Kenneth Shiu Pong Ng, Jiru Zhang, Jose Weng Chou Wong and Kennis Kaiqi Luo
Food delivery apps (FDAs), as a well-known technology, have been widely adopted by restaurants and customers. Different from existing studies in this field that mainly focused on…
Abstract
Purpose
Food delivery apps (FDAs), as a well-known technology, have been widely adopted by restaurants and customers. Different from existing studies in this field that mainly focused on the technical (internal) parts, the study aims to introduce a new framework by linking up technical (internal) factors and service-related (external) factors in the context of FDAs. This study also empirically analyzes a comprehensive model that identifies the impacts of internal and external factors in FDAs on the continuous use intention.
Design/methodology/approach
This study proposes a comprehensive model integrating internal and external factors with a sample of 498 respondents who had ordered or purchased food through delivery apps for the partial least squares structural equation modeling (PLS-SEM) analysis.
Findings
The results of the study show that time-saving is the most significant factor on customers' perceptions, and various food choices and usefulness have also direct positive impacts on perceived value and satisfaction. In addition, perceived value shows a stronger effect than satisfaction on customers' continuous usage.
Originality/value
These findings provide a new perspective on FDAs, which not only simplify the elements of FDAs but also classify internal and external factors to foster the theoretical and practical development. Ultimately, the model proposed and validated in this study can serve as the basis for future FDAs and other service apps development.
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Xuetao Sun, Yu Zhao and Guangsheng Zhang
China’s overall grain production efficiency is currently relatively low, and how to improve it is key to high-quality grain development. High-standard farmland construction fills…
Abstract
Purpose
China’s overall grain production efficiency is currently relatively low, and how to improve it is key to high-quality grain development. High-standard farmland construction fills the gaps in grain production, providing a path for improving comprehensive grain production capacity.
Design/methodology/approach
This paper analyzes grain production data from 281 Chinese cities and uses the meta-Malmquist index to calculate total factor productivity (TFP) of grain and the spatial autoregressive model with spatial error (SARAR) model to analyze the impact of high-standard farmland construction on TFP of grain. Finally, it tests the model for robustness and heterogeneity.
Findings
TFP of grain has room for improvement, and technological efficiency has a greater impact on it than technological progress. TFP of grain in the eastern region is significantly higher than that in central and western regions. High-standard farmland construction can significantly improve TFP and technology efficiency of grain, and its economic benefits are directly affected by the differences in regional grain economic development. High-standard farmland construction improves TFP of grain through agricultural mechanization, and its impact is heterogeneous; TFP of grain in the (poor, plain) eastern region is more likely to share its benefits.
Originality/value
This paper evaluates whether high-standard farmland construction has achieved its purpose of enhancing comprehensive grain production capacity. It offers novel insights into elevating element quality and comprehensive grain production capacity, provides a theoretical reference from the perspective of element quality and draws corresponding policy implications for high-standard farmland.
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Bahram Abediniangerabi, Mohsen Shahandashti and Atefe Makhmalbaf
The purpose of this study is to investigate the effect of panel connections on the hygrothermal performance of facade panels using a coupled, transient heat and moisture transfer…
Abstract
Purpose
The purpose of this study is to investigate the effect of panel connections on the hygrothermal performance of facade panels using a coupled, transient heat and moisture transfer analysis.
Design/methodology/approach
A coupled, transient heat and moisture transfer analysis has been conducted to investigate the effect of panel connections in the hygrothermal behavior of facade panels. Governing partial differential equations for the coupled heat and moisture transfer were formulated. Four panel connections proposed by pre-cast/pre-stressed concrete institute were modeled for the ultra-high performance fiber-reinforced concrete facade panel as illustrations and a finite element method was used to solve the numerical models.
Findings
The results of heat transfer analysis showed that steel connections could significantly reduce the thermal resistivity of facade panels by converging heat fluxes and acting as thermal bridges within facade panels. The results also showed that the maximum heat flux in the steel connector of the panel to foundation connection was 10 times higher compared to the other connections. Also, the results of moisture transfer showed that air gaps between the panels had higher moisture flux compared to the other layers in the models. The results show the significant importance of panel connections in the energy performance analysis of facade systems. They also highlight the importance of devising novel connection designs and materials that consider the transient, coupled heat and moisture transfer in the connections to effectively exploit the potential opportunities provided by innovative facade systems to improve building energy efficiency.
Originality/value
This paper, for the first time, investigates the effect of panel connections in the hygrothermal performance of building facade systems using a coupled, transient heat and moisture transfer analysis.
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Sudhanshu Joshi, Manu Sharma, Shalini Bartwal, Tanuja Joshi and Mukesh Prasad
The study proposes to determine the impending challenges to lean integration with Industry 4.0 (I4.0) in manufacturing that aims at achieving desired operational performance…
Abstract
Purpose
The study proposes to determine the impending challenges to lean integration with Industry 4.0 (I4.0) in manufacturing that aims at achieving desired operational performance. Integrating lean and Industry 4.0 as the two industrial approaches is synergetic in providing operational benefits such as increasing flexibility, improving productivity, reducing cost, reducing delivery time, improving quality and value stream mapping (VSM). There is an urgent need to understand the integrated potential of OPEX strategies like lean manufacturing and also to determine the challenges for manufacturing SMEs and further suggest a strategic roadmap for the future.
Design/methodology/approach
The current work has used a combined approach on interpretative structural modeling (ISM) and fuzzy Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) approach to structure the multiple level analysis for the implementation challenges to integrate OPEX strategies with Industry 4.0.
Findings
The research has found that the indulgence of various implementation issues like lack of standardization, lack of vision and lack of trained support, all are the major challenges that inhibit the integration of OPEX strategies with I4.0 technologies in manufacturing.
Research limitations/implications
The research has investigated the internal factors acting as a roadblock to lean and Industry 4.0 adoption. Further studies may consider external factors to lean and Industry 4.0 implementation. Also, further research may consider other operational excellence approaches and extend further to relevant sectors.
Practical implications
This study provides the analysis of barriers that is useful for the managers to take strategic actions for implementing OPEX strategies with I4.0 in smart manufacturing.
Originality/value
The research determines the adoption challenges towards the integrated framework. This is the first study to explore challenges in integrating OPEX strategies with I4.0 technologies in manufacturing SMEs.
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Julian M. Müller, Nikolai Kazantsev, Richard Allmendinger, Amirhossein Salehi-Amiri, Jacqueline Zonichenn Reis, Shaden Jaradat, Helena Bartolo and Paulo Jorge Da Silva Bartolo
This conceptual paper aims to present a perspective on how to engineer sustainability through the prism of Industry 4.0 technologies and outline propositions to guide future…
Abstract
Purpose
This conceptual paper aims to present a perspective on how to engineer sustainability through the prism of Industry 4.0 technologies and outline propositions to guide future research.
Design/methodology/approach
This study presents a literature review developing four research propositions, focusing on the nine leading technologies underpinning Industry 4.0 to engineer economic, environmental and social sustainability dimensions.
Findings
The authors derive benefits and challenges of Industry 4.0 technologies across all three business model elements: value creation, value delivery and value capture. The authors derive those for the economic, environmental and social dimensions of sustainability. Thereupon, we develop several propositions for future research.
Practical implications
The authors provide suggestions to practice how to better achieve value in all three sustainability dimensions through implementing a business model perspective, ecosystem thinking, societal demands and Data Governance and AI integration.
Social implications
By linking societal aspects of Industry 4.0 technologies with environmental, and economic aspects, the authors provide several suggestions how to implement Industry 4.0. For instance, policymakers are recommended to support entire ecosystems than isolated solutions.
Originality/value
The paper contributes to extant literature by conceptualising how Industry 4.0 can leverage value in reaching sustainability in all three dimensions and produce broader ecosystems-wide impacts.
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Utku Civelek, P. Erhan Eren and Mert Onuralp Gökalp
This paper presents the design and implementation of collaborative data science framework (CoDS), a knowledge management system for consolidating data science activities in an…
Abstract
Purpose
This paper presents the design and implementation of collaborative data science framework (CoDS), a knowledge management system for consolidating data science activities in an enterprise.
Design/methodology/approach
The development of the CoDS framework is grounded on the design science research methodology for information systems research. In our case study, we first designed the initial framework for CoDS based on a systematic literature review. Then, we collected the expert opinions of eight data scientists to validate the need for generic content for such a knowledge management system. In the second iteration, a portfolio prototype is developed by the same data scientists as a part of our technical action research. Finally, a survey is conducted with 57 data analyst candidates in the last iteration.
Findings
Using the CoDS portfolio strengthened the communication among data scientists and stakeholders to improve development and scaling activities. It eased the reuse or modification of existing analytical solutions in other company processes.
