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This paper aims to assess the readiness of retail workers to use blockchain technology (BCT) to improve supply chain performance. The assessment was made via a quantitative approach taken using a theoretical framework based on Keller’s motivation model and self-determination theory in the BCT context.

The authors collected data from 567 retail workers from an emerging country through a structured survey questionnaire. The authors tested the hypotheses of the proposed model using Warp PLS 7.0 and controlled firm age, industry type and technological intensity.

Our findings may help firms in making the process of digital transformation inclusive. The authors found that supplier-based attention and motivation through BCT lead to supply chain performance, and that supplier-based satisfaction and trust achieved through BCT positively impact supply chain performance. Further, supplier-based relevance on raw material selection with the higher trust and motivation levels achieved through BCT was found to have a positive impact on supply chain performance.

IT supply chain applications are referred to as “lean” rather than “rich” because they still rely mainly on written and numerical means to present data. When the environment is less ambiguous, then less rich media can be used to facilitate communication. IT supply chain applications allow suppliers to spend time building relationships with other suppliers instead of focusing on administrative tasks, thus enhancing such relationships.

This study can be considered the first to assess retailer readiness to use BCT to improve supply chain performance through the theoretical lens of Keller’s motivation model and self-determination theory.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

Drawing motivation from the greater exposure to uncertainty and condition changes that affect large projects due to their long lifecycle, this paper aims to investigate how the time factor affects the use of governance mechanisms to pursue the success of these projects.

To pursue its aim, the article applies the dichotomization between the hard and soft mechanisms of project governance to the analysis of a historical case study, whose findings are organized over the short, medium and long periods. The case selected is referred to the peculiar water system, made up of tunnels named “bottini,” that was in use in Siena (Italy) as the old aqueduct. Specifically, the study focuses on the project of expansion of this water system that was realized during the 14th century for the construction of the “Bottino maestro di Fontegaia.”

This article highlights the different relevance that, during the lifecycle of large projects, is assumed by hard and soft governance mechanisms, with the former having main relevance in a short and medium period, and the latter usually emerging in the medium period and, subsequently, playing a growing role for the project success in the long period.

The article contributes to the literature on large projects by providing novel insights about how the time factor impacts the governance of these projects. Furthermore, the case study, with its unique history, highlights the relevance of combining effectively the hard and the soft dimensions of project governance to pursue success.

This paper aims to design the nano-titanium dioxide (TiO₂) coating system which has superhydrophilic property, self-cleaning mechanism and antifog property as well as strong adhesion on glass substrate.

Two hydrophilic materials have been used such as TiO₂ nanoparticles as fillers and hydrophilic copolymer, Pluronic F-127 by using simple sol–gel approach. The prepared solution was applied onto glass through dip- and spray-coating techniques and then left for drying at ambient temperature.

The nano-TiO₂ superhydrophilic coating has achieved the water contact angle of 4.9° ± 0.5°. The superhydrophilic coating showed great self-cleaning effect against concentrated syrup and methylene blue where thin layer of water washes the dirt contaminants away. The nano-TiO₂ coating exhibits great antifog performance that maintains high transparency of around 89% when the coated glass is placed above hot-fog vapor for 10 min. The fog droplets were condensed into water film which allowed the transmission of light through the glass. The strong adhesion of coated glass shows no total failure at scratch profile when impacted with scratch load of 500, 800 and 1,200 mN.

Findings will be useful in the development of self-cleaning superhydrophilic coating that is applicable on building glass and photovoltaic panel.

The developed nano-TiO₂ coating is developed by the combination of hydrophilic organic copolymer–inorganic TiO₂ network to achieve great superhydrophilic property, optimum self-cleaning ability and supreme antifog performance.

The findings will be useful for residents in building glass window where the application will reduce dust accumulation and keep the glass clean for longer period.

The synthesis of nano-TiO₂ superhydrophilic coating which can be sprayed on large glass panel and cured at ambient temperature.

