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The purpose of this paper is to examine the application of C-shaped QFD 3D Matrix in comparing process characteristics (PC), performance aspects (PA) and customer requirements, simultaneously and to prioritize the first two sets, respectively.

A three dimensional matrix has been developed with three sets of PC, PA and customers’ requirements and C-shaped matrix has been applied for simultaneous comparison of the dimensions and prioritization of the subsets of PC and PA. The proposed approach has been examined in a post bank.

Findings confirm the possibility of simultaneous comparison and prioritization of the three sets of dimensions of this study in post bank services. In addition, “growth and learning” and “bilateral relationship with suppliers” had the first priorities among PA and PC, respectively.

While the proposed approach has many advantages, filling the matrixes is time-consuming. Since illustrating the 3D matrix was not possible, the matrix was separated into five two-dimensional matrixes.

Compared to the studied literature, the proposed approach is practically new in the post bank services.

Nowadays, the Internet of Things (IoT) offers new intelligent services and business model opportunities by extending the internet connectivity into physical devices and daily objects. Recently, this technology has had a deep impact on all the aspects of human societies from industry to management and business. Therefore, the purpose of this paper is to predict the impact of using IoT services on the development of electronic business (e-business).

For evaluating the model’s elements, a questionnaire was designed. It was revised by experts with significant experiences. For statistical analysis, SmartPLS 3.2 is used.

The findings have confirmed the validity of the proposed model. The results also have indicated that the quality of IoT services (scalability, availability, reliability and ease of use), security of IoT services (trust, reputation, privacy and encryption) and IT knowledge of users (usage skills, awareness, experience and accuracy) have a positive and significant impact on development of e-business. Furthermore, the obtained results have revealed that the operating cost of IoT services (transmission time, storage capacity, functionality and stability) is significantly influenced by the development of e-business.

By capturing a number of important factors in the proposed model, this study can provide a significant theoretical influence for researchers in the related area of interest. In fact, this study provides a worthwhile direction by assessing e-business performance, which, so far, has not been well assessed in many developing countries.

From a practical perspective, the statistical results support the crucial role of the following factors: quality of IoT services, the security of IoT services, operating cost of IoT services and IT knowledge of users. Hence, aspects relating to these factors must be the focus of attention of any organization in their endeavor to development of e-business.

This study meaningfully contributes to the knowledge and literature by focusing more on development e-business and IoT as original technology for further understanding, investigating other important features and applying advanced statistical analyses technique. By doing so, this study was able to develop the current understanding regarding the main aspects of development e-business and IoT services.

Sharing knowledge with business partners is a challenging issue as firms need to share their valuable know-how assets with individuals or other companies out of their organizational boundaries. As supply chain management (SCM) deals with various stakeholders, firms face difficulties with privacy and ownership when they share their know-how with suppliers or business partners. This study introduces blockchain technology as a mediator in improving knowledge sharing (KS) practices in supply chains.

The data have been collected from surveys with 116 experts working in blockchain start-ups and organizations, and the authors used structural equation modeling for its analysis.

The results show that two features of blockchain technology, namely transparency and security, have the highest impacts on mediating knowledge sharing impacts on supply chain performance. The authors’ findings also highlight that among the performance metrics of SCM, speed is highly improved when blockchain technology is used for knowledge sharing. Their study provides guidance for managers on how to improve SCM performance through KS, which is empowered by a blockchain system.

The authors’ findings help organizations to improve supply chain actions, improve innovation, enhance competitive advantage and increase the speed of relationships in the supply chain. The research also contributes literature by analyzing the key factors showing how knowledge sharing structure may be improved by blockchain technology which would be helpful for both academics and practitioners.

Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.

The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.

The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.

This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.

This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.