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Using Big Data to Improve Customer Experience and Business Performance
Abstract
In an environment where communications service providers (CSPs) increasingly have the same service offers and devices, offering a superior customer experience is a priority to compete. Solutions that have the ability to highlight what really matters in driving customer satisfaction and deliver actionable insights from their wide-reaching customer, network, and service data are key differentiators for CSPs. This paper explores ways of integrating big data insights with automated and assisted processes related to key customer touchpoints to ultimately improve the customer experience. We show how innovation from Alcatel-Lucent and Bell Labs helps CSPs improve their business performance, using unique methodology designed to select the right key quality indicators, build accurate key business objective “formula,” predict customer behavior, and ultimately understand which factors are influencing the most. This can be used for example to improve the Net Promoter Score (NPS). The net result is a happier customer and a higher customer value. © 2014 Alcatel-Lucent.
... Although some data suggests an association between Big Data implementation and performance outcomes [38], there is much less research available on how business performance outcomes can be made sustainable with adoption and usage of Big Data. For example, business performance improvement is an area of research that has received considerable attention, where researchers are reporting an enhancement of business performance from across the sectors and across the globe after Big Data implementation [37,38,[40][41][42]. However, the role of business performance outcomes that are sustainable with long-term implementation of Big Data, have yet not been studied, though the reason could be the fairly nascent introduction of Big Data in business. ...
... Business performance is assessed through employees' perceptions about their company's performance on revenue enhancement, customer satisfaction and employee satisfaction, which are the performance indicators used to evaluate performance [37,38,[40][41][42], see Table 1. ...
... The following Figure 1 shows the conceptual framework of the research: Business performance is assessed through employees' perceptions about their company's performance on revenue enhancement, customer satisfaction and employee satisfaction, which are the performance indicators used to evaluate performance [37,38,[40][41][42], see Table 1. ...
The UAE has officially launched the Big Data initiative in the year 2022; however, the interest in and adoption of Big Data technologies and strategies had started much earlier in the private and public sectors. This research aims to explore the perceptions of the UAE employees on factors needed to implement sustainable Big Data and the continuous impact on their organizational performance. A total of 257 employees were randomly selected for an online survey, and data were collected using a Likert-style five-point scale that was tested for validity and reliability. The findings indicate that employees believe that Big Data Sustainable Implementation leads to Business Performance. Additionally, employees consider factors such as Big Data Architecture Quality, Human Cognitive Factors, and Organizational Readiness to significantly impact on Sustainable Implementation. Further, a moderating impact of Human Cognitive Factors was found on the relationship between Big Data Architecture Quality and Sustainable Implementation. The study provides managerial insights and recommendations for policymaking.
... The value of big data is not its size -it is that it can offer new kinds of information to study -information that has never previously been collected (Stephens-Davidowitz, 2017). The amount of data produced by users nowadays exponentially increases, and by reviewing the data, generated by users while interacting with IS, development teams could understand better what users are really doing and how they respond; and in addition, some defects are detected in real-time (Spiess et al., 2014;King, Churchill, and Tan, 2017). Development teams in the IT sector already collect huge amounts of implicit feedback in the form of usage data, error records, and sensor data (Maalej et al., 2015). ...
... The more data is available, the greater the chances are of understanding the user and his behavior (Spiess et al., 2014;Anderson, 2015). When tracking objective user data, some authors distinguish between active and passive monitoring, i.e., there are two types of data: implicit and explicit (Maalej et al., 2015;Liikkanen, 2016). ...
... A survey conducted in Sweden shows that there is a lot of potential for data-driven decision making in agile software development but currently unfulfilled (Svensson, Feldt, and Torkar, 2019). The development team should be able to incorporate the demands of all users when deciding what to develop and when to release the application (Spiess et al., 2014;Maalej et al., 2015). Collecting data, learning from it, and making iterations in the (re)design phase to find positive and negative elements leads to data-based approaches that provide systematic observation and a more ambitious approach for modeling and development (King, Churchill, and Tan, 2017). ...
... Whether it is a lack of information, confusing navigation on a website, or a disconnect between online and offline experiences, recognizing these challenges allows luxury car manufacturers to address them effectively Data 2024, 9, 48 5 of 20 and enhance the overall customer experience. Ultimately, the insights gained from customer journey analysis can drive improvements in marketing strategies [35], personalized customer interactions, and the development of more targeted and effective communication campaigns. By aligning their efforts with the customer journey, luxury 179 car brands can create a holistic and tailored experience that resonates with their customers and fosters brand loyalty. ...
