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People’s economic behaviors are often embedded in their social networks. Based on social embeddedness theory, this study investigates how customers’ social network embeddedness (structural and relational embeddedness) affects their online referral intentions through four studies.

This study adopted a vignette experimental design and used both offline and online channels to collect data. The respondents were undergraduate students and consumers, covering different age groups and occupation types. We used the ANOVA and bootstrap methods of SPSS to conduct data processing and hypothesis testing.

We found that when fewer (vs more) people in a customer’s social network participated in reward referral programs (RRPs), the customer’s online referral intention was also lower because of higher perceived social costs and lower perceived control. However, when the tie strength, reciprocity and normative influence between customers and people who have participated in RRPs were high, customers’ online referral intention also increased, ignoring the influence of participants’ network size. In these relationships, perceived social costs and perceived control played important mediating roles.

The results provide practical implications for enterprises to develop efficient RRPs in the context of the increasing popularity of the mobile Internet and intelligent terminals.

This study offers insights into the influence mechanism of social network embeddedness on referral likelihood, contributing to the field of RRPs and social embeddedness theory.

This study aims to deeply understand customer experiences toward Internet of Things (IoT) applications in retail by developing machine learning models for aspect-based sentiment analysis (SA). It includes creating a related terms dictionary and proposing implications for retail businesses in Vietnam based on these analyses. The ultimate goal is to gain insights into customer opinions and assist administrators in formulating effective digital transformation and business strategies within the Vietnamese market.

Initially, this research uses qualitative methods to identify different aspects of customer experience at stores equipped with IoT applications. Then, quantitative methods were applied through classification machine learning models which were trained on the annotated data set to classify comments into aspects and sentiments. Finally, the classification results were analyzed and visualized to draw implications about customer opinions of these stores.

This study collected 77,042 customers’ comment from potential and actual customers who have ever shopped at retail stores with IoT applications deployed worldwide, identified ten new aspects of customer experience in this field and built a dictionary of related terms. Furthermore, this study contributed two efficient ensemble models with an accuracy of 81% and 89% for analyzing aspects and customer sentiments, respectively. This study also proposes implications for managers regarding the use of IoT technology in retail stores to improve shopping experiences for customers.

This study’s findings help managers develop appropriate digital transformation and business strategies for integrating IoT technology into retail stores, especially for retail businesses in the Vietnamese market based on the analysis results and proposed model.

This paper examines to what extent blockchain creates legitimacy and trust in different modes of public governance. It posits that while blockchain aims for political legitimacy through decentralising, immutable and consensus-based mechanisms, the execution of these mechanisms is limited in legitimating governance, which has knock-on effects on trust. It provides an original contribution by recontextualising and reframing blockchain as a governance mechanism that should, and must, perform a legitimating function in order to engender trust.

The research adopts a comprehensive framework for understanding the legitimacy of blockchain governance, positioning it in terms of co-governance, self-governance and hierarchical governance modes. It systematically analyses blockchain whitepapers, legislation, government documents and other sources in three paradigmatic case studies where blockchain governance failed. These cases are then used to assess blockchain according to three key characteristics of decentralisation, immutability and consensus.

The research finds that blockchain’s use in governance settings still relies on legitimacy conferred from other sources – namely state – in order to generate trust. Significant limitations in its de facto political decentralisation, immutability and consensus protocols can create failures in co-governance, self-governance and hierarchical-governance applications, thus limiting the legitimation function of blockchain in facilitating political trust.

These findings are significant in highlighting blockchain’s limitations as a decentralised, immutable and consensus-driven legitimating tool, which has knock-on effects on trust in technology and governance more broadly. It also has broader implications in more clearly highlighting the interconnectedness of political trust and legitimacy in governance processes.

The anti-friction and anti-wear properties of WS₂@GO composites on paraffin liquid were investigated with a four-ball tribometer.

A series of graphene oxide (GO) nano hybrid composites decorated with tungsten disulfide (WS₂) were prepared in-suit by hydrothermal strategy.

The results showed that compared to the virgin oil, friction coefficient and diameter of wear scare of lubricant oil containing W/G = 1:1 hybrid composite was reduced by 42.7% and 31.6%, respectively. At the microscopic, the excellent lubrication performance resulted from the tribo-chemical reaction on the sliding interface, which promotes the formation of tribo-film with a thickness of 8 nm. The carbonization compound, WO₃ and Fe₂O₃ in the tribo-film results from the tribo-chemical reactions at the sliding interface, which can improve the stability and strength of tribo-film. Thereby the metal surface was further protected from friction and wear.

A series of WS₂@GO composites were prepared in-suit by a hydrothermal strategy, and the tribo-film was analyzed by the transmission electron microscope and X-ray photoelectron spectrometer.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0397

Polyethylene terephthalate (PET) as a fiber molding polymer is widely used in aerospace, electrical and electronic, clothing and other fields. The purpose of this study is to improve the thermal insulation performance of polyethylene terephthalate (PET), the SiO₂ aerogel/PET composites slices and fibers were prepared, and the effects of the SiO₂ aerogel on the morphology, structure, crystallization property and thermal conductivity of the SiO₂ aerogel/PET composites slices and their fibers were systematically investigated.

The mass ratio of purified terephthalic acid and ethylene glycol was selected as 1:1.5, which was premixed with Sb₂O₃ and the corresponding mass of SiO₂ aerogel, and SiO₂ aerogel/PET composites were prepared by direct esterification and in-situ polymerization. The SiO₂ aerogel/PET composite fibers were prepared by melt-spinning method.

The results showed that the SiO₂ aerogel was uniformly dispersed in the PET matrix. The thermal insulation coefficient of PET was significantly reduced by the addition of SiO₂ aerogel, and the thermal conductivity of the 1.0 Wt.% SiO₂ aerogel/PET composites was reduced by 75.74 mW/(m · K) compared to the pure PET. The thermal conductivity of the 0.8 Wt.% SiO₂ aerogel/PET composite fiber was reduced by 46.06% compared to the pure PET fiber. The crystallinity and flame-retardant coefficient of the SiO₂ aerogel/PET composite fibers showed an increasing trend with the addition of SiO₂ aerogel.

The SiO₂ aerogel/PET composite slices and their fibers have good thermal insulation properties and exhibit good potential for application in the field of thermal insulation, such as warm clothes. In today’s society where the energy crisis is becoming increasingly serious, improving the thermal insulation performance of PET to reduce energy loss will be of great significance to alleviate the energy crisis.

In this study, SiO₂ aerogel/PET composite slices and their fibers were prepared by an in situ polymerization process, which solved the problem of difficult dispersion of nanoparticles in the matrix and the thermal conductivity of PET significantly reduced.

Amid the increasing water risks faced by firms, external investors are becoming more interested in corporate water disclosure and research on its drivers has become prominent. This paper aims to investigate the impact of water resource tax (WRT) on water disclosure and other related drivers.

This study uses the WRT policy as a quasi-natural experiment and applies the difference-in-differences method.

The results indicate that WRT policy significantly stimulates water disclosure. Improving green innovation and strengthening internal control are potential channels through which WRT works. Moreover, WRT’s effect is more pronounced in firms that face high institutional pressures and have better internal resource support.

The findings suggest that water-sensitive firms should disclose water information to acquire resources from external stakeholders to support their green transition. It also provides implications for governments to incorporate other external forces in shaping the direction and intensity of WRT and consider the resource constraints of small and private firms in green transformation.

This study is of assistance in promoting water environmental protection in areas experiencing water stress and provides an opportunity for external stakeholders (external investors, nongovernmental organizations, governments, consumers, suppliers, communities and media) to advocate the water disclosure of firms with high water risks.

The attempt is novel in the context of considering the water regulation risks and the demands of external stakeholders. It provides new insights into the factors influencing water disclosure from the perspective of political stakeholders.

Toner is a crucial dry colorant composite used in printing based on the electrophotographic process. The quality of printed images is greatly influenced by the toner production method and material formulation. Chemically in situ polymerization methods are currently preferred. This paper aims to optimize the characteristics of a composite produced through emulsion polymerization using common raw materials for electrophotographic toner production.

