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ردیف | عنوان | نوع |
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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 |