... This information is invaluable for luxury car manufacturers and marketers looking to tailor their products and marketing strategies to this demographic. While ordinary car data analysis can also offer customer insights, the luxury car segment provides a more affluent and diverse customer base, making the insights more valuable [35]. • Advanced Safety and Driver Assistance Systems-Luxury cars often feature cuttingedge safety and driver assistance systems, generating data related to adaptive cruise control, lane-keeping assistance, and collision detection. ...
The concept of luxury, considering it a rare and exclusive attribute, is evolving due to technological advances and the increasing influence of consumers in the market. Luxury cars have always symbolized wealth, social status, and sophistication. Recently, as technology progresses, the ability and interest to gather, store, and analyze data from these elegant vehicles has also increased. In recent years, the analysis of luxury car data has emerged as a significant area of research, highlighting researchers’ exploration of various aspects that may differentiate luxury cars from ordinary ones. For instance, researchers study factors such as economic impact, technological advancements, customer preferences and demographics, environmental implications, brand reputation, security, and performance. Although the percentage of individuals purchasing luxury cars is lower than that of ordinary cars, the significance of analyzing luxury car data lies in its impact on various aspects of the automotive industry and society. This literature review aims to provide an overview of the current state of the art in luxury car data analysis.
... Finally, in the blue cluster, we find articles that deal with how to integrate Big Data insights into automated processes related to key customer touchpoints to improve customer value (Spiess et al., 2014) and how to use AI to deliver coherent streams of connections through different touchpoints for effective customer engagement (Singh et al., 2020). In the centre, still in blue, some articles examine how AI might affect the core characteristics of CRM (Libai et al., 2020) and improve operational efficiency and customer service (Prentice and Nguyen, 2020), in particular with the use of bots (Trivedi, 2019). ...
... In fact, some studies have validated a specific technique in a real-world setting (Spiess et al., 2014;Chatterjee et al., 2020aChatterjee et al., , 2020bMogaji et al., 2020); however, these examples are still few and isolated, and the literature does not yet provide an overarching big picture that presents and compares these new techniques and their applications. As the selection of a technique depends on many factors (e.g. ...
Purpose
Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management (CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research.
Design/methodology/approach
A total of 212 peer-reviewed articles published between 1989 and 2020 were extracted from the Scopus database, and 2 bibliometric techniques were used: bibliographic coupling and keywords’ co-occurrence.
Findings
Outcomes of the bibliometric analysis enabled the authors to identify three main subfields of the AI literature within the CRM domain (Big Data and CRM as a database, AI and machine learning techniques applied to CRM activities and strategic management of AI–CRM integrations) and capture promising paths for future development for each of these subfields. This study also develops a three-step conceptual model for AI implementation in CRM, which can support, on one hand, scholars in further deepening the knowledge in this field and, on the other hand, managers in planning an appropriate and coherent strategy.
Originality/value
To the best of the authors’ knowledge, this study is the first to systematise and discuss the literature regarding the relationship between AI and CRM based on bibliometric analysis. Thus, both academics and practitioners can benefit from the study, as it unveils recent important directions in CRM management research and practices.
... Research states that the amount of data generated by users while they are using the application is increasing exponentially [28]. In this reality, it must be clearly defined what kind of data might be collected and processed and how to interpret it to match the goal described in Section 2.2. ...
... Nowadays, IT companies collect enormous data about their users' behaviour and profiles, and this amount is growing exponentially [28]. The amount of data causes a serious throwback to the approach. ...
The most common reason for software product failure is misunderstanding user needs. Analysing and validating user needs before developing a product can allow to prevent such failures. This paper investigates several data-driven techniques for user research and product design through prototyping, customer validation, and usability testing. The authors implemented a case study software product using the proposed techniques, and analyses how the application of UX/UI research techniques affected the development process. The case study results indicate that preliminary UX/UI research before the development reduces the cost of product changes. Moreover, the paper proposes a set of metrics for testing the effectiveness of UX/UI design.
... AI-powered CRMs enable businesses to stay ahead of evolving market trends and shifting consumer demands. By continuously analysing data and generating predictive insights, these systems help companies identify emerging opportunities, adapt their strategies, and develop innovative products and services that meet changing customer expectations [24]. ...
