Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers' activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues.
This article provides a theoretical framework for comprehending the connections between dynamic data analytics capability (DDAC), innovation capabilities (IC), supply chain resilience (RES), and sustainable supply chain performance (SSCP). Since this is the first empirical investigation of the sequential mediation effect between DDAC and SSCP through IC and RES, it fills a critical need in the supply chain literature. A quantitative methodology was used, involving a survey questionnaire distributed to 259 large Pakistani manufacturing firms. We used PLS-SEM to test for the expected associations. Findings show that using DDAC has a beneficial effect on both innovative and resilient capabilities, which in turn leads to better SSCP. The research illuminates the sequential mediating roles of product, process, and resilience, underlining the need of combining data-driven innovation with resilience in order to achieve sustainable supply chain performance. These results provide useful guidance for businesses that want to boost their sustainability results by taking a more all-encompassing approach to data-driven innovation and resilience.
Cloud ERP is a type of enterprise resource planning (ERP) system that runs on the vendor's cloud platform instead of an on-premises network, enabling companies to connect through the Internet. The goal of this study was to rank and prioritise the factors driving cloud ERP adoption by organisations and to identify the critical issues in terms of security, usability, and vendors that impact adoption of cloud ERP systems. The assessment of critical success factors (CSFs) in on-premises ERP adoption and implementation has been well documented; however, no previous research has been carried out on CSFs in cloud ERP adoption. Therefore, the contribution of this research is to provide research and practice with the identification and analysis of 16 CSFs through a systematic literature review, where 73 publications on cloud ERP adoption were assessed from a range of different conferences and journals, using inclusion and exclusion criteria. Drawing from the literature, we found security, usability, and vendors were the top three most widely cited critical issues for the adoption of cloud-based ERP; hence, the second contribution of this study was an integrative model constructed with 12 drivers based on the security, usability, and vendor characteristics that may have greater influence as the top critical issues in the adoption of cloud ERP systems. We also identified critical gaps in current research, such as the inconclusiveness of findings related to security critical issues, usability critical issues, and vendor critical issues, by highlighting the most important drivers influencing those issues in cloud ERP adoption and the lack of discussion on the nature of the criticality of those CSFs. This research will aid in the development of new strategies or the revision of existing strategies and polices aimed at effectively integrating cloud ERP into cloud computing infrastructure. It will also allow cloud ERP suppliers to determine organisations' and business owners' expectations and implement appropriate tactics. A better understanding of the CSFs will narrow the field of failure and assist practitioners and managers in increasing their chances of success.
Consistent with the worldwide call to combat environmental degradation concerns and advance sustainable development, there is increasing pressure on organizations to ensure organizational strategies include green initiatives. In this regard, environmental strategic focus is a relevant concept for scholars and business leaders. Underpinned by dynamic capability and stakeholder theory, the present study hypothesizes that ESF derives environmental performance, coordinated by mediating role of green shared vision that strategic environmental planning and decision making. Additionally, the current study employed ISO 14001 and technological capability as moderators between ESF and the green shared vision link. Methodologically, the data for this study was collected from 162 senior managerial officials working in EMS 14,001-accredited manufacturing firms in Malaysia. The data were analyzed with the AMOS 23 software to perform covariance-based structural equation modeling (CB-SEM), and then hierarchical regression analysis and moderated-mediation analysis were applied with SPSS 25. The findings confirmed that ESF is positively linked to environmental performance. The results validate that green shared vision acts as a positive mediator between ESF and environmental performance, in which the creation and sharing of knowledge embedded in a green shared vision serve as enablers to create higher environmental performance. The current study also validates a significant moderating role of ISO 14001 and technological capability between ESF and green shared vision. The study confirms how environmental strategies are integrated into environmental management processes that can serve as a source of dynamic capabilities.
