scholarly journals The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

2021 ◽  
Vol 10 (10) ◽  
pp. 653
Author(s):  
Zixin Dou ◽  
Yanming Sun ◽  
Zhidong Wu ◽  
Tao Wang ◽  
Shiqi Fan ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Mass Customization ◽  
Management Control ◽  
Future Research ◽  
Learning Technology ◽  
Dynamic Learning ◽  
Data Quality Management ◽  
Research Profile ◽  
Social Internet Of Things ◽  
Few Data

In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems.

Dysfunctional Behavior in Organizations: Insights from the Management Control Literature

2017 ◽  
Vol 37 (4) ◽  
pp. 117-141 ◽  
Author(s):  
Krista Fiolleau ◽  
Theresa Libby ◽  
Linda Thorne
Keyword(s):  
Risk Factors ◽  
Internal Control ◽  
Management Control ◽  
Future Research ◽  
Fraud Triangle ◽  
Audit Risk ◽  
Research Opportunities ◽  
Dysfunctional Behavior ◽  
Research Questions ◽  
Published Research

SUMMARY As the scope of the audit continues to broaden (Cohen, Krishnamoorthy, and Wright 2017), research questions in management control and internal control are beginning to overlap. Even so, there is little overlap between these fields in terms of published research to date. The purpose of this paper is to take a step in bridging the gap between the management control and the internal control literatures. We survey relevant findings from the extant management control literature published between 2003 and 2016 on dysfunctional behavior and the ways in which it might be mitigated. We then use the fraud triangle as an organizing framework to consider how the management control literature might help to address audit risk factors identified in SAS 99/AU SEC 316 (AICPA 2002). The outcome of our analysis is meant to identify and classify the extant management control literature of relevance to research on internal control in a manner that researchers new to the management control literature will find accessible. We conclude with a set of future research opportunities that can help to broaden the scope of current research in internal control.

scholarly journals Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

2021 ◽  
Vol 18 (3) ◽  
pp. 1327
Author(s):  
Leonardo J. Gutierrez ◽  
Kashif Rabbani ◽  
Oluwashina Joseph Ajayi ◽  
Samson Kahsay Gebresilassie ◽  
Joseph Rafferty ◽  
...  
Keyword(s):  
Mental Health ◽  
Internet Of Things ◽  
Data Acquisition ◽  
Identity Management ◽  
Service Level Agreement ◽  
Service Level ◽  
Self Organization ◽  
Future Research ◽  
Study Results ◽  
Open Issues

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.

Trust Management in Social Internet of Things: Architectures, Recent Advancements and Future Challenges

2020 ◽  
pp. 1-1
Author(s):  
Wazir Zada Khan ◽  
Qurat-ul-Ain Arshad ◽  
Saqib Hakak ◽  
Muhammad Khurram Khan ◽  
Saeed-Ur-Rehman
Keyword(s):  
Internet Of Things ◽  
Trust Management ◽  
Social Internet Of Things ◽  
Future Challenges

The 4Cs of mass customization in service industries: a customer lens

2020 ◽  
Vol 34 (4) ◽  
pp. 499-511 ◽  
Author(s):  
Jessica L. Pallant ◽  
Sean Sands ◽  
Ingo Oswald Karpen
Keyword(s):  
Boundary Conditions ◽  
Mass Customization ◽  
Design Methodology ◽  
Service Industries ◽  
Future Research ◽  
Practical Application ◽  
Theoretical Understanding ◽  
Content Type ◽  
Priority Areas ◽  
Research Priority

Purpose Increasingly, customers are demanding products that fit their individual needs. Many firms respond by cultivating product individualization via mass customization, often integrating this capability via interactive platforms that connect them with customers. Despite such customization, research to date has lacked cohesion, often taking the organizational, rather than customer, view. The purpose of this paper is to provide inconclusive theorizing in regard to customization from the consumers’ perspective. Design/methodology/approach The review and synthesis of the literature revealed that co-configuration is an underexplored domain of mass customization. Consequently, an initial conceptualization of co-configuration is developed and compared with current customization strategies. Specifically, the definition and boundary conditions of co-configuration are compared with three domains of mass customization, namely, co-production, co-construction and co-design. This led to the development of research priority areas to establish an agenda for future research on mass customization and its role in customer’ firm relationships. Findings This paper provides the delineation of four distinct consumer customization strategies, conceptualized in a matrix, and proposes separate customer journey visualizations. In advancing the theoretical understanding by means of a unifying typology, this paper identifies three existing Cs of mass customization (co-production, co-construction and co-design) and focuses specifically on a fourth (co-configuration), identified as an understudied mass customization strategy. Originality/value This paper extends the previous conceptualizations of mass customization comprising co-production, co-design and co-construction. The proposed typology establishes a foundation for four research priority areas that can improve both academic rigor and practical application.

Applying the Internet of Things into Ecological Environmental Monitoring

2012 ◽  
Vol 198-199 ◽  
pp. 1755-1760 ◽  
Author(s):  
Guo Ping Zhou ◽  
Ya Nan Chen
Keyword(s):  
Internet Of Things ◽  
Environmental Monitoring ◽  
Software Design ◽  
Ecological Environment ◽  
Future Research ◽  
The Internet ◽  
Environment Monitoring ◽  
Carbon Dioxide Gas ◽  
Data Collection And Analysis ◽  
The Internet Of Things

Applying the Internet of Things (IOT) into ecological environmental monitoring is the goal of this paper. There are several advantages of the Internet of Things (IOT) applying in ecological environment monitoring. A hierarchical monitoring system is presented, including system architecture, hardware/software design, information flow and software implementation. In the end, using carbon dioxide gas in the atmosphere for experimental purposes, in data collection and analysis. Experiments showed that this system is capable of monitoring ecologica environment, which orientate the future research of forest ecosystem.

scholarly journals Graph-based service recommendation in Social Internet of Things

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110090
Author(s):  
Yuanyi Chen ◽  
Yanyun Tao ◽  
Zengwei Zheng ◽  
Dan Chen
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Latent Variable ◽  
Smart Cities ◽  
Knowledge Graph ◽  
Service Recommendation ◽  
Variable Model ◽  
Completion Problem ◽  
Social Internet Of Things ◽  
Object Usage

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

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