The advent of artificial intelligence (AI) has revolutionized the landscape of customer relationship management (CRM), enabling businesses to harness the power of intelligent automation, predictive analytics, and data-driven insights. This article explores the transformative impact of AI-powered CRMs on business success, highlighting their ability to enhance customer understanding, personalize experiences, and drive operational efficiency. By leveraging AI algorithms to analyze vast amounts of customer data, these advanced CRM systems empower businesses to anticipate customer needs, identify emerging market trends, and make informed strategic decisions. The article delves into the key advantages of AI-powered CRMs, including improved customer satisfaction, increased productivity, and enhanced profitability. It also addresses the challenges associated with implementing AI in CRM, such as data privacy concerns and employee adaptation, while providing practical recommendations for businesses considering adoption. Through real-world examples and expert insights, this article underscores the vital role of AI-powered CRMs in shaping the future of customer engagement and business growth in an increasingly competitive and data-driven marketplace.
... In an era of digitization and Big Data, it is now possible for companies to focus on the potentially unrecognized desires and values of customers when developing their products (Grieger & Ludwig, 2019;Tseng, 2023;Zhan et al., 2018). Customer integration improves customer satisfaction and reduces the market risk in innovation and product improvements (Krcmar, 2015;Spiess et al., 2014). In this context, customer studies can be used to measure the intention to use through the active participation of customers. ...
In the era of digital transformation, automotive companies are rapidly evolving towards customer integration and innovation. This study addresses a critical issue of low usage rates for the lane change assistance function in advanced driving assistance systems. While existing literature focuses on labeling lane change maneuvers in terms of appropriateness at a given moment, overlooking the frequency and classification of different lane change scenarios. This study selects and combines over 104,000 lane change events extracted from customer fleet data and applies the k-means clustering algorithm. The five distinct cluster groups indicate the categorization and frequency of different lane change scenarios. This approach is based on real customer usage data, providing practical benefits for developing customer-valued functions in semi-autonomous vehicles. The findings contribute to a new methodology for enhancing the lane change assistance function and underscore the importance of understanding specific lane change scenarios for efficient and safe driving experiences.
... The empirical studies (Akter Wamba, Gunasekaran, Dubey & Childe, 2016;Wamba, Gunasekaran, Akter, Ren, Dubey, & Childe, 2017;Côrte-Real, Oliveira, & Ruivo, 2017;Spiess, T'Joens, Dragnea, Spencer, & Philippart, 2014;Singh & El-Kassar, 2019;Wamba, Gunasekaran, Akter, Ren, Dubey, & Childe, 2017) reviewed in this study found significant positive relationship between big data and business performance in Indian, European countries, Indonesia, Thailand and China. Therefore, Big data would enhance further technological growth and consequently give rise to sustainable SMEs performance in developing economy (Nigeria). ...
The fourth industrial revolution has great influence on entrepreneurial development and economic growth since the revolution is gathering new jobs with new skills, and a majority of the senior workforce will probably have a lot of problems with those kinds of changes and challenges. The objectives of this paper are to present the features components of Industry 4.0, the relationship between Big data and sustainable business performance, the relationship between the Internet of a Thing (IoT) and sustainable SMEs' performance, and also the relationship between smart factories and sustainable SMEs' performance. Based on the conceptual model, the explanatory research design was employed; the model indicates there would be a significant relationship between 4.0 components (Big data, IoT & smart factory) and sustainable SMEs' performance. The paper concluded with the recommendation that government and SME owners/managers should avail themselves of the 4th industrial revolution's advantages and leverage big data, the Internet of Things, and smart factories to promote information technology (IT), and implementation, which contributes to sustainable SMEs' performance.
... The development of internet and cellular technologies has led to a large rise in the flow of information relevant to consumers. [7,8] This increase in information flow has led to a major increase in the flow of information. In addition, a significant number of thriving companies are considering the possibility of making use of such data in order to effectively manage the ties they have with their clients. ...
The primary objective of this paper is to present a holistic and usefully managerially oriented perspective on customer relationship management (CRM) as a kind of company strategy. The primary goals are as follows: to investigate the beginnings of CRM, its subsequent development and the changes that have taken place over the course of time, as well as to outline its future directions; to evaluate the many different definitions of CRM and select the one that most accurately captures the essence of CRM; to investigate the components of CRM; to develop a structure that guarantees CRM is approached in a strategic, balanced, and integrated manner; to define CRM strategy; and to locate CRM strategy within the who, what, where, when, why, The article offers recommendations for effective CRM strategy applications. This study article makes an attempt to analyse researches on Big Data Analytics, Data Mining methods, and "Big Data Analytical Frameworks that may be employed in Customer Relationship Management. The purpose of this study is to present existing uses of Big Data in Customer Relationship Management, as well as their problems, limits, and potential future prospects in this sector by analysing the aforementioned research".
