Shu-hsien Liao, Retno Widowati and Shang-Chen Chan
The delivery service business model is the final link in logistics for both online-and-offline (O2O) businesses. O2O business models combine e-commerce and physical commerce…
Abstract
Purpose
The delivery service business model is the final link in logistics for both online-and-offline (O2O) businesses. O2O business models combine e-commerce and physical commerce, using online marketing techniques to drive consumption in physical channels. Regarding collaborative recommendation, a recommendation mechanism involves two or more parties, such as logistics, retail firms and e-commerce operators, working together to obtain necessary consumer information and knowledge, such as profiles and preferences, as the basis for personalized product recommendations. Thus, delivery service and O2O purchasing integration for retail collaborative recommendations development are valuable research issues on retail and distribution management.
Design/methodology/approach
This study implements two-stage data mining analytics for clustering and association rules analysis, to investigate Taiwan consumers' (n = 2,169) preferences for delivery service. This process clarifies delivery service and O2O purchasing behaviours and preferences to find knowledge profiles/patterns/rules for retail collaborative recommendations.
Findings
This study first found several knowledge profiles/patterns/rules on our subjects. Discussion and implications for Taiwan retail and delivery service operators are also presented. The research findings show that delivery service is a valuable resource for O2O business models for retail collaborative recommendations.
Originality/value
Regarding originality and value, collaborative recommendation is a mechanism that seeks to understand consumers' lives and context. From the retail perspective, delivery and retail operators can join to discover valuable data on the platform through interactive data on consumer preferences for delivery service and O2O purchasing. These operators can then summarize the information to make collaborative recommendations more accurately, thus increasing O2O purchasing.
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Linling Zhang, Shuangqun Li and Wei Zhang
The purpose of this paper is to explore carbon emission reduction of electric vehicles from the perspective of electricity consumption.
Abstract
Purpose
The purpose of this paper is to explore carbon emission reduction of electric vehicles from the perspective of electricity consumption.
Design/methodology/approach
Electric vehicles (EVs) consume large amounts of electricity, thereby generating large amounts of carbon dioxide (CO2) emissions, so there is an urgent need to consider whether EVs have greater potential for reducing carbon emissions than other modes of transport. In this paper, the carbon emission reduction potential (CERP) coefficients of EVs are examined under three different scenarios from an interprovincial electricity trading perspective. Scenario analysis was used to quantify the CERP of EVs in 18 provinces in China.
Findings
The results show the following: (1) The higher the proportion of general-fuel vehicles in all transportation, the higher the CERP of EVs. (2) Interprovincial power trading affects the proportion of coal power consumed in a province, and the higher the proportion of clean power in the purchased power, the lower the proportion of coal power consumed in that province. (3) The proportion of coal power in the electricity consumption of a province is correlated negatively with the CERP of EVs in that province.
Originality/value
This paper quantifies the CERP of EVs compared with other modes of transport and gives provinces a more intuitive understanding of the CERP of EVs. Furthermore, we derive the carbon emission shift out of each province via the electricity trading paths among provinces, analyzing the impacts of the variability between different provinces on EV carbon emissions.
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Sami Ullah, Tooba Ahmad, Khuram Shahzad, Mohit Kukreti, Muhammad Rehan Shaukat and Abdul Sami
Sustainability is a pressing global issue that affects everyone on the planet. This study aims to provide a deeper understanding of the nuanced interplay between mindful…
Abstract
Purpose
Sustainability is a pressing global issue that affects everyone on the planet. This study aims to provide a deeper understanding of the nuanced interplay between mindful organizing (MO), organizational learning capability (OLC), leadership commitment to sustainable development goals (SDGs) and sustainability performance, adopting the theoretical foundation of organizational learning.
Design/methodology/approach
The survey data from 728 middle management employees of exporting firms in Pakistan were collected at two points. The mediated moderation analysis was performed through structural equation modeling in AMOS 26.
Findings
The results indicate a positive relationship between MO and sustainability performance. The mediating effect of OLC highlights that firms that acquire, assimilate and apply knowledge and insights leverage their MO strategies to improve sustainability performance more effectively. Additionally, the leadership commitment to SDGs amplifies the positive effect of OLC on sustainability performance.
Practical implications
These results have important implications for managers and policymakers who seek to promote sustainability in organizations. The findings suggest that cultivating a mindful organizational culture and investing in learning capability enhance sustainability performance. Exporting firms should develop comprehensive learning programs that embed mindfulness and sustainability into the core of organizational culture. More interdisciplinary research is needed to integrate insights from environmental science, psychology, management and organizational behavior.
Originality/value
This research stands out for its integrative approach, practical relevance, empirical examination of important concepts and alignment with global sustainability goals. Exporting firms must understand how organizational learning capabilities and MO can be harnessed to achieve sustainable outcomes.
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Sarah Wahba, Sara El-Deeb and Sandra Metry
This study draws on social influence and social comparison theories to investigate the effect of social media influencers on intention to visit with the mediating role of upward…
Abstract
Purpose
This study draws on social influence and social comparison theories to investigate the effect of social media influencers on intention to visit with the mediating role of upward social comparison. It studies the reasons underlying people's intention to visit a destination from an emotional perspective.
Design/methodology/approach
PLS-SEM models were applied to a total of 527 responses.
Findings
This study provides empirical evidence supporting the Source Credibility Model's elements within the context of influencer marketing and travel intentions. Furthermore, it expands the current knowledge of upward social comparison emotions by reporting that upward contrastive emotions would partially mediate the relationship between expertise and intention to visit, and that upward assimilative emotions would fully mediate the relationship between attractiveness and intention to visit while partially mediating the relationship between trustworthiness and expertise and intention to visit.
Practical implications
The article adds new insights to tourism marketing as well as helps both destination marketers and travel influencers. For marketers, it is advised to collaborate with credible influencers known for expertise and trustworthiness, leveraging their perspectives to rebuild travel confidence and reassure tourists about safety measures. Travel influencers are recommended to convey their intrinsic passion and enthusiasm through their posts to create an inspiring connection with the audiences.
Originality/value
This paper is the first to address the relationship between travel influencers and intention to visit with the mediation role of both positive and negative emotions.
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Jing Li, Rui Ling, Fangjie Sun, Jinming Zhou and Haiya Cai
This paper adds risk perception and personalized human-computer interaction to the technology acceptance model, and further analyzes the impact of personalized unmanned ride…
Abstract
Purpose
This paper adds risk perception and personalized human-computer interaction to the technology acceptance model, and further analyzes the impact of personalized unmanned ride hailing on users' behavior intention.
Design/methodology/approach
This study model was tested using a sample of 299 social media users from China and we apply structural equation modeling (SEM) to build the theoretical framework.
Findings
Our results show that perceived ease of use has a greater positive impact on behavior intention compared to perceived usefulness. In addition, we find that the impact of risk perception on behavior intention is manifested in a number of ways, including people’s risk perception of the new technology, people’s risk perception of data leakage, and so on. Finally, we find that users’ personalized human-computer interaction has a positive effect on their perceived ease of use, perceived usefulness, and behavior intention.
Originality/value
Our study contributes to illuminate the pivotal role of tailoring the human-computer interface to individual preferences and needs for ride-hailing platforms from the perspective of behavior intention.
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Ahmet Cagri Kilinc, Turker Turkoglu, Harun Mert Ilbeyli, Sare Celik and Yunus Emre Nehri
The purpose of this study is to develop a low-cost and efficient method for 3D printing CuSn15 bronze alloy parts using a pneumatic extrusion system. By avoiding complex processes…
Abstract
Purpose
The purpose of this study is to develop a low-cost and efficient method for 3D printing CuSn15 bronze alloy parts using a pneumatic extrusion system. By avoiding complex processes such as filament preparation and solvent/catalytic debinding, the study aims to streamline the low-cost production process of metallic components while maintaining high mechanical performance. The research also seeks to evaluate the effects of different sintering temperatures and times on the mechanical properties of the printed parts.
Design/methodology/approach
A simple and cost-effective pneumatic extrusion system was designed to 3D print a metal paste containing CuSn15 alloy powders. The metal paste was prepared by manually mixing of CuSn15 powders, carboxymethyl cellulose and distilled water. The printed parts were subsequently dried and sintered at various temperatures and times to study the effects of these parameters on the material properties. Tensile test and scanning electron microscope analysis were conducted to assess the structural integrity and mechanical performance of the samples.
Findings
The study found that the pneumatic extrusion system enabled the successful 3D printing of CuSn15 bronze alloy parts without the need for complex processes. Increasing sintering temperature led to improved mechanical properties and decreased porosity. Increasing the sintering time at 820 °C led to a reduction in mechanical performance. The study demonstrated that the sintering parameters significantly influence the porosity and mechanical properties of the printed parts.
Originality/value
This study introduces a novel approach to 3D printing CuSn15 bronze alloy using a pneumatic extrusion system, eliminating the need for traditional filament preparation and solvent/catalytic debinding processes. The research provides new insights into the effect of sintering parameters on the mechanical properties of additively manufactured metal parts. By simplifying the production process, this study offers a low-cost, efficient method for producing complex-shaped metallic components, potentially expanding the applicability of 3D printing in industries such as electronics, marine and mechanical engineering.
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Yu Liu and Ziming Zeng
Previous research mainly uses graph neural networks on syntactic dependency graphs, often neglecting emotional cues in sarcasm detection and failing to integrate image features…
Abstract
Purpose
Previous research mainly uses graph neural networks on syntactic dependency graphs, often neglecting emotional cues in sarcasm detection and failing to integrate image features for multimodal information effectively. To address these limitations, this study proposes a novel multimodal sarcasm detection model based on the directed graph isomorphism network with sentiment enhancement and multimodal fusion (DGIN-SE-MF).
