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There has recently been much interest in analysing the creation of personalised tourism services and studying their effect on organisations. However, there still needs to be more work in analysing their effect on happiness and the role that emotions play in these processes. This paper aims to analyse, in the context of personalised and innovative tourism services, which factors can encourage and improve managers' happiness.

A model of analysis is presented with five variables: tourism managers' happiness (TMH), innovative personalised tourism services, internal factors: emotions (IFE), organisational factors (OF) and personal factors (PF). Eight hypotheses are proposed and tested with a structural equation model.

The results allow the authors to affirm that personalised technological advances applied to tourism services not only contribute to improving the happiness of hotel managers but also in generating emotions that contribute to improving their attitude towards the company.

This scientific work has some limitations. Firstly, this study was carried out exclusively in Spain due to the relevance of this country in the international tourism sector, according to the World Tourism Organisation. The results achieved in this research should be contrasted with other studies in other territories. Secondly, the interviews and surveys were carried out at specific time intervals. It has not led to problems of significant bias in the variance of the standard method. Therefore, it is desirable to undertake longitudinal or cross-sectional studies for future research. Thirdly, it is interesting to develop theoretical models that include other psycho-directive or leadership style constructs to determine whether they holistically enhance the subjective well-being of hospitality managers. Moreover, other types of factors of a social or strategic nature can be considered, which can positively or negatively impact the analysed variables. Finally, future research can deepen the empirical analysis of the relationship between managerial competencies and digital innovation from the perspective of happiness management. These findings would contribute to a greater cognitive understanding of the implications of personalised and innovative tourism services on hotel establishments' happiness and economic benefits.

This paper shows the fundamental role of a happy leadership style in creating responsible, green and innovative environments in today's digital society. Furthermore, the happiness of tourism managers can contribute to the generation of high quality and excellent services that are in line with the principles of sustainable development.

Personalised technological advances applied to tourism services not only contribute to improving the happiness of hotel managers but also to generating emotions that contribute to improving their attitude towards the company. On the other hand, it has been observed that personalised and innovative tourism services generate positive effects at organisational, internal and personal levels. The following reflections are advanced: The development of internal factors such as the emotions of awe and gratitude or the generation of trust can enhance the happiness of tourism managers. The happiness of tourism business managers can be enhanced by developing OF such as smart-personalised tourism services and data protection. The happiness of tourism managers can be enhanced by the development of PF such as travellers' desires, expectations and needs, or other factors such as disposable income, health status or family situation.

This is the first empirical study that focuses on investigating how personalised and innovative tourism services affect managing happiness.

The purpose of this paper is to report a case study in which financial feasibility integrated quality function deployment (QFD) approach was implemented.

Customer complaints were systematically gathered. The house of quality (HoQ) matrix was developed. The technical descriptors were prioritized and subjected to the financial feasibility study. The cost calculations were carried out and the actions were derived. A set of value engineering (VE) principles was used during this case study.

The study reported in this paper indicated the need for integrating financial feasibility study with QFD for enhancing the effectiveness of the method. The measures taken to prevent the customer complaints will be of considerable value to the manufacturing organizations.

During the conduct of case study, high-cost factors restricted the selection of materials which would exhibit higher performance. The case study was carried out in a single electronic switches manufacturing organization.

The manufacturing costs incurred have been reduced by incorporating changes in the part material. The outcomes of the study have been considered for further implementation in the case organisation which indicated the practicality of the study.

The concept of apportionment of HoQ cost developed with the idea of integrating the same with QFD is the original contribution of the authors.

The purpose of this study is to investigate the influence of varying concentrations of nano-SiO₂ particle doping on the structure and properties of the micro-arc oxidation (MAO) coating of 7075 aluminum alloy. This research aims to provide novel insights and methodologies for the surface treatment and protection of 7075 aluminum alloy.

The surface morphology of the MAO coating was characterized using scanning electron microscope. Energy spectrometer was used to characterize the elemental content and distribution on the surface and cross section of the MAO coating. The phase composition of the MAO coating was characterized using X-ray diffractometer. The corrosion resistance of the MAO coating was characterized using an electrochemical workstation.

The results showed that when the addition of nano-SiO₂ particles is 3 g/L, the corrosion resistance is optimal.

This study investigated the influence of different concentrations of nano-SiO₂ particles on the structure and properties of the MAO coating of 7075 aluminum alloy.

This paper aims to research, identify and discuss the benefits and overall role of big data and artificial intelligence (BDAI) in the tourism sector, as this is depicted in recent literature.

A systematic literature review was conducted under the McKinsey’s Global Institute (Talwar and Koury, 2017) methodological perspective that identifies the four ways (i.e. project, produce, promote and provide) in which BDAI creates value. The authors enhanced this analysis methodology by depicting relevant challenges as well.

The findings imply that BDAI create value for the tourism sector through appropriately identified disseminations. The benefits of adopting BDAI strategies include increased efficiency, productivity and profitability for tourism suppliers combined with an extremely rich and personalized experience for travellers. The authors conclude that challenges can be bypassed by adopting a BDAI strategy. Such an adoption will stand critical for the competitiveness and resilience of existing established and new players in the tourism sector.