... Social network data mining and sentiment analysis fieldshave become challenging and the problems researched applying them appear to be exciting as organizations have started dealing with large volumes of complex and timely arriv ing data volumes using the latesthardware and software.Ext racting reliable and actionable inputs from the data pool is a resource consuming and challenging task. It is required for a successful business to master this art of predictive analysis from the available information to stay ahead of competitors and also stay in the competition Thanks to the recent smartphone revolution and new technologies in the telecom sector,the mobile phone density has reached higher levels [9]. In this scenario, subscriber churn has raised alarms for telecom operators as customerschange their operators for service and quality reasons. ...
Predicting customer churn is a big challenge and a survival basic for Telecom operators. In a large and competitive market like India, it is very essential to gather real-time customer feedback as a health indicator. Social networks have evolved as a rich source of real-time sentiments and opinions of the general public. In this research, tweets for the Twitter handle of 5 major telecom brands in India: Aircel, Bharti Airtel, Idea Cellular, Reliance Jio and Vodafone India were extracted for six months to develop a prediction model for telecom subscriber churn prediction using the sentiment score. Naïve Bayes classifier implementation and TextBlob library of Python were used to assign polarities to user sentiments. Customer satisfaction represented by the overall monthly sentiment score has been used to predict customer churn. The predictions made by the model were validated using IBM S PSS and were within the acceptable limits. The results of the sentiment analysis based prediction model can be of great use for telecom operators to take timely actions for improving the future customer experience and avoiding customer churn.
... As the 4th Industrial Revolution unfolds, many advanced technologies have emerged, catalyzing significant transformations in the logistics industry [1]. Driven by big data, a core component of the revolution, the logistics sector has shifted its focus from suppliers to consumers [2,3]. This shift has accelerated the adaptation of various fields to swiftly accommodate consumer requirements, including providing tailored services, enhanced reliability, customized mass transportation, and automated logistics warehouses. ...
This study aims to predict new technologies by analyzing patent data and identifying key technology trends using a Temporal Network. We have chosen big data-based smart logistics technology as the scope of our analysis. To accomplish this, we first extract relevant patents by identifying technical keywords from prior literature and industry reports related to smart logistics. We then employ a technology prospect analysis to assess the innovation stage. Our findings indicate that smart logistics technology is in a growth stage characterized by continuous expansion. Moreover, we observe a future-oriented upward trend, which quantitatively confirms its classification as a hot technology domain. To predict future advancements, we establish an IPC Temporal Network to identify core and converging technologies. This approach enables us to forecast six innovative logistics technologies that will shape the industry’s future. Notably, our results align with the logistics technology roadmaps published by various countries worldwide, corroborating our findings’ reliability. The methodology presents in this research provides valuable data for developing R&D strategies and technology roadmaps to advance the smart logistics sector.
... Additionally, studies have found a positive impact of technology on customer experience. For example, Spiess et al. [35] found that integrating big data insights with process automation in different customer touchpoints improves customer experience. Moreover, Huseynov [36] discusses using big data to transform crucial business operations for better decision-making and improved customer experience. ...
Digital transformation (DT) has attracted the attention of management and organizational scholars in the past decade. In addition, firms are increasingly interested in using DT to obtain a competitive advantage. Nevertheless, studies on DT outcomes remain scarce. Therefore, this study empirically investigated the effect of digital transformation on firm performance by classifying the capabilities required to realize digital transformation, customer experience, and IT innovation. A structured questionnaire was used to collect data from 164 representatives of service sector firms in Saudi Arabia, namely chief information officers, chief transformation officers, and IT managers. Based on the findings of this study, it is evident that digital transformation, customer experience, and IT innovation positively impact a firm’s performance, with customer experience exhibiting the strongest effect.
... These approaches open up numerous opportunities to capture value resulting from customer engagement behaviors, leading to advanced competencies that help firms sustain value creation over time [20]. In this topic, the authors often explore ways of integrating big data insights with automated and assisted processes related to key customer touchpoints to ultimately improve the customer experience [21][22][23]. ...
... Melihat akar penyebab dan diagnostik merupakan bentuk analisis deskriptif yang melibatkan analisa pada data, dan juga melihat ke belakang dan mengungkapkan apa yang telah terjadi dan memulai tindakan tertentu pada data yang diuji, dan membacakan hasilnya [12]. Statistik biasanya berupa deskriptif (menggambarkan keadaan sesuai data) atau inferensial (mencari beberapa kemungkinan adanya perbedaan dalam sebuah data). ...
