Internet of Things based Customer Relationship Management – A Research Perspective

Due to the rapid growth in the electronic business, information technology has become very necessary for building up efficiency and effective transactions among organizations. Consequently, Cloud Computing (CC) becomes a very important area to achieve the organization's performance. The process of integration between Cloud Computing (CC) and the Customer Relationship Management (CRM) is a new research area. Therefore, scientific research and literature review related to Cloud Computing and CRM processes are limited. Yet, the impact of Cloud Computing on CRM processes remains under exploration and investigation. To this effect, this paper takes on a holistic approach that examines the combined effects of Cloud Computing Benefits (cost and ease of use) and CRM processes (customer acquisition, customer retention and customer expansion) on organization's performance. It also presents a conceptual framework for Cloud Computing and CRM processes, based on the analysis of various models presented in Cloud Computing and CRM processes. The main emphasis is focused on the concept of Cloud Computing Benefits (cost and ease of use) and CRM processes (customer acquisition, customer retention and customer expansion) by examining some reasons that make the organization interested in using this modern technology from one hand and how Cloud Computing can encourage organizations to implement CRM to improve their performance on the other. Therefore, this paper contributes to develop three models: Cloud Computing Benefits, CRM process, and integration between the Cloud Computing and CRM process to improve organization performance model. Also, it describes more valid factors to identify how an organization deals with its performance in the future by providing an advice for the Information Technology (IT) industry as how an analytical integration between Cloud Computing Benefits and CRM process improves organization's performance – something that should be taken into account in developing countries to attain a competitive advantage. This study is divided in three sections: Literature Review, Research Model, and Conclusion.

Due to the rapid growth in the electronic business, information technology has become necessary for building efficiency and effective transactions among organizations. Consequently, Cloud Computing (CC) becomes a very important area to achieve the organization's performance. The process of integration between Cloud Computing (CC) and the Customer Relationship Management (CRM) is a new research area. Therefore, scientific research and literature review related to Cloud Computing and CRM processes are limited. This paper takes a holistic approach that examines the combined effects of Cloud Computing Benefits (cost and ease of use) and CRM processes (customer acquisition, customer retention and customer expansion) on organization's performance. It also presents a conceptual framework for Cloud Computing and CRM processes, based on the analysis of various models presented in Cloud Computing and CRM processes. The main emphasis is focused on the concept of Cloud Computing Benefits and CRM processes by examining some reasons that make the organization interested in using this modern technology from one hand and how Cloud Computing can encourage organizations to implement CRM to improve their performance on the other. This paper contributes to develop three models: Cloud Computing Benefits, CRM process, and integration between the Cloud Computing and CRM process to improve organization performance model. Also, it describes more valid factors to identify how an organization deals with its performance in the future by providing an advice for the Information Technology (IT) industry as how an analytical integration between Cloud Computing Benefits and CRM process improves organization's performance – something that should be taken into account in developing countries to attain a competitive advantage. This study is divided in three sections: Literature Review, Research Model, and Conclusion.

Due to the strong competition that exists among organisations and the rapid change in the business environment, knowledge has turned out to become a key source for organisations to enhance the competitive advantage. Integrating Knowledge Management (KM) and Customer Relationship Management (CRM) process is a new research area, therefore, scientific research and literature around it remain limited. In addition, the impact of KM process on customer acquisition, retention, and expansion to improve customer satisfaction remains under study and report. The aim of this paper is to present a conceptual framework of KM integrated with CRM called Customer Knowledge Relationship Management (CKRM) Process depending on analysis of various models presented in KM and CRM. The main highlighting is laid upon the concepts of the concept of customer knowledge (knowledge about customer, knowledge for customer, knowledge from customer). Therefore, this paper contributes to the development of KM process (Knowledge Process about Customer, Knowledge Process for Customer, and Knowledge Process from Customer). The paper investigated how the companies in Jordan developed KM process to improvement the CRM process. Based on data collected from the company, results from analysis indicated that the KM process had a positive effect on CRM process.

