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This study provides a framework to explain the attitude–intention gap in viewing advertisements in social networks. Going beyond the literal and evaluative inconsistency issues in measuring factors with theory of planned behavior (TPB), the authors propose and test a theoretical framework with possible moderators to the relationship between the attitude and behavioral intention.

Two surveys were conducted to test the theoretical framework, one with students and the other with working people in Hanoi, Vietnam. After testing measure reliabilities and validities, hypotheses were tested with regressions using SPSS.

In general, the attitude was still found to have a positive relationship with the behavioral intention. However, the attitude–intention gap still exists as trust in social network was found to moderate the relationship between the attitude and intention with the working people sample while trust in brands advertised facilitate the relationship with the student sample. Interestingly, involvement was not found to moderate the relationship.

Several practical implications can be recommended. In general, the marketing strategy for managers is still to develop positive attitude by consumers toward viewing advertisements. However, personalization strategy should be taken with care in advertisement in social network. Providing consumers with perceived privacy control may help enhance the advertisement effectiveness. Finally, building trusts, on or off the social network, should be optimized to increase the users' intention to view advertisements in social network.

This research offers a new explanation for the attitude–intention inconsistency in general and for viewing advertisements in social networks in particular. Going beyond the measurement issues, the research suggests looking at the process under that the attitude can be formed and activated to impact on the intention. Moreover, mixed findings from two comparable samples provide nuanced insights for different groups of consumers.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0563.

The current review aims to examine the growth trajectory, most influential documents, intellectual and conceptual structure of the literature regarding gender issues in family business research.

The bibliometric analysis was performed using 224 documents from 1991 to 2020 extracted from the Web of Science database.

The review finds that this field's knowledge grew exponentially during the last three decades, mainly after 2003 and the last several years. Based on the co-citation analysis, three major research lines are identified: “Women's challenges and opportunities in the family business”, “Gender diversity in the family business corporate board”, and “Gender and family SMEs management.” The temporal co-word analysis reveals that “Gender diversity in the family business corporate board” is the latest research line.

By reviewing prominent cited references and documents that cited them, the authors provide the landscapes and research gaps of three major research lines for further development.

Different countries have responded to the pandemic with distinct domestic and international travel restrictions. The purpose of this paper is to investigate the stringency of the coronavirus disease 2019 (COVID-19) countermeasures in Japan against their G20 cohorts. Primary data were monitored at a ski resort in Kyushu regarding the social acceptance of initial COVID-19 countermeasures, ranging from hygiene and local “lockdowns” to border control measures.

The stringency of the COVID-19 countermeasures was examined using data from the Oxford COVID-19 Government Response Tracker (OxCGRT) and triangulated with the early stage social acceptance of survey respondents in Aso Kuju National Park in February 2020 that consisted of 165 valid Japanese language questionnaires.

An one-way analysis of variance (ANOVA) identified significant differences in social acceptance for countermeasures, with more-concerned respondents agreeing more strongly with “low-tech” health protocols, such as washing hands (M = 3.7) or wearing a mask (3.4). More concerned visitors were significantly more likely to modify their travel plans (2.9) or cancel their trip altogether (2.7). Male day trippers were less likely to be concerned by the COVID-19 pandemic.

This paper's originality is derived from a triangulation of the stringency of Japan's initial COVID-19 countermeasures via a combination of comparison with G20 cohorts and social acceptance of domestic snowboarders and skiers. Moreover, by shining a light on the trade-off between public health and human rights, the paper provides a current review of the ethical dimension of a travel restriction debate that is often overlooked in the ongoing pandemic.

To follow and maintain an appropriate distance to the selected target person (STP), the mobile robot is required to have capabilities: the human detection and tracking and an efficient following strategy with a smooth manner that does not appear threatening to the STP and surroundings. The efficient following strategy must integrate the STP position and the obstacle information to achieve smooth and safe human-following behaviors, especially in unknown environments where robot does not have understandings in advance. The purpose of this study is to propose a robust-adaptive-behavior strategy for mobile robots.

This paper presents a robust-adaptive-behavior strategy (RABS) based on the fuzzy inference mechanism to help the robot follow the STP effectively in various unknown environments with the real-time obstacle avoidance, both indoor and outdoor and on different robot platforms. In which, the traversability of robots’ unknown surrounding environments is analyzed by using the STP position and the obstacle information obtained from the two dimensional laser scan, whose purpose is to choose the highest-traversability-score direction (HTSD) and an adaptive-safe-following distance (ASFD). Then, the HTSD, the ASFD and the current velocity of the robot are considered as inputs of the fuzzy system to adjust its velocity smoothly.

The proposed RABS is verified by a set of experiments using a real big-heavy autonomous mobile robot (BH-AMR), with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg), aiding smart factories. The obtained results have shown that the proposed RABS equips the BH-AMR with the ability to follow the STP smoothly and safely even when the robot is moving at the maximum speed 1.5 (m/s).

In this paper, the autonomous mobile robot considers all environments as unknown even when it is working in mapped environments. This limitation is presented clearly in the future works section.

This proposed method can be used to help the autonomous mobile robot support persons in factories, hospitals, restaurants, supermarkets or at the airports.

This paper presents a RABS, including three new features: a fuzzy-based solution to help human-following robots maintain an appropriate distance to the STP safely and smoothly with the maximum velocity 1.5 (m/s); the proposed fuzzy-based solution, an adaptive vector field histogram and a new approach for the STP tracking is combined to follow the STP and avoid the collision simultaneously in unknown indoor and outdoor environments; the proposed RABS is considered for BH-AMRs (with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg)) to serve real tasks in smart factories.

Collision avoidance is considered as a crucial issue in mobile robotic navigation to guarantee the safety of robots as well as working surroundings, especially for humans. Therefore, the position and velocity of obstacles appearing in the working space of the self-driving mobile robot should be observed to help the robot predict the collision and choose traversable directions. This paper aims to propose a new approach for obstacle tracking, dubbed MoDeT.

First, all long lines, such as walls, are extracted from the 2D-laser scan and considered as static obstacles (or mapped obstacles). Second, a density-based procedure is implemented to cluster nonwall obstacles. These clusters are then geometrically fitted as ellipses. Finally, the combination of Kalman filter and global nearest-neighbor (GNN) method is used to track obstacles’ position and velocity.

The proposed method (MoDeT) is experimentally verified by using an autonomous mobile robot (AMR) named AMR SR300. The MoDeT is found to provide better performance in comparison with previous methods for self-driving mobile robots.

The robot can only see a part of the object, depending on the light detection and ranging scan view. As a consequence, geometrical features of the obstacle are sometimes changed, especially when the robot is moving fast.

This proposed method is to serve the navigation and path planning for the AMR.

(a) Proposing an extended weighted line extractor, (b) proposing a density-based obstacle detection and (c) implementing a combination of methods [in (a) and (b) constant acceleration Kalman and GNN] to obtain obstacles’ properties.

The purpose of this paper is to estimate a recent trend in climate change and its impact on livelihood of community living in Nam Dinh province, Vietnam. Further, it aims to increase the government attention for adaptation measures by providing awareness of climate change and its negative impacts on livelihood.

For study purpose, cross-sectional and secondary data sets were used. The community perceptions about climate change were recorded by face-to-face interviews of 500 respondents from Nam Dinh province, Vietnam in April 2015 by using a well-structured questionnaire, whereas secondary data were collected from the statistical yearbook of General Statistics Office of Vietnam. To accomplish the study objectives, Cobb–Douglas production function and Likert scale were used to estimate the community perceptions of climate change and impact of climate change on livelihood, respectively.

Results depict that climate change negatively impacted on the productivity of rice and livestock. Particularly, frequently occurring of droughts, floods and salinity intrusion negatively impacted on rice productivity, while livestock productivity is decreased by frequent occurring of flood storms in study area.

The study results suggest a government support is essential to achieve sustainable livelihoods for coastal communities living in the Red River Delta, particularly some adaptation measures in the context of climate change are required in study area.