Data is a collection of information that contains a broad picture related to a situation. The amount of data is not necessarily better, because a large data set makes it difficult to convert data into information in a timely manner, especially in analyzing data which produces meaningful and relevant information which ultimately results in quick and appropriate action. Higher education management in Indonesia requires fast and accurate academic reports so that it can facilitate strategic decision making in order to improve the quality of education. This study aims to carry out a comprehensive process of analyzing academic data at universities to display them into interactive data visualizations, so that they can retrieve the information in it and make strategic decisions. The method used is a data visualization technique, which allows users to easily see the insights or insights contained in the data. The results obtained are data that has passed the preprocessing stage, can prepare data before being analyzed and processed to be used to make data visualization, so that the information obtained is more varied. This information can be used as a reference by academic managers to make strategic decisions.
... The former was assessed with a technical log book on the cloud that was updated daily with technical issues and suggestions for improvement from all study participants. The end users' perceived usability and satisfaction were assessed by means of the System Usability Scale (SUS) [29] and Net Promoter Score (NPS) [30], alongside general questions about satisfaction. ...
Background
Innovative digital health tools are increasingly being evaluated and, in some instances, integrated at scale into health systems. However, the applicability of assessment methodologies in real-life scenarios to demonstrate value generation and consequently foster sustainable adoption of digitally enabled health interventions has some bottlenecks.
Objective
We aimed to build on the process of premarket assessment of 4 digital health interventions piloted at the Hospital Clinic de Barcelona (HCB), as well as on the analysis of current medical device software regulations and postmarket surveillance in the European Union and United States in order to generate recommendations and lessons learnt for the sustainable adoption of digitally enabled health interventions.
Methods
Four digital health interventions involving prototypes were piloted at the HCB (studies 1-4). Cocreation and quality improvement methodologies were used to consolidate a pragmatic evaluation method to assess the perceived usability and satisfaction of end users (both patients and health care professionals) by means of the System Usability Scale and the Net Promoter Score, including general questions about satisfaction. Analyses of both medical software device regulations and postmarket surveillance in the European Union and United States (2017-2021) were performed. Finally, an overarching analysis on lessons learnt was conducted considering 4 domains (technical, clinical, usability, and cost), as well as differentiating among 3 different eHealth strategies (telehealth, integrated care, and digital therapeutics).
Results
Among the participant stakeholders, the System Usability Scale score was consistently higher in patients (studies 1, 2, 3, and 4: 78, 67, 56, and 76, respectively) than in health professionals (studies 2, 3, and 4: 52, 43, and 54, respectively). In general, use of the supporting digital health tools was recommended more by patients (studies 1, 2, 3, and 4: Net Promoter Scores of −3%, 31%, −21%, and 31%, respectively) than by professionals (studies 2, 3, and 4: Net Promoter Scores of −67%, 1%, and −80%, respectively). The overarching analysis resulted in pragmatic recommendations for the digital health evaluation domains and the eHealth strategies considered.
Conclusions
Lessons learnt on the digitalization of health resulted in practical recommendations that could contribute to future deployment experiences.
... Big data can also contribute at achieving competitive advantage (Barham, 2017;Mikalef et al., 2020;Sellami et al., 2020) and creating value in an organization (Grover et al., 2018;Line et al., 2020;Vidgen et al., 2017). Organizations implementing data-driven strategies also gain by improving customer experience (Kodapanakkal et al., 2020;Spiess et al., 2014) and the overall firm performance (Akter et al., 2016;Yasmin et al., 2020). As stated above, the aim of this study is to provide a systematic review on the literature related to business applications of big data in the tourism industry. ...
This review paper aims at providing a systematic analysis of articles published in various journals and related to the uses and business applications of big data. The goal is to provide a holistic picture of the place of big data in the tourism industry. The reviewed articles have been selected for the period 2013-2020 and have been classified into 8 broad categories namely business strategy and firm performance; banking and finance; healthcare; hospitality; networks and telecommunications; urbanism and infrastructures; law and legal regulations; and government. While the categories are reflective of components of tourism industries and infrastructures, the meta-analysis is organized around 3 broad themes: preferred research contexts, conceptual developments, and methods used to research big data business applications. Main findings revealed that firm performance and healthcare remain popular contexts of research in the big data realm, but also demonstrated a prominence of qualitative methods over mixed and quantitative methods for the period 2013-2020. Scholars have also investigated topics involving the notions of competitive advantage, supply chain management, smart cities, but also ethics and privacy issues as related to the use of big data.
... The first BPM-DI trend implies that digital technologies continuously change the experience of end customers and this possibly with an increased speed. With enriched data management and big data analytics, organizations can use data for incorporating customer-centric offerings (Spiess et al., 2014). Market responsiveness and creating value propositions about customer requirements are the basics of developing a new and exceptional customer experience (Barnes et al., 2009). ...