Besides identifying the benefits that BDAI brings in the tourism sector, the research proposes a guidebook to overcome challenges when introducing such new technologies. The exploration of the BDAI literature brings important implication for managers, academicians and consumers. This is the first systematic review in an area and contributes to the broader e-commerce marketing, retailing and e-tourism research.

本论文旨在研究、指出、和讨论大数据和人工智能（BDAI）在旅游业中的优势和整体作用。这些方面也在近文献中有所提到。

本论文采用系统综述方式, 在McKinsey’s Global Institute方法论的指导下, 确认BDAI可以在四种方面（预测、产出、提高、以及提供）创造价值。我们也通过阐述相关挑战来增强这个分析方法。

本论文结果显示BDAI通过适当的传播方式来为旅游业中创造价值。采用BDAI战略的好处包括：对旅游提供商带来高效、多产、盈利, 以及对旅游者们带来极度丰富和个性化的旅游体验。我们还总结了采取BDAI战略带来的诸多挑战。采用BDAI战略对旅游业中现有和新参与者的竞争力和弹性起到至关重要的作用。

除了指出了旅游业中BDAI带来的优势, 本论文还提出了一个指南, 来指导当新科技被引进时如何克服挑战。本论文通过对BDAI文献的梳理, 其文献综述结果对经理、学者、和消费者都有重要的启示作用。本论文是首篇在BDAI领域的系统综述, 对拓展电子商务营销、零售、和电子旅游科研有着重大贡献。

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

The purpose of the study is to fabricate a joint between two aluminium metal matrix composites using microwave hybrid heating (MHH).

Taguchi design of experiments was applied to conduct the experimental study. The mechanical properties such as ultimate tensile strength, micro-hardness and porosity were studied. Grey Relational Analysis was applied to understand the significance of fabrication parameters of best performing sample. The dominant factor of fabrication was analysed using ANOVA. The best performance sample was further characterised using X-ray diffraction and field emission scanning electron microscopy. Energy dispersive X-ray was used to analyse the elemental composition of the sample.

The Aluminium Metal Matrix Composite (AMMC) joint was successfully fabricated using MHH. The mechanical properties were mainly influenced by the fabrication factor of exposure time.

The formation of AMMC joint using MHH might explore the way for the industries in the field of joining.

Using Taiwanese enterprises that produce computer and consumer electronics products as case subjects, this study aims to explore the effective product innovation strategies applied to cope with competition in the global market and develop a competitive advantage. The product design strategies and methods of companies that used different types of innovation strategies were then analyzed. Finally, a mapping framework for product innovation and design strategies was proposed for enterprises in the computer and consumer electronics industry in Taiwan.

The two stages of this study were a questionnaire survey and case studies. Stage 1: A questionnaire survey and literature review were performed to explore the innovation strategies used by companies in the Taiwanese computer and consumer electronics industry. The purpose of the literature review was to determine the definitions and content of innovation strategies. Stage 2: Design and R&D managers were interviewed to explore the practical design strategies and approaches to product design in the industry.

These four innovation strategies and ways of product design are closely related to the scale, business type and product development conditions in enterprises. Notably, different innovation strategies have different approaches to product design. Generally, product design emphasizes “new experience” in aggressive innovation enterprises, “new value” in market innovation enterprises, “new service” in technical innovation enterprises and “new positioning” in opportunity innovation enterprises. The findings of this study provide a reference for product R&D and design in enterprises.

In recent years, the global market of computer and consumer electronic products has been fiercely competitive. Therefore, only enterprises that can innovate, respond rapidly and maintain advantages in product design can survive in the market. Hopefully, this case study of companies in the Taiwan computer and consumer electronic industry can provide a reference for product R&D and design. The findings of this study provide a reference for product R&D and design in enterprises, especially the Chinese market.

The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.

Some studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.

This study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.

This study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.

English original movies played an important role in English learning and communication. In order to find the required movies for us from a large number of English original movies and reviews, this paper proposed an improved deep reinforcement learning algorithm for the recommendation of movies. In fact, although the conventional movies recommendation algorithms have solved the problem of information overload, they still have their limitations in the case of cold start-up and sparse data.

To solve the aforementioned problems of conventional movies recommendation algorithms, this paper proposed a recommendation algorithm based on the theory of deep reinforcement learning, which uses the deep deterministic policy gradient (DDPG) algorithm to solve the cold starting and sparse data problems and uses Item2vec to transform discrete action space into a continuous one. Meanwhile, a reward function combining with cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely.

In order to verify the feasibility and validity of the proposed algorithm, the state of the art and the proposed algorithm are compared in indexes of RMSE, recall rate and accuracy based on the MovieLens English original movie data set for the experiments. Experimental results have shown that the proposed algorithm is superior to the conventional algorithm in various indicators.

Applying the proposed algorithm to recommend English original movies, DDPG policy produces better recommendation results and alleviates the impact of cold start and sparse data.

The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.

To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.

An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.

The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.

To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.