Emulsion polymerization provides the possibility to optimize the physical and color properties of the final products. Response surface methodology (RSM) was used to optimize variables affecting particle size (PS), PS distribution (PSD), glass transition temperature (T_g°C), color properties (ΔE) and monomer conversion. Box–Behnken experimental design with three levels of styrene and butyl acrylate monomer ratios, carbon black pigment and sodium dodecyl sulfate surfactant was used for RSM optimization. Additionally, thermogravimetric analysis and surface morphology of composite particles were examined.

The results indicated that colorants with small PS, narrow PSDs, spherical shape morphology, acceptable thermal and color properties and a high percentage of conversion could be easily prepared by optimization of material parameters in this method. The anticipated outcome of the present inquiry holds promise as a guiding beacon toward the realization of electrographic toner of superior quality and exceptional efficacy, a vital factor for streamlined mass production.

To the best of the authors’ knowledge, for the first time, material parameters were evaluated to determine their impact on the characteristics of emulsion polymerized toner composites.

Green and digital transitions (twin transition) help in achieving climate neutrality, cutting down the pollution level and reviving biodiversity. Organizations can mitigate their ecological footprint, improve their digital portfolio and fulfil their ethical responsibilities by fostering a sustainable global community and adhering to a well-defined plan. In such backdrop, this study aims to propose strategies by identifying the strengths, weaknesses, opportunities and threats of the twin transition and to categorize and evaluate the internal and external factors for achieving environmental sustainability.

Following a comprehensive review of the literature, 27 factors have been identified and analyzed using the strengths, weakness, opportunities and threat matrix (SWOT). Threats, opportunities, weakness, strength matrix analysis approach is used to limit risk, overcome weaknesses and take advantage of opportunities. Effective strategies are designed and implemented using strategic planning action and evaluation matrix (SPACE) and quantitative strategic planning (QSPM) approach.

The study found that aggressive strategies considering economic strength and global opportunities must be used to accomplish green growth objectives. Out of three strategies, the focus on green technological innovation by taking advantage of financial mechanisms at the international level is highly recommended to achieve environmental sustainability.

Through promoting green and digital transformation, the current study provides insightful policy recommendations for economic development. It may help to improve operational efficacy and to obtain a competitive edge by successfully limiting their adverse environmental effects in the global market through the implementation of twin transition. The process of developing a strategy aids stakeholders, including society, workers and other parties, in giving environmental sustainability a top priority while making financial decisions.

Strategy development helps society, employees and all the other stakeholders to prioritize their green and digital transition decisions for environmental sustainability.

The current study identifies the factors formulating strategies using SWOT, TOWS, SPACE and QSPM matrix analysis for achieving environmental sustainability in the pervasive twin transition.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

The purpose of this study is to find optimum production parameters using the Taguchi method in terms of printing material (a composite material produced from polylactic acid [PLA] and polypropylene [PP]) impacts on wear and friction.

A composite filament coil was fabricated from PLA and PP pellets using a lab-based extrusion device. The hardness and chemical structure of the filaments were examined. The filament-based material extrusion process was used to produce cylindrical shapes for tribological (pin-on-disc) analyses. Moreover, Taguchi methodology was performed to find the most influential parameter on tribological characteristics.

The hardness (Shore D) values of the filaments were polylactic acid (PLA) (85), polypropylene (PP) (58) and PLA and PP mixture of 50:50 (PLAPP) (63). Fourier-transform infrared spectroscopy (FTIR) and thermogravimetric analysis results show that PLA and PP mixtured successfully. The diameter deviation of all samples was found as lower than 5%. The honeycomb pattern presented the best dimensional accuracy. Changing printing patterns and temperatures affect the friction profile. Coefficient of friction (COF) values of cylindrical samples of PLA and PP samples around 0.35 and 0.31, respectively. Overall, material type for wear resistance applications is a key point.

To the best of the author’s knowledge, there seems to be a lack of 3D-printed PP and PLAPP studied in terms of tribological aspects as a neat polymer. The tribological profiles of PP, PLA and PLAPP (PLA and PP mixture of 50:50) were investigated in this study. The author believe that this is a preliminary study that might close the gap and add some novel information to the current literature.