... Therefore, Big Data can be used efficiently in a variety of fields, including information technology to increase security and troubleshooting by examining patterns in existing logs; customer service by using information from call centres to obtain customer patterns and, thus, improve customer satisfaction by customising services; enhancing services and goods by using social media content. Various studies have assessed the efficacy of big data, such as [17] and [46], who described that a business which knows prospective customers' preferences allows it to adapt its product in order to reach a broader group of individuals. ...
This exploratory research aims to ascertain participants’ perspectives on the use of Big Data and Big Data Analytics methods during audit brainstorming sessions at Canadian audit firms, and whether such methods aid in the risk assessment process to fraud detection. A Canadian qualitative research method is applied in this study to provide an overview of the results from audit industry interviews. The complete sample included twenty-two external auditor participants who attended an office interview in Canada during the third and fourth quarters of 2019; twelve auditors from the Big-4 and ten from mid-size audit firms. The research discovered that, on average, brainstorming sessions interpret the impact of Big Data and Big Data Analytics favourably. Additionally, our study findings show that utilising Big Data Analytics during brainstorming sessions improves the efficiency and effectiveness of fraud risk evaluations substantially. Finally, the paper discussed how Big Data had altered auditors’ positions and professional decisions. Our analysis and results have a broader effect on the purpose of fraud detection brainstorming sessions and the quantification of fraud risk in an audit context.KeywordsBig dataBig data analyticsBrainstormingAuditCanadian audit firms
... Using BDA in e-commerce helps in gaining customer trust by delivering on the promise of reliable products and consistent service Akter and Wamba (2016) ct4 BDA enables e-commerce industry drives to gain customer loyalty by ensuring networks, services, and operations are meeting their customers' functional needs by working flawlessly Spiess et al. (2014) Data Privacy (DP) dp1 ...
Purpose
The study aims to identify the potential drivers of big data analytics (BDA) practices in the supply chain and develop a sustainability evaluation model to evaluate drivers of big data for sustainability development.
Design/methodology/approach
The mixed-method approach was applied to assess sustainability dimensions and calculate the score using two phases. In Phase I, the BDA drivers in the e-commerce industry were finalised using the partial least square based structural equation modelling (PLS-SEM) method. In Phase II, a case study in the Indian fashion e-commerce industry was carried out to evaluate sustainability dimensions with respect to drivers of BDA and the sustainability score was calculated using the fuzzy analytical hierarchical process (AHP) method.
Findings
The index for economic sustainability (0.220), social sustainability (0.142) and environmental sustainability (0.182) were derived. The higher index value of economic sustainability compared to social sustainability and environmental sustainability signified those drivers of big data bring social and environmental uncertainty along with economic sustainability.
Research limitations/implications
The study will help practitioners promote BDA use for developing environmental/social/economic sustainability in supply chains. Policymakers must ensure whether the integration of BDA practices brings down cost and brings strategic value for ensuring big data success. The study will help managers decide a constant trade-off between the requirement for social, environmental and economic performance.
Originality/value
The study corroborates and adds to the BDA literature by emphasising the positive role of BDA in sustainability development in the supply chain area and highlighting the significant role of different drivers of BDA in sustainability development.
... Application description: Customer experience management is a known business where ML is increasingly used [58]. ...
The maturity of machine learning (ML) development and the decreasing deployment cost of capable edge devices have proliferated the development and deployment of edge ML solutions for critical IoT-based business applications. The combination of edge computing and ML not only addresses the development cost barrier, but also solves the obstacles due to the lack of powerful cloud data centers. However, not only the edge ML research and development is still at an early stage and requires substantial skills normally missed in resource-constrained communities, but also various infrastructure constraints w.r.t. network reliability and computing power, and business contexts from the resource-constrained environments require different considerations to make edge ML applications context aware through smart and intelligent runtime strategies. In this paper, we analyze representative real-world business scenarios for edge ML solutions and their contexts in resource-constrained communities and environments. We identify and map the key distinguished contexts of distributed edge ML and discuss the impacts of these contexts on data and software components and deployment models. Finally, we present key research areas, how we should approach them, and possible tooling for making edge machine learning solutions smarter in resource-constrained communities and environments.
... Nomological validity was tested by regressing theoretically related antecedents (e.g., motivations to adopt digital technologies) to the formative constructs. For example, we found that smart manufacturing technologies were significantly positively associated with reshoring, in line with Stentoft and Rajkumar (2020), and that data processing technologies were significantly positively associated with improving customer service, in line with previous literature (Spiess et al., 2014). Therefore, we concluded that the measures related to digital technologies are reliable and valid constructs. ...