Due to significant requirement of energy, water, material, and other resources, the manufacturing industries significantly impact environmental, economic, and social dimensions of sustainability (triple bottom-line). In response, today's research is focused on finding solution towards sustainable manufacturing. In this regard, sustainability assessment is an essential strategy. In the past, a variety of tools was developed to evaluate the environmental dimension. Because of this fact, previous review studies were grounded mostly on tools for green manufacturing. Unlike previous review articles, this study was aimed to review and analyze the emerging sustainability assessment methodologies (published from 2010 to 2020) for manufacturing while considering the triple bottom-line concept of sustainability. In this way, the paper presents a decade review on this topic, starting from 2010 as the guidelines for the social dimension became available in 2009. This paper has analyzed various methods and explored recent progress patterns. First, this study critically reviewed the methods and then analyzed their different integrating tools, sustainability dimensions, nature of indicators, difficulty levels, assessment boundaries, etc. The review showed that life cycle assessment and analytic hierarchy process-based approaches were most commonly used as integrating tools. Comparatively, still, environmental dimension was more commonly considered than economic and social dimensions by most of the reviewed methods. From indicators' viewpoint, most of the studied tools were based on limited number of indicators, having no relative weights and validation from the experts. To overcome these challenges, future research directions were outlined to make these methods more inclusive and reliable. Along with putting more focus on economic and social dimensions, there is a need to employ weighted, validated, and applicable indicators in sustainability assessment methods for manufacturing.
Background: Enterprise resource planning (ERP) is critical to enhancing the ability to control commercial activities and results in a competitive advantage when combined with an organisation's existing competitive advantages. However, our practise review reveals that end users resist ERP implementation because the resulting changes will alter the current status quo. The implementation of an ERP system in an organisation is complex as it affects multiple areas of the business. Resistance to change is cited as a factor of ERP failure. Methods: In this study, we conducted a systematic literature review using Transfield's five stages and established a conceptual framework for ERP system implementation in science and technology parks (STPs). Articles collected from Emerald, Science Direct, ProQuest and Scopus databases between 1 st June 2021 and 15 th June 2021. Two authors were assigned to check the suitability of the articles in order to avoid risk of bias. Articles were analysed based on components of a research paper and the data was tabulated using MS Excel. Results: Only eight papers (0.011% of all the papers) appeared when we searched for papers related to ERP with a focus on post ERP Implementation, end-user behaviours, organisational performance, and the accelerated SAP (system application and product) methodology. We found that there are hardly any articles on ERP post implementations in STP context particularly based on the evaluation part of accelerated SAP. Conclusions: Results indicate the lack of studies in this field, particularly those addressing issues related to STP. This study attempted to broaden the understanding of the ERP's effectiveness, particularly in terms of an organisation's operational performance.
Systematically analysing the relative importance and hierarchical relationships among the influencing factors of the cross-border e-commerce ecosystem holds rich theoretical value and practical significance for the development of this ecosystem. A total of 19 influencing factors covering four aspects affecting the cross-border e-commerce ecosystem are identified by means of the relevant literature, web pages, research, and discussions with relevant experts and scholars, and the decision-making trial and evaluation laboratory (DEMATEL) and interpretative structural modeling (ISM) method is used to analyse the cause-effect correlation of each factor and to obtain a cause-effect diagram and a multi-level recursive structure model. The results show that three factors, i.e., the e-commerce platform development level, cross-border e-commerce competitiveness, and the cross-border e-commerce transaction scale, have a greater degree of influence on the other influencing factors. Additionally, three factors, i.e., the information development level, GDP, and cross-border online shopping demand, are vulnerable to the influence of the other factors. The level of cross-border e-commerce platform development, cross-border e-commerce competitiveness, and inter-firm competition are the root factors and occupy an important position in the cross-border e-commerce ecosystem as influencing factors and influence the stability of the cross-border e-commerce ecosystem by affecting the other factors.
The evolution of Internet technology is closely mirrored by the innovative business and profit models emerging within the platform economy. Integrating this economic framework, online video platforms are dynamically refining their pricing strategies to optimize profit margins. This paper, anchored in the theoretical construct of second-degree price discrimination, selects the Chinese online video platforms iQiyi and Tencent Video for in-depth case studies. It charts the progression of its pricing strategies and juxtaposes these with those of prominent international online video sites to highlight both congruities and divergences. The synthesis of theoretical models with real-world case studies culminates in strategic recommendations to foster the growth of Chinese online video platforms in the global Internet arena.
