in a real environment using an autonomous walking acquisition platform, and the system loaded with the method can run well for a long time and take into account the environmental adaptability of multiple scenes.

A multi-sensor data tight coupling method is proposed to fuse laser and vision information for optimal solution of the positional attitude. A bidirectional RANSAC algorithm is used for the removal of visual mismatched point pairs. Further, oriented fast and rotated brief feature points are used to build a bag-of-words model and construct a real-time loopback framework to reduce error accumulation. According to the experimental validation results, the accuracy and robustness of the single-sensor SLAM algorithm can be improved.

Doctor–medical institution collaboration (DMIC) services are an emerging service mode in focal online health communities (OHCs). This new service mode is anticipated to affect user satisfaction and doctors' engagement behaviors. However, whether and how DMIC occurs is still ambiguous because the topic is rarely examined. To bridge this gap, this study explores doctors' participation in DMIC services and its effects on their online performance, as well as its effect on patients' evaluation of them on OHC platforms.

The authors propose hypotheses based on structural holes theory. A unique dataset obtained from one of the most popular OHCs in China is used to test the hypotheses, and difference-in-differences estimation is adopted to test the causality of the relationship.

The results demonstrate that providing DMIC services improves doctors' online consultation performance and patients' evaluations of them but has no significant effect on doctors' knowledge-sharing performance on OHC platforms. Doctors' knowledge-sharing performance and consultation performance mediate the relationship between participation in DMIC services and patients' evaluation of doctors. Regarding doctors' participation in DMIC services, its impact on doctors' consultation performance and patients' evaluation of them is weaker for doctors with higher professional titles than for doctors with lower professional titles.

The findings clarify the value creation mechanisms of online collaboration between doctors and medical institutions and thereafter facilitate doctors' participation in DMIC services and enhance the sustainable development of OHCs.

Large optical mirror processing systems (LOMPSs) consist of multiple subrobots, and correlated disturbance terms between these robots often lead to reduced processing accuracy. This abstract introduces a novel approach, the nonlinear subsystem adaptive dispersed fuzzy compensation control (ADFCC) method, aimed at enhancing the precision of LOMPSs.

The ADFCC model for LOMPS is developed through a nonlinear fuzzy adaptive algorithm. This model incorporates control parameters and disturbance terms (such as those arising from the external environment, friction and correlation) between subsystems to facilitate ADFCC. Error analysis is performed using the subsystem output parameters, and the resulting errors are used as feedback for compensation control.

Experimental analysis is conducted, specifically under the commonly used concentric circle processing trajectory in LOMPS. This analysis validates the effectiveness of the control model in enhancing processing accuracy.

The ADFCC strategy is demonstrated to significantly improve the accuracy of LOMPS output, offering a promising solution to the problem of correlated disturbances. This work holds the potential to benefit a wide range of practical applications.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.