There is a great expectation that Industry 4.0 technologies will enable better circular economy (CE) results at firms. However, it is unclear how these technologies might contribute to CE. We hypothesize that Industry 4.0 technologies are positively related to the level of integration among actors along the supply chain and within the firm supply chain integration (SCI), which, in turn, explains superior CE results. By employing partial least square structural equation models on original survey data based on a sample of more than 1200 Italian manufacturing firms and almost 200 adopters, we find that disentangling for the type of technologies is essential to understanding both their direct and indirect role toward CE. Smart manufacturing technologies have a stronger impact on CE outcomes than data processing technologies; the mediating effect of SCI is verified for the former but not for the latter type, questioning the possibility for those technologies to support sustained CE performance in the long run.
... An implicit assumption is that new technologies (e.g. AI and robots) provide opportunities to integrate better physical and virtual touchpoints via intelligent processes to improve customers' experiences Savastano et al., 2019;Spiess et al., 2014). Such an integration interaction elicits important technological interdependencies Voorhees et al., 2017) and improves transition to phygital systems (Stankov and Gretzel, 2020). ...
Purpose
This paper aims to focus on how companies shape the architecture of a phygital customer journey through the exploitation of smart technologies.
Design/methodology/approach
The research adopts a qualitative method using a grounded theory approach involving leading players in digital customer solutions and service providers from different industries.
Findings
The shaping of the architecture of the phygital customer journey comes from the interplay between systems of insights and systems of engagement activated by multiple customer-provider interactions in an entanglement of physical and digital contexts.
Practical implications
Companies need to design a blended approach to bridge disconnected contexts, capture new opportunities and provide customer engagement along the entire journey.
Originality/value
This study depicts the “phygital customer journey” under systems of insights and systems of engagement: These systems operate as dynamic architectures to capture insights and engage customers.
... In the era of stiff competition, service providers who can provide improved customer experience will be able to thrive in the marketplace [107]. The big data insights after implementation of Industry 4.0 will result in automated and assisted processes at key customer touchpoints, which will give customers a new experience [108]. Service organizations after implementing Industry 4.0 can benefit from improved customer experience which may impact their sales positively. ...
Industry 4.0 marks a new paradigm and has expanded its domain from theoretical concepts to real-world applications. Industry 4.0 is, however, still in the state of infancy and conceptual state wherein it is not clear as to how to incorporate many dynamic technological concepts in different sectors. Previous studies have conceptually delineated the benefits, challenges, and CSFs of Industry 4.0, however, there is yet to be an empirical study that critically examines the differences in benefits, challenges, and critical success factors (CSFs) of Industry 4.0 in both manufacturing and service industries and rank them. This study through an online survey captures the view of senior management professionals who have experience in Industry 4.0 implementation in major companies in Asia, Europe, and North America. 96 senior management professionals participated in this study through an online survey. The qualitative data on benefits and challenges were analysed using thematic analyses. The quantitative data on critical success factors were ranked using the normalisation of the mean to find the most important factors. Further agreement analysis was conducted in the manufacturing and service sectors for the CSFs. This study identifies the top five benefits and challenges in the manufacturing and service industries. The CSFs for Industry 4.0 was put forward and ranked in both the manufacturing and service industries.
... The aims of customer behaviors' analytics can be generally divided into detection of behavioral patterns and prediction of behaviors [135]. The former allows for a better understanding (description) of customers, while the latter, for an adjustment of enterprise's actions towards actual and potential customers. ...
The main purpose of this paper is to provide a theoretically grounded discussion on big data mining for customer insights, as well as to identify and describe a research gap due to the shortcomings in the use of the temporal approach in big data analyzes in scientific literature sources. This article adopts two research methods. The first method is the systematic search in bibliographic repositories aimed at identifying the concepts of big data mining for customer insights. This method has been conducted in four steps: search, selection, analysis, and synthesis. The second research method is the bibliographic verification of the obtained results. The verification consisted of querying the Scopus database with previously identified key phrases and then performing trend analysis on the revealed Scopus results. The main contributions of this study are: (1) to organize knowledge on the role of advanced big data analytics (BDA), mainly big data mining in understanding customer behavior; (2) to indicate the importance of the temporal dimension of customer behavior; and (3) to identify an interesting research gap: mining of temporal big data for a complete picture of customers.