Objective: Medical device development, from the product's conception to release to market, is very complex and relies significantly on the application of exact processes. This paper aims to provide an analysis and summary of current research in the field of medical device development methodologies, discuss its phases, and evaluate the associated legislative and risk aspects. Methods: The literature search was conducted to detect peer-reviewed studies in Scopus, Web of Science, and Science Direct, on content published between 2007 and November 2019. Based on exclusion and inclusion criteria, 13 papers were included in the first session and 11 were included in the second session. Thus, a total of 24 papers were analyzed. Most of the publications originated in the United States (7 out of 24). Results: The medical device development process comprises one to seven stages. Six studies also contain a model of the medical device development process for all stages or for just some of the stages. These studies specifically describe the concept stage during which all uncertainties, such as the clinical need definition, customer requirements/needs, finances, reimbursement strategy, team selection, and legal aspects, must be considered. Conclusion: The crucial factor in healthcare safety is the stability of factors over a long production time. Good manufacturing practices cannot be tested on individual batches of products; they must be inherently built into the manufacturing process. The key issues that must be addressed in the future are the consistency in the classification of devices throughout the EU and globally, and the transparency of approval processes.
In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.
The current global trend in sustainable business practices is to optimize green innovation performance. To protect the environment and maintain their own survival, organizations must strengthen their green innovation capabilities. Drawing on the recourse-based view and ecology modernization theory (EMT), this study examines the direct effect of green strategic orientations, green entrepreneurial orientation, green market orientation, green innovation orientation, and green organizational culture on the firm's green innovation capability, as well as the mediating effect of green innovation capability on the relationship of these four factors and green innovation performance. Besides, this study also explored the moderating effects of green management system implementation and firm size on the association between green innovation capability and green innovation performance. To test the hypothesized model, a questionnaire survey was administered to gather responses from 293 medium-sized and large manufacturing firms operating in Pakistan. The partial least squares method was used for data analysis. The results revealed that green entrepreneurial orientation, green market orientation, green innovation orientation, and green organizational culture positively impacted green innovation capability, which subsequently positively influenced green innovation performance. Moreover, effective implementation of green management systems can bolster the effect of green innovation capability on green innovation performance, and the mediating effect of green innovation capability has also been confirmed. These indicated that the management of medium and large manufacturing firms operating in Pakistan should focus on encouraging green innovation and training employees regarding the latest eco-friendly technologies to attain performance and sustainable development goals. Policymakers should implement green business development programs and offer rewards or penalties for promoting compliance. The present study contributes greatly to the literature by applying EMT as an alternative to address the mediating role of green innovation capability and the moderating effect of green management system implementation in enhancing firms' green innovation performance.
At present, the development speed of international trade cannot catch up with the economic development speed, and the insufficient development speed of international trade will directly affect the rapid development of national economy. In order to solve the problem of international trade, the overall optimal scheduling of trade vehicles and the optimal planning of trade transportation path are very important to improve enterprise services, reduce enterprise costs, increase enterprise benefits, and enhance enterprise competitiveness. The second development of the program is based on the programming interface provided by Baidu map. This paper proposes a neural network algorithm for genetic optimization of multiple mutations, which overcomes the shortcoming of traditional genetic algorithm population "ten" character distribution by mixing multiple coding methods, and enhances the local search ability of genetic algorithm by introducing a new large-mutation small-range search population. The example application shows that the optimization method can realize the optimization of international trade path under real road conditions and greatly improve the work efficiency of actual trade.
The stress of environmental regulations, sustainable development objectives, and global warming is becoming more prominent now. Most studies conclude that the industrial sector is largely at fault and under tremendous pressure to address these climate change issues. This study highlights the significance of green innovation to Chinese firms in mitigating these conservational challenges, and the study probes the association between green innovation and absorptive capacity. Additionally, board capital (the social and human capital of directors) and environmental regulation-both drivers of green innovation-are explored as moderators between green innovation and absorptive capacity. With appropriate econometric methods and theoretical support from the natural resource-based review, the resource dependency theory, and the Porter hypothesis, the results indicate the positive relationship between green innovation and absorptive capacity. They also reveal board capital and environmental regulation as positive moderators, emphasizing their significance to green innovation. This study offers several suggestions and directives for stakeholders, such as businesses, policymakers, and governments, to foster green innovation for greater profitability, minimizing negative industrial consequences.