Purpose– The purpose of this paper is to study the application of knowledge management (KM) factors on customer relationship management (CRM) process in Malaysian Multimedia Super Corridor (MSC) status organisations.Design/methodology/approach– A questionnaire survey was conducted on KM workers (customer service) in Malaysian MSC status organisations. Based on the extensive review of the current literature, eight KM factors were investigated to examine the application toward CRM process.Findings– The data, collected from 96 knowledge workers, suggest that KM process, organisational infrastructure and technology are three important predictors for effective CRM process.Practical implications– The practical implication of this paper could be useful for business managers who want to enhance organisational CRM through implementing KM practices to support their organisation’s KM efforts.Originality/value– This is one of the few papers to study the application of KM factors on CRM process in Malaysia.

Organizations have found that functional approach to business destroys flexibility and agility by gaining experience over the time. The main weakness with Task-based organizations is that they can hardly act flexibly and adapt themselves to the changing environment. In today’s' dynamic and competitive business world, organizations focus significantly on managing and improving their business processes. Therefore, nowadays to be survived and successful, certainly, the process approach must be followed. In addition, organizations having such improvement and innovations are looking to create organizational wealth. In this regard, the present study is trying to identify and describe processes from reputable reference models in Customer Relationship Management (CRM) and more importantly to design and streamline CRM processes effectively for organizations in order to improve processes and offer value to customers. In this paper, first, by identifying and explaining CRM reference models, three models were selected based on five criteria (flexibility, Understandability, comprehensiveness, Completeness, and Usability) using Analytic Hierarchy Process (AHP) technique which is relative as follow: SAP, CCOR & PCF. Then, the processes of the three models were extracted and gathered in a comprehensive list to offer a united framework to design CRM processes. Finally, a case study will show the applicability of the proposed model. With this paper, we enrich research by a valuable process framework for developing well-designed reference models.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

INTRODUCTION Value creation strategies in the 21st century have shifted from product differentiation and operational efficiency to knowledge creation and relationship building across the value network. Knowledge management (KM) and relationship management (RM) are two sides of the same coin in gaining competitive advantages in the knowledge-based economy. The focus of relationship management has been on customer relationship management (CRM), which has helped companies to acquire and retain customers and increase market share and profitability. For businesses that have a high percentage of sales through indirect channels, such as the hightech industry (Tanoury & O'Leary, 2000), partner relationship management (PRM) has become an important business strategy in the sell-side relationship management. As procurement cost and suppliers' commitment to meet production requirements can drastically impact a firm's performance (Aberdeen, 2001; Lang, Paravicini, Pigneur, & Revaz, 2002; Carey, 2005), the upstream supplier relationship management (SRM) is equally important as the downstream CRM and PRM. Enterprise relationship management (ERM) is a business strategy that optimizes the relationships between a firm and its customers, channel partners, suppliers and alliance partners in order to maximize opportunities. Knowledge management is a value creation strategy that exploits the knowledge assets of an enterprise to achieve business goals. It is concerned with the creation, sharing and utilization of knowledge in an organization. The internal focus of knowledge management to improve product innovation and operational efficiency has shifted to an external focus of enhancing business processes across the extended enterprise. In particular, the application of KM to CRM business processes and the management of customer knowledge have received increasing attention both in academia and in business. The merging of KM and ERM yields two perspectives: the ERM-oriented KM (EKM) and the KM-oriented ERM (KERM). The focus of prior research in this area has been on the integration of KM and CRM. While different models have been proposed for customer knowledge management (CKM) and knowledge-enabled CRM (KCRM), there are no standardized definitions of these terms. A commonly accepted concept is that CKM deals with the processes of managing customer knowledge, whereas KCRM deals with the utilization of knowledge to improve CRM processes. Gibbert, Leibold and Probst (2002) described CKM as the management of knowledge from customers and emphasized CKM as the strategic process by which customers are empowered as knowledge partners. It proposed five styles of CKM that can be used by companies depending on the nature of their customers. Paquette (2006) described CKM as the processes that a firm employs to manage the identification, acquisition and internal utilization of customer knowledge. Su, Chen and She (2006) proposed a conceptual framework for an E-CKM model by incorporating IT into the CKM model. Eppler, Siefried and Popnack (1999) classified business processes by knowledge intensity and process complexity. High CRM process complexity implies a high degree of knowledge to pursue the process goals. KCRM is the enablement of CRM processes by knowledge across the operations of marketing, sales and service. Gebert, Geib, Rolbe, and Piernpp (2002) and Gebert, Geil, Kobe, and Brenner (2003) described a CKM model based on a research approach in business engineering developed at the University of St. Gallen. The term CKM used by the St. Gallen authors refers to both the CKM and the KCRM aspects in the context of this paper. The St. Gallen approach derives from the reflections about CRM (Derliyski & Frohlich, 2004; Zanjani, Rouzbehani, & Dabbagh, 2008), in which customer knowledge is applied to support CRM processes (Gebert et al., 2002; Bueren, Schierholtz, Kolbe, & Brenner, 2004). The St. Gallen concept of the CKM model describes the basic elements for successful knowledge management in customer-oriented processes (Gebert et al. …