Design/methodology/approach
The approach extracts image and text features through vision transformer and BERT, respectively. To deeply integrate the extracted features, the author develops a text-guided multi-head attention fusion mechanism module. Subsequently, a directed graph is constructed through SE and the multimodal factorized bilinear pooling method to integrate image features into the graph. The DGIN then fuses the image and text features, using a weighted attention mechanism to generate the final representation.
Findings
The model is validated on three datasets: English, Chinese and an Indonesian–English dataset. The results demonstrate that the proposed model consistently outperforms other baseline models, particularly on the Chinese and English sarcasm datasets, achieving F1 scores of 88.75 % and 83.10 %, respectively.
Originality/value
The proposed model addresses the inadequacies of previous methods by effectively integrating emotional cues and image features into sarcasm detection. To the best of the authors’ knowledge, this is the first work to leverage a DGIN-SE-MF for this task, leading to significant improvements in detection performance across different languages.
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Yazhe Chen, Qingyu Shang, Youwei Zhang, Ying Yao, Adesh Kumar Tomar, Risheng Long and Max Marian
This study aims to investigate the mechanical and tribological behavior of 70Mn steel with different laser re-melted textured patterns.
Abstract
Purpose
This study aims to investigate the mechanical and tribological behavior of 70Mn steel with different laser re-melted textured patterns.
Design/methodology/approach
Laser surface re-melting (LSR) was used to manufacture various textured patterns (i.e. line, grid and mixed) on both the original and heat-treated 70Mn steel plates. The micro-hardness, microstructure, tensile strength, yield strength, elongation, coefficients of friction (COF) and worn morphologies were characterized to evaluate the impact of different textured patterns on the overall performance.
Findings
The results show that re-melted unit exhibited the highest surface hardness on the subsurface. The increase in surface hardness of the re-melted unit for the heat-treated 70Mn steel samples was much lower than that of the original ones. The re-melted textured patterns did not improve the tensile strength, yield strength and elongation of either original or heat-treated 70Mn steel samples. The re-melted textured patterns effectively reduced the average COFs of heat-treated 70Mn steel samples, but increased friction of the non-heat-treated samples.
Originality/value
This study provides valuable insights into enhancing the mechanical properties and tribological characteristics of 70Mn steel, particularly in the automotive, heavy machinery and high-load application sectors. These industries have stringent requirements for durability and friction control, and the findings of this research are expected to effectively extend the lifespan of mechanical components.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0443/
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Maixia Shang, Jinglin Liu, Mengqi Li and Xiaobao Chai
The purpose of this paper is to present the performance requirements for the all-electric high lift motors and the inductance calculation process for the fractional slot…
Abstract
Purpose
The purpose of this paper is to present the performance requirements for the all-electric high lift motors and the inductance calculation process for the fractional slot concentrated winding.
Design/methodology/approach
This paper presents the research work related to all-electric aircraft high lift motors and the inductance calculation process of fractional slot concentrated winding. Firstly, this paper introduces the performance requirements for the high lift motor and summarizes the general process for calculating the inductance in fractional slot motors. Secondly, the analytical model of winding armature total inductance is obtained by the winding function method. Thirdly, a straightforward calculation method is employed for determining the total slot leakage inductance. Finally, the accuracy of the inductance calculation and controllability of the motor are confirmed through finite element model and motor control strategies.
Findings
In the fractional slot concentrated winding, the armature total inductance is equal to the armature self-inductance plus the armature mutual inductance. The slot leakage inductance is divided into the slot leakage self-inductance and the slot leakage mutual inductance. This allows inductance to be obtained quickly without finite element model.
Originality/value
This paper provides the inductance results of analytical and finite element simulation; the control strategy is employed to verify the conformity of the design requirements and control performance under the rated conditions. The implementation of double verification assures the practicality and effectiveness of the high lift motor.
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Yuan Sun, Shuyue Fang, Anand Jeyaraj and Mengyi Zhu
This study aims to explore how communication visibility affects employees’ work engagement from the negative perspective of employees’ perceived overload in the context of…
Abstract
Purpose
This study aims to explore how communication visibility affects employees’ work engagement from the negative perspective of employees’ perceived overload in the context of enterprise social media (ESM) and the role of ESM policies in the relationship between communication visibility and perceived overload.
Design/methodology/approach
This study examines how communication visibility (i.e. message transparency and network translucence) affects employees’ perceived overload (i.e. information overload and social overload), which in turn affects employees’ work engagement, and how ESM policies moderate the relationship between communication visibility and perceived overload. Partial least squares (PLS) analysis was conducted on data gathered from 224 ESM users in workplaces.
Findings
Communication visibility has significant positive impacts on perceived overload, perceived overload has significant negative impacts on work engagement and ESM policies negatively moderate the relationships between communication visibility and perceived overload, except for the relationship between message transparency and social overload.
Practical implications
The findings provide new insights for organizational managers to formulate ESM policies to mitigate perceived overload and guidance for ESM developers to improve ESM functions to alleviate perceived overload.
Originality/value
This study provides empirical evidence to explain the role of communication visibility and perceived overload in employees’ work engagement, which contributes to the existing literature on the negative impacts of communication visibility.