In the context of Industry 4.0, manufacturing metrology is crucial for inspecting and measuring machines. The Internet of Things (IoT) technology enables seamless communication between advanced industrial devices through local and cloud computing servers. This study investigates the use of the MQTT protocol to enhance the performance of circularity measurement data transmission between cloud servers and round-hole data sources through Open CV. Accurate inspection of circular characteristics, particularly roundness errors, is vital for lubricant distribution, assemblies, and rotational force innovation. Circularity measurement techniques employ algorithms like the minimal zone circle tolerance algorithm. Vision inspection systems, utilizing image processing techniques, can promptly and accurately detect quality concerns by analyzing the model's surface through circular dimension analysis. This involves sending the model's image to a computer, which employs techniques such as Hough Transform, Edge Detection, and Contour Analysis to identify circular features and extract relevant parameters. This method is utilized in the camera industry and component assembly. To assess the performance, a comparative experiment was conducted between the non-contact-based 3SMVI system and the contact-based CMM system widely used in various industries for roundness evaluation. The CMM technique is known for its high precision but is time-consuming. Experimental results indicated a variation of 5 to 9.6 micrometers between the two methods. It is suggested that using a high-resolution camera and appropriate lighting conditions can further enhance result precision.
The current production and conception have impacted the environmental hazards. Green innovation (GI) is the ideal solution for sustainable production, consumption, and ecological conservation. The objective of the study is to compare comprehensive green innovation (green product, process, service, and organization) impact on firm financial performance in Malaysia and Indonesia, along with the first study to measure the moderation role of the corporate governance index. This study has addressed the gap by developing the green innovation and corporate governance index. Collected panel data from the top 188 publicly listed firms for 3 years and analyzed it using the general least square method. The empirical evidence demonstrates that the green innovation practice is better in Malaysia, and the outcome also shows that the significance level is higher in Indonesia. This study also provides empirical evidence that board composition has a positive moderation relationship betwixt GI and business performance in Malaysia but is insignificant in Indonesia. This comparative study provides new insights to the policymakers and practitioners of both countries to monitor and manage green innovation practices.
In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model's effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences.
Previous researches show that buy (growth) companies conduct income increasing earnings management in order to meet forecasts and generate positive forecast Errors (FEs). This behavior however, is not inherent in sell (non-growth) companies. Using the aforementioned background, this research hypothesizes that since sell companies are pressured to avoid income increasing earnings management, they are capable, and in fact more inclined, to pursue income decreasing Forecast Management (FM) with the purpose of generating positive FEs. Using a sample of 6553 firm-years of companies that are listed in the NYSE between the years 2005-2010, the study determines that sell companies conduct income decreasing FM to generate positive FEs. However, the frequency of positive FEs of sell companies does not exceed that of buy companies. Using the efficiency perspective, the study suggests that even though buy and sell companies have immense motivation in avoiding negative FEs, they exploit different but efficient strategies, respectively, in order to meet forecasts. Furthermore, the findings illuminated the complexities behind informative and opportunistic forecasts that falls under the efficiency versus opportunistic theories in literature.
Mass valuation of properties is important for purposes like property tax, price indices construction, and understanding market dynamics. There are several ways that the mass valuation can be carried out. This paper reviews the conventional MRA and several other advanced methods such as SAR, Kriging, GWR, and MWR. SAR and Kriging are good for modeling spatial dependence while GWR and MWR are good for modeling spatial heterogeneity. The difference between SAR and Kriging is the calculation of weights. Kriging weights are based on the spatial dependence or so called the semi-variogram analysis of the price data whereas the weights in SAR are based on the spatial contiguity between the sample data. MWR and GWR are special types of regression where study region is subdivided into local sections to increase the accuracy of prediction through neutralizing the heterogeneity of autocorrelations. MWR assigns equal weights for observations within a window while GWR uses distance decay functions. The merits and drawbacks of each method are discussed.
We had reviewed the current practice in stocks market analysis where stock is represented by its closing price, and then found that this approach may be misleading. In actuality, in the daily activity of stocks market, stock is represented by four prices, namely opening, highest, lowest, and closing prices. Thus, stock is a multivariate time series of those four prices and not a univariate time series of closing price only. In this paper all four prices will be considered. Then, the notion of multivariate time series similarity among stocks will be developed as a generalisation of univariate time series similarity. The results are used to construct stocks network in multivariate setting. To filter the economic information contained in that network, the standard tools in econophysics is used. Furthermore, the topological properties of stocks are analysed by using the most common centrality measures. As an example, Bursa Malaysia data are investigated and we show that the proposed approach can better figure out the real situation compared to the current one.