... Each moment of the phygital journey entail a deeper-level experiential journey with multiple online and offline touchpoints and several activities and interactions that affect emotions and behaviours. This aspect also mirrors the opportunity to customise CJs (Spiess et al., 2014). The intended journey is changed by what customers discover during the journey, what their emotions lead to, what insights they gain during interactions (online and offline) and even the consequences of the activities they perform. ...
Purpose
The wider possibility of connectivity offers additional opportunities for customers to experience value propositions. The online world is only one side of the customer experience. The integration of digital technologies, social presence and physical elements increases the complexity of customer journey. This paper aims to map the phygital customer journey by focusing on millennials.
Design/methodology/approach
The study adopted a qualitative methodology to investigate 50 millennials from Italy. Millennials had to describe, in two phases, a journey they had recently made. First, they used sticky notes with no restrictions on expressing their feelings and structuring their CJ. Second, customers transferred the sticky notes’ contents, consider the information provided and map the journey with additional details using the Uxpressia software.
Findings
This paper frames the Millennials customer journey as a cycle of four moments: connect, explore, buy and use. Each moment enacts the customer experience as a mixture of emotional, behavioural and social responses. Online and offline interactions blur the boundaries between the physical and digital world (i.e. phygital): millennials move back-and-forth or jump from one action to another according to the evolving path of emotions and interactions.
Originality/value
The phygital customer journey provides an alternative understanding of customer journey occurring as a fuzzy process or loop. A phygital map develops as a circular path of moments seen as phenomenological microworlds of events, interactions, relationships and emotions.
... According to them, the result should be a better relationship with the customers, workable advantages, and cost reduction in the current market where CSPs (communication service providers) have very similar service offers; providing a better customer experience is a significant priority. In their work, Spiess et al. [48] investigated different ways of implementing big data to improve customer experience. The authors mainly concentrated on the technology from Alcatel-Lucent and Bell Labs for improving business performances. ...
Customization of products or services is a strategy that the business sector has embraced to build a better relationship with the customers to cater to their individual needs and thus providing them a fulfilling experience. This whole process is known as customer relationship management (CRM). In this context, we extensively surveyed 138 papers published between 1996 and 2021 in the area of analytical CRM. Although this study consisted of papers from different business sectors, a fair share of focus was directed to the telecommunication industry and generalized CRM techniques usages. Different science and engineering-based data repositories were studied to ascertain significant studies published in scientific journals, conferences, and articles. The research works on CRM were considered and separated into IT and non-IT-based techniques to study the methods used in different business sectors. The main target behind implementing CRM is for the better revenue growth of the company. Different IT and non-IT-based techniques are used in the analytical CRM area to achieve this target, and researchers have been actively involved in this domain. The purpose of the research was to show the impact of IT-based techniques in the business world. A detailed future course of research in this area was discussed.
This study explores the impact of metaverse technology on business models (BMs). Despite increasing academic and practical interest, the adoption and seamless integration of this technology poses substantial challenges for businesses. This study adopts a grounded-theory approach to explore how firms can incorporate this technological innovation within their existing BMs. Drawing upon insights from 20 interviews, the data were structured using the Gioia methodology, uncovering 5 dimensions that elucidate how companies can leverage metaverse technology to augment value creation, capture, and delivery, both internally and externally, within their BMs. These dimensions serve as a roadmap for firms seeking to embrace the metaverse, offering insights into potential adaptations to their existing BMs. This study contributes to the theoretical discourse surrounding the metaverse by delineating specific components within BMs that can be tailored to accommodate metaverse integration. Furthermore, our findings offer invaluable guidance and recommendations to firms and ventures, highlighting the diverse areas within the value creation process where metaverse integration can be strategically applied. This research lays the foundation for a more comprehensive understanding of metaverse technology's role in shaping the business landscape.
In this chapter, we will discuss the applications of various martechs in different activities and operations related to digital marketing, including value creation and capture; customer collaboration and co-creation; digital segmentation, targeting and positioning; integrated digital marketing communication; digital branding; digital consumer behavior; product management and development; price management; delivery and tracking; customer relationship, experience and journey; digital platforms and digital marketing channel management; digital selling and retailing; and digital evidence management.
Digital transformation refers to strategic activities undertaken by organizations to improve and simplify their process and even alter their business models with abreast to enhance firm performance. Thus, the aim of this study was to analyze the impact of digital transformation on organizational performance among the Jordanian commercial banks listed on the Amman Stock Exchange. The descriptive research design was used in this quantitative study. Primary data were collected to achieve the objectives of the study. The target population was employees (managers and non-managers) of Jordanian commercial banks listed on the Amman Stock Exchange. The sample size was selected using Krejcie and Morgan rule; after data cleaning procedures, the final sