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This study investigates human behavior, specifically attitude and anxiety, toward humanoid service robots in a hotel business environment.

The researcher adopted direct observations and interviews to complete the study. Visitors of Henn-na Hotel were observed and their spatial distance from the robots, along with verbal and non-verbal behavior, was recorded. The researcher then invited the observed hotel guests to participate in a short interview.

Most visitors showed a positive attitude towards the robot. More than half of the visitors offered compliments when they first saw the robot receptionists although they hesitated and maintained a distance from them. Hotel guests were also disappointed with the low human–robot interaction (HRI). As the role of robots in hotels currently remains at the presentation level, a comprehensive assessment of their interactive ability is lacking.

This study contributes to the HRI theory by confirming that people may treat robots as human strangers when they first see them. When a robot's face is more realistic, people expect it to behave like an actual human being. However, as the sample size of this study was small and all visitors were Asian, the researcher cannot generalize the results to the wider population.

Current robot receptionist has limited interaction ability. Hotel practitioners could learn about hotel guests' behavior and expectation towards android robots to enhance satisfaction and reduce disappointment.

Prior robot research has used questionnaires to investigate perceptions and usage intention, but this study collected on-site data and directly observed people's attitude toward robot staff in an actual business environment.

Territory-wide system assessment (TSA) was launched and administered by Hong Kong (HK) Education Bureau (EDB) since 2004. Since then, parents and teachers have been questioning its need, value, uselessness, effectiveness, harm for schools, teachers and students. In 2015, the issue blew up with Kau Yan School’s principal boycotting the tests. A series of discussions in the public and media and different surveys were then carried out widely in HK. After review, EDB announced in 2017 that the revised version of TSA be extended to Primary 3 students in HK. The purpose of this paper is to propose that TSAs for Primary 3, Primary 6 and Secondary 3 need a further review to judge their need and uselessness.

This paper reviews the educational policy governing the administration of the TSA. Primary and secondary data from focus group meetings, press interviews (Bogdan and Biklen, 1982; Miles and Huberman, 1994; Ouiment et al., 2001) and public reports would be analyzed. Besides, participant observation (Nosich, 1982; Sou, 2000; Sou and Zhou, 2007) and theoretical reasoning (Nosich, 1982; Sou, 2000; Sou and Zhou, 2007) have been applied for the critical review of this controversial test. The contrast study on the conflicting views of stakeholders in the education industry would bring up some insights of this controversial educational policy in Assessment for Learning.

Conflicting and contrasting perceptions from TSA to basic competency assessment (BCA) among stakeholders of education and government include governmental stakeholder – EDB’s awareness; EDB stressed that TSA is a low-stakes assessment which does not need extra practice for students; non-governmental stakeholders including legislative councilors’ perception, school principals’ perception, teachers’ perception, parents’ perception and students’ perception. Facing the opposition and grievances of different stakeholders, EDB announced in January 2017 that the revised version of TSA: BCA, be extended to HK in May 2017. Parents and legislative councilors were angry and they ask for a review or even cancellation for Primary 3 TSA.

This original study will initiate more thorough revisions and discussions for the TSAs for Primary 3, Primary 6 and Secondary 3 in HK, as a quality educational management step. While TSA for Primary 3 has been reviewed and substantially “revised,” the community at large still asks for further revision for its needs, uselessness and harm for parents, teachers and students. Since the underlying causes of students’ suicides are not fully identified, the problem of over-drilling practices for TSAs for Primary 3, Primary 6 and Secondary 3 needs to be satisfactorily resolved. Thus, TSAs for Primary 6 and Secondary 3, like that for Primary 3, should be reviewed for probable revision.

Interorganizational collaboration has been a major source of exploratory innovation. Despite much research, the authors’ understanding about how partner cultural distance is harnessed for exploratory innovation is limited. The authors’ conceptual framework aims to address this gap by explaining the social-psychological processes between perceived partner cultural distance and exploratory innovation.

Drawing on research in organizational learning and culture mixing, the authors propose a multilevel model with two parallel processes – cultural brokering and cultural defense. If managers are engaged in the former and are protected from the latter, then the partnership will produce more exploratory innovation. Cultural brokering is encouraged by prompting a learning mindset, while cultural defense is preempted by dampening social categorization across organizational boundaries.

Cultural brokering can be encouraged by building operational-level managers' (OLMs') collaborative strength through developing a learning orientation, allowing them delivery for exploration, cultivating mutual trust with partners. Cultural defense can be preempted by protecting OLMs from intergroup anxieties through providing organizational support to the OLMs, bridging social categorization faultlines and setting shared collaborative goals. Whether an alliance can unleash its potential depends on not just how cultural brokering is enabled but also how cultural defense is curtailed.

This paper takes a microfoundational approach and considers micro-level processes in a partnership. Furthermore, the model takes the operational managers' perspective and defines culture at the organizational level. All these differences allow us to provide a nuanced picture of how diverse partnerships can be harnessed for exploratory innovation through a few easily-implementable measures.

Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.