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

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The internet of things (IoT) transforms the world in many ways. It combines many types of hardware and software with a variety of communication technologies to enable the development of innovative applications. A typical IoT system consists of IoT device, IoT gateway, IoT platform, and IoT application. Developing these elements and delivering an IoT system for fulfilling business requirements encompasses many activities to be executed and is not straightforward. To expedite these activities, some major vendors provide software development kits (SDK), integrated development environments (IDE), and utility tools for developing software to be executed on IoT devices/gateways. Moreover, these vendors utilize their cloud platforms to provide fundamental services, such as data storage, analytics, stream processing, for developing IoT systems. These vendors also developed IoT specific cloud-based services, such as connectivity and device management, to support IoT system development. This chapter presents an overview of tools and platforms provided by five major vendors.

This paper investigated the effect of customer satisfaction measures on customer retention, and the National Social Security Fund (NSSF) Ilala office in Tanzania was used as a case study. In particular, it examined how relationship marketing, service quality, and customer relationship management (CRM) affect customer satisfaction and retention, also examined how customer satisfaction mediates these relationships. The study followed the case study design and focused on NSSF employees and members. A sample of 50 respondents was chosen by means of simple random sampling. Structured questionnaires and reviews of documents were used as sources of data collection. To analyse the primary data the Statistical Package of Social Sciences (SPSS) was used, and multiple regression analysis was performed to establish relationships between independent variables (relationship marketing, quality of service, CRM), mediating variable (customer satisfaction), and dependent variable (customer retention). The results showed that service quality and CRM had a positive, significant effect on customer satisfaction and customer retention, and relationship marketing had a smaller or less significant impact. Additionally, customer satisfaction emerged to have a critical mediating impact and enhanced the influence of service quality and CRM on customer retention. The paper concludes that service quality practices and high-quality CRM systems are the key to improving the satisfaction and retention of members of social security institutions. It recommends that NSSF should consider staff training on relationship marketing, continuous quality improvement and CRM systems to improve customer experience and customer loyalty. The research can be extended to other regions and pension schemes in future to provide comparative data.

The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main objective of this paper is to examine potential solutions to the security problems that arise in IoT environments. It includes a thorough analysis of recent research and technological innovations in the field, with a particular emphasis on how different security methods are applied across IoT systems. By identifying the most common security vulnerabilities and outlining their impact on IoT networks, the review suggests improved methods to safeguard IoT data and ensure privacy. The findings aim to support researchers, developers, and businesses in designing more secure IoT solutions, and contribute to the establishment of stronger data protection policies. Ultimately, the review serves as a resource for those seeking to enhance the security of IoT devices and systems in an increasingly interconnected world.