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نتیجه جستجو - analytics

تعداد مقالات یافته شده: 516
ردیف عنوان نوع
81 Effectual and causal reasoning in the adoption of marketing automation
استدلال اثربخش و علیتی در اتخاذ اتوماسیون بازاریابی-2020
Research on technology adoption in organizations traditionally assumes that these organizations follow rational, strategic and planned adoption processes. However, a gradually emerging view is that the adoption of technology is also characterized by entrepreneurial or effectual reasoning, primarily due to technological and market uncertainties that call for more agile and experimental approaches at the digital age. Drawing on effectuation theory, we develop a research framework to examine the managerial reasoning during the adoption of marketing automation technology. Based on the results of a comparative multiple-case study on four large-sized industrial firms, we develop a maturity model of marketing automation adoption and show that even large-sized B2B companies apply effectual reasoning, which problematizes the rationality assumption in the technology adoption literature. Second, we show that during the adoption process, organizations dominant reasoning mode follows an iterative pattern in which the adopting organization moves back and forth between effectuation and causation. Finally, we identify five key domains of marketing automation (customer knowledge, information systems infrastructure, analytics, interdepartmental dynamics and change management) and describe their gradual evolution at different stages of the adoption process.
Keywords: Agile implementation | Business-to-business (B2B) marketing | Case study | Effectuation | Marketing automation | Technology adoption
مقاله انگلیسی
82 Is the private sector more efficient? Big data analytics of construction waste management sectoral efficiency
آیا بخش خصوصی کارآمدتر است؟ تجزیه و تحلیل داده های بزرگ از کارآیی بخش مدیریت زباله های ساختمانی-2020
Efficiency disparity between the public and private sectors is a non-trivial issue that concerns fundamental choices of socio-political-economic systems. Waste management academia and industry also wrestle with issues relating to the choice between public and private sectors. To examine the disparity exclusively caused by “sector”, in statistics language, one needs data that is sufficiently big to control many other confounders, e.g., sites, project types, and construction technologies. This paper attempts to ascertain the construction waste management (CWM) efficiency disparity between the public and private sectors by using big data in Hong Kong. The waste disposal records of 132 projects, including 70 public and 62 private projects, were extracted and analysed. By comparing the waste generation flows (WGFs) and accumulative WGFs, it is found that, by and large, there is no significant efficiency disparity in CWM between the two sectors. However, a closer investigation discovered that the private sector outperforms their public counterpart in demolition projects, while the latter performs better in foundation and new building projects. Although there are private projects with higher CWM performance, their divergence between the best and average projects are larger than public ones. Such findings thus reject casual remarks that the private sector is more efficient in CWM. The underlying reasons maybe the waste management index practice promoted in public projects while the private sector is often incentivized to perform better CWM to save waste disposal levies. Future research is recommended to delve into the causes of the efficiency disparity and introduce CWM interventions accordingly.
Keywords: Public-private disparity | Economic efficiency | Construction waste management | Big data | Hong Kong
مقاله انگلیسی
83 A new model to compare intelligent asset management platforms (IAMP)
مدل جدیدی برای مقایسه سیستم عامل های مدیریت دارایی هوشمند (IAMP)-2020
Nowadays, no business activity escapes the fourth industrial revolution, called industry 4.0, which is characterized by digitalization of processes. The possibility of simultaneously having systems with greater interconnection, more information and greater flexibility, allows companies to have a clearer view of their processes and consequently improve their effectiveness and efficiency. The digital transformation can no longer be based simply on making the processes more efficient, but on creating more sustainable and profitable customer relationships, continuously aligning the value of the product with the changing customer requirements. Even though managing assets over the Internet is increasingly common, much effort is needed to identify the functionality that should be provided by these platforms to enhance existing asset management practices. The effort of IT vendors is focused on the development of IoT platforms, which allow, among other functions, to create a connection between machinery and digital systems, protect all devices and data against hacking or attacks, control operations and maintenance of equipment or perform different analyses of assets or systems. The aim of this paper is to understand the functionalities of the existing IAMP platforms, providing a system that evaluates these functionalities based on the business objectives from the point of view of asset management. This methodology allows maintenance managers guiding the evolution of the life cycle of their assets according to the business value conception. This makes this methodology especially suitable for supporting new challenging scenarios of maintenance management. In this paper we first talk about the structure of an IAMP, then how they integrate the asset management model and a summary of the features and modules that have the most known IAMP platforms. Finally, an evaluation system of IAMP platforms and a case study is presented based on their content for asset management. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0)
Keywords: Asset Management | Industrial IoT | Digitalization | Predictive Analytics | Intelligent assets management systems
مقاله انگلیسی
84 Analytics in the era of big data: The digital transformations and value creation in industrial marketing
تجزیه و تحلیل در عصر داده های بزرگ: تحولات دیجیتال و ایجاد ارزش در بازاریابی صنعتی-2020
Big data analytics has been a topical area in the past decade. Despite it is emphased as a promising tool for the B2B sectors, there is a short of academic studies about this phenomenon in the industrial markets. Existing big data analytics focuses more on the consumers marketing aspect, while in fact both the consumers data and the machine-generated transaction data can be gathered and analysed at the interorganisational level. Subsequently, there is a need to increase the attention on the B2B aspects of big data analytics and the interactions of stakeholders. This paper, therefore, investigates the digital transformation enabled by big data analytics in the industrial markets and provides a conceptual framework. It solicits research articles that provide insights into various industrial contexts of this topic and applied both qualitative and quantitative approaches to identify the big data gathering and applications for value creation.
Keywords: Big data | B2B analytics | Digital transformations | Management revolution | Value creation
مقاله انگلیسی
85 Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management
ادغام تجزیه و تحلیل داده های بزرگ جاسازی شده معماری شهر هوشمند با وب سایت RESTful برای ارائه خدمات کارآمد و مدیریت انرژی-2020
Emergence of smart things has revolutionized the conventional internet into a connected network of things, maturing the concept of Internet of Things (IoT). With the evolution of IoT, many attempts were made to realize the notion of smart cities. However, demands for processing enormous amount of data and platform incompatibilities of connected smart things hindered the actual implementation of smart cities. Keeping it in view, we proposed a Big Data analytics embedded smart city architecture, which is further integrated with the web via a smart gateway. Integration with the web provides a universal communication platform to overcome the platform incompatibilities of smart things. We introduced Big Data analytics to enhance data processing speed. Further, we evaluated authentic datasets to determine the threshold values for intelligent decision-making and to present the performance improvement gained in data processing. Finally, we presented a representational state transfer (RESTful) web of things (WoT) integrated smart building architecture (smart home) to reveal the performance improvements of the proposed smart city architecture in terms of network performance and energy management of smart buildings.
Keywords: Smart city | Big Data analytics | Smart home | Web of things | RESTful architecture
مقاله انگلیسی
86 Cognitive computing, Big Data Analytics and data driven industrial marketing
محاسبات شناختی ، تحلیل داده های بزرگ و بازاریابی صنعتی مبتنی بر داده ها-2020
The integration of cognitive computing and big data analytics leads to a new paradigm that enables the application of the most sophisticated advances in information and communication technology (ICT) in business, including industry, business to business, and related decision-making process. The same paradigm will lead to several breakthroughs in the subfield of industrial marketing: a field both promising and extremely challenging. This special issue makes a case that cognitive computing and big data are a source of a new competitive advantage that, if properly embraced, will further consolidate industrial marketing management position in the of core the decision-making process of businesses operating locally and globally. In this vein, the value added of this special issue is twofold. On the one hand, this special issue communicates high quality research on big data analytics and data science as it is applied in industrial marketing management; On the other hand, it proposes a multidisciplinary approach to the study of the design, implementation and provision of sophisticated applications and systems necessary for data-driven industrial marketing decisions.
مقاله انگلیسی
87 An empirical study on business analytics affordances enhancing the management of cloud computing data security
یک مطالعه تجربی در مورد تحلیلی تجارتی تجاری که باعث افزایش مدیریت امنیت داده های رایانش ابری می شود-2020
The mechanism of business analytics affordances enhancing the management of cloud computing data security is a key antecedent in improving cloud computing security. Based on information value chain theory and IT af- fordances theory, a research model is built to investigate the underlying mechanism of business analytics af- fordances enhancing the management of cloud computing data security. The model includes business analytics affordances, decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and the management of cloud computing data security. Simultaneously, the model considers the role of data-driven culture and IT business process integration. It is empirically tested using data collected from 316 enterprises by Partial Least Squares-based structural equation model. Without data-driven culture and IT business process integration, the results suggest that there is a process from business analytics affordances to decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and to the management of cloud computing data security. Moreover, Data-driven culture and IT business process integration have a positive mediation effect on the relationship between business analytics affordances and decision-making affordances of cloud computing data security. The conclusions in this study provide useful references for the enterprise to strengthen the management of cloud computing data se- curity using business analytics.
Keywords: Management of cloud computing data security | Business analytics affordances | Data security decision-making rationality | Data-driven culture | IT business process integration
مقاله انگلیسی
88 Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications
دستیابی به عملکرد پایدار در زنجیره تأمین کشاورزی داده محور: مروری بر تحقیقات و کاربردها-2020
The lack of industrialization, inadequacy of the management, information inaccuracy, and inefficient supply chains are the significant issues in an agri-food supply chain. The proposed solutions to overcome these challenges should not only consider the way the food is produced but also take care of societal, environmental and economic concerns. There has been increasing use of emerging technologies in the agriculture supply chains. The internet of things, the blockchain, and big data technologies are potential enablers of sustainable agriculture supply chains. These technologies are driving the agricultural supply chain towards a digital supply chain environment that is data-driven. Realizing the significance of a data-driven sustainable agriculture supply chain we extracted and reviewed 84 academic journals from 2000 to 2017. The primary purpose of the review was to understand the level of analytics used (descriptive, predictive and prescriptive), sustainable agriculture supply chain objectives attained (social, environmental and economic), the supply chain processes from where the data is collected, and the supply chain resources deployed for the same. Based on the results of the review, we propose an application framework for the practitioners involved in the agri-food supply chain that identifies the supply chain visibility and supply chain resources as the main driving force for developing data analytics capability and achieving the sustainable performance. The framework will guide the practitioners to plan their investments to build a robust data-driven agri-food supply chain. Finally, we outline the future research directions and limitations of our study.
Keywords: Agriculture supply chain | Food supply chain | Sustainability | Sustainable performance | Supply chain visibility | Big data | Blockchain | Data analytics | Supply chain resources
مقاله انگلیسی
89 IFC-based process mining for design authoring
استخراج فرآیند مبتنی بر IFC برای تألیف طراحی-2020
Building Information Modelling (BIM) is defined as the process of creation and management of digital replica for building products in a collaborative design set-up. On this basis, BIM as a digital collaboration platform in AECO (Architecture, Engineering, Construction, and Operation) industry, can be upgraded to assist monitoring, control and improvement of the business processes related to planning, design, construction and operation of building facilities. The main problem in this regard, is the wastage of data related to activities completed by different actors during the project; and subsequently, the lack of analytics to discover latent patterns in collaboration and execution of such processes. The present study aims to enable BIM to capture digital footprints of project actors and create event logs for design authoring phase of building projects. This is done using files in IFC (Industry Foundation Classes) format, archived during the design process. We have developed algorithms to create event logs from such archives, and analyzed the event logs using process mining (i.e. process discovery, conformance checking and bottleneck analysis), to identify measures derived from as-happened processes. BIM managers can implement such measures in monitoring, controlling and re-engineering work processes related to design authoring. Two case studies were completed to validate and verify the products and findings of the research. Our results show that process models discovered/fine-tuned at various resolutions and from different perspectives (including ‘actor-centric’ and ‘phase-centric’ views) can provide a realistic view of the BIM project execution. This includes understanding the structure of collaboration and hand-over of work; evaluation of compliance with the BIM execution plan; and detection of bottlenecks and re-works. While the scope of the study has been limited to design authoring processes, this mindset can be extended to other BIM uses, and other phases (such as construction and operation) of building projects. Given the growing efforts on upgrading BIM to capture and formalize the lifecycle data on the products, processes and actors, this study can strongly support BIM managers with documentation and evaluation of the business processes and workflows in their project teams.
Keywords: Building Information Modeling | Business processes management | Data mining | Process mining | BIM management | BIM Execution Planning
مقاله انگلیسی
90 Internet of energy-based demand response management scheme for smart homes and PHEVs using SVM
اینترنت برنامه پاسخگویی به تقاضای مبتنی بر انرژی برای خانه های هوشمند و PHEV با استفاده از SVM-2020
The usage of information and communication technology (ICT) in the power sector has led to the emergence of smart grid (SG). The connected loads in SG are able to communicate their consumption data to the grid using ICT and thus forming a large Internet of Energy (IoE) network. However, various issues such as–increasing demand–supply gap, grid instability, and deteriorating quality of service persist in this network which degrade its performance. These issues can be handled in an efficient way by managing the demand response (DR) of different types of loads. For this purpose, cloud computing can be leveraged to gather the data generated in IoE network and perform analytics to manage DR. Working in this direction, a novel scheme to handle the DR of smart homes (SHs) and plug-in hybrid electric vehicles (PHEVs) is presented in this paper. The proposed scheme is based on analyzing the demand of these users at the cloud server for flattening the overall load profile of grid. This scheme is divided into two hierarchical stages which work as follows. In the first stage, the residential and PHEV users are identified whose demands can be regulated. This task is achieved with the help of a binary-class support vector machine (SVM) which uses Gaussian kernel function to classify these users. In the next stage, the load in SHs is curtailed on the basis of a pre-defined rule-base after analyzing the consumption data of various devices; whereas PHEVs are managed by controlling their charging rates. The efficacy of proposed scheme has been tested on PJM benchmark data and Open Energy Information dataset. The simulation results prove that the proposed scheme is effective in maintaining the overall load profile of SG by managing the DR of SHs and PHEV users.
Keywords: Data analytics | Demand response | Plug-in hybrid electric vehicles | Smart grid | Smart homes | Support vector machine
مقاله انگلیسی
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