The aim of this review is to present together the studies on textile-based moisture sensors developed using innovative technologies in recent years.

The integration levels of the sensors studied with the textile materials are changing. Some research teams have used a combination of printing and textile technologies to produce sensors, while a group of researchers have used traditional technologies such as weaving and embroidery. Others have taken advantage of new technologies such as electro-spinning, polymerization and other techniques. In this way, they tried to combine the good working efficiency of the sensors and the flexibility of the textile. All these approaches are presented in this article.

The presentation of the latest technologies used to develop textile sensors together will give researchers an idea about new studies that can be done on highly sensitive and efficient textile-based moisture sensor systems.

In this paper humidity sensors have been explained in terms of measuring principle as capacitive and resistive. Then, studies conducted in the last 20 years on the textile-based humidity sensors have been presented in detail. This is a comprehensive review study that presents the latest developments together in this area for researchers.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

The main purpose of this paper is to examine the status of poverty and its reduction by following the inclusive development approach. This study is designed to examine the benefits obtained from development programs, assess the government’s commitment to alleviating social inequality, and its impacts on the redistribution of wealth and poverty reduction.

To evaluate the implementation of the various development schemes and enhance grass-roots participation, a survey was carried out on 540 households, selected through multistage stratified sampling techniques in three different states of Punjab. The study employed an exploratory factor analysis on 21 independent variables to identify the key factors influencing poverty reduction subsequently followed by the binary logistic regression to access the sectoral impact of inclusiveness on poverty reduction in Punjab.

Exploratory Factor analysis extracted six key factors from the selected 21 variables, also called statements: “'Housing Development Resources”; “Human Capital Variables”; “Livelihood Essentials”, “Medical and Family Welfare Benefits”; “Receiving Educational Benefits”; and Social Security Benefits’. Binary logistic regression revealed that Housing Development Resources, Human Capital Variables, and Receiving Educational Facilities, significantly predict the likelihood of poverty reduction with inclusive growth in Punjab.

To provide basic amenities to rural people, increased people’s participation, decentralized planning, extended irrigation facilities, improved equipped facilities, and improved cultivation techniques are pivotal. The Indian Government has implemented several programs and projects to develop and support rural households. However, these schemes have faced many challenges such as rigidity, non-adaptability to local conditions, late disbursements of funds, reallocation of funds to unrelated expenditures by some states, embezzlement, and bribery demands. Hence, the findings indicate the presence of pseudo-inclusivity in Punjab’s growth.

The study’s uniqueness lies in its focus on selected districts of Punjab and also its application of exploratory factor analysis and binary logistic regression to construct a statistical model from the selected variables.

This study aims to model tourist activities in a network and explore the properties of the network. Such network enables the author to explain and quantify how tourist activities are connected in determining tourist consumption as well as the organization of destination supply.

The author developed a network formation mechanism to create edges between nodes based on the joint probability of a pair of activities undertaken by tourists at a destination. By adjusting network sparsity, the author created an ensemble of four topologically similar networks for empirical testing. The author used tourist activity data of Hong Kong inbound tourists to test the network model.

The author found a robust hub–periphery topological structure of the tourist activity network. In addition, the network is featured by high clustering, short diameter and positive correlations between four node centralities, namely, degree, closeness, betweenness and eigenvector centralities. The author also generated the k-cores of the networks to further unravel the structure of hub nodes. The author found that the k-cores are dominated by tourist activities related to shopping or sightseeing, suggesting the high complementarity of these activities.

This study provides a different lens through which tourist consumption can be understood from a macroscopic angle by examining network topology and from a microscopic angle by examining node centralities.

To the best of the author’s knowledge, this is the first study attempting to model tourist activity and consumption in a network and explore the properties of the network. Not only has this study provided a new real-world network for network research, but it has also suggested an innovative modeling approach for tourist behavior research.