Practical implications
The CoDS presents a platform on which business details, data-related knowledge, modeling procedures and deployment steps are shared for (1) mediating and scaling ongoing projects, (2) enriching knowledge transfer among stakeholders, (3) facilitating ideation of new products and (4) supporting the onboarding of new employees and developers.
Originality/value
This study proposes a novel structure and a roadmap for creating a data science knowledge management system for the collaboration of all stakeholders in an enterprise.
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Yudi Fernando, Mohammed Hammam Mohammed Al-Madani and Muhammad Shabir Shaharudin
This paper aims to investigate how manufacturing firms behave to mitigate business risk during and post-COVID-19 coronavirus disease (COVID-19) on the global supply chain.
Abstract
Purpose
This paper aims to investigate how manufacturing firms behave to mitigate business risk during and post-COVID-19 coronavirus disease (COVID-19) on the global supply chain.
Design/methodology/approach
A systematic literature review for data mining was used to address the research objective. Multiple scientometric techniques (e.g. bibliometric, machine learning and social network analysis) were used to analyse the Lens.org, Web of Science and Scopus databases’ global supply chain risk mitigation data.
Findings
The findings show that the firms’ manufacturing supply chains used digitalisation technologies such as Blockchain, artificial intelligence (AI), 3D printing and machine learning to mitigate COVID-19. On the other hand, food security, government incentives and policies, health-care systems, energy and the circular economy require more research in the global supply chain.
Practical implications
Global supply chain managers were advised to use digitalisation technology to mitigate current and upcoming disruptions. The manufacturing supply chain has high uncertainty and unpredictable global pandemics. Manufacturing firms should consider adopting Blockchain technology, AI and machine learning to mitigate the epidemic risk and disruption.
Originality/value
This study found the publication trend of how manufacturing firms behave to mitigate the global supply chain disruptions during the global pandemic and business uncertainty. The findings have contributed to the supply chain risk mitigation literature and the solution framework.
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Abyiot Teklu Meshesha, Belay Simane Birhanu and Mintewab Bezabih Ayele
This study aims to examine smallholder farmers’ perceptions toward the adoption of climate-smart agriculture (CSA) in smallholder farmers in the Upper Blue Nile Highlands of…
Abstract
Purpose
This study aims to examine smallholder farmers’ perceptions toward the adoption of climate-smart agriculture (CSA) in smallholder farmers in the Upper Blue Nile Highlands of Ethiopia. Available research focused on profitability and economic constraints alone, disregarding the farmers’ perception of the adoption of CSA innovations. There is relatively little empirical work on farmers’ perceptions of innovations. Hence, a critical research gap that will strengthen CSA innovation research and practice includes understanding farmers’ perceptions about CSA innovations and how these perceptions interact with their adoption.
Design/methodology/approach
A cross-sectional household survey was conducted among 424 smallholder farmers selected from five agro-ecosystems. A structured questionnaire was used to collect primary data and a review of literature and documents was used to collect secondary data. The study used a multivariate probit model to examine perception factors affecting the likelihood of adopting multiple CSA innovations. The dependent variables were eight CSA innovations, while the independent variables were crafted from the three pillars of CSA.
Findings
Major CSA innovations adopted by farmers include improved variety, crop residue management, crop rotation, compost, row planting, soil and water conservation, intercropping and agroforestry. Farmers’ perception toward CSA innovations includes: CSA innovations sustainably increase productivity and income; enhance soil fertility; diversify livestock feed and energy sources; reduce soil erosion, weed infestation and crop failure; enhance soil organic matter, reduce chemical fertilizer use and rehabilitate land. Farmers’ positive perceptions of the benefits of CSA innovations for increasing crop productivity, reducing agricultural vulnerability to climate change and lowering farm greenhouse gas emissions have boosted adoption.
Practical implications
Farmers’ perceptions toward CSA innovations must be enhanced to increase the adoption of CSA innovations in the smallholder agriculture system. The CSA innovation scale-up strategies should focus on farmers’ perception of CSA innovation benefits toward food security, climate change adaption and mitigation outcomes. Awareness of CSA needs the close collaboration of public extension as well as local institutions such as farmers’ training centers.
Originality/value
The study adopts a multivariate probit model that models farmers’ simultaneous CSA innovation choices. Hence, this study contributes to the literature in four significant areas. First, it argues for differential treatment of the perception of smallholder farmers about innovations is needed. Second, it recognizes the interdependence of the adoption of innovations. Third, it directly assesses the farmers’ perception, while others use proxies to measure it. Finally, there are limited or no studies that address the perception of innovations within the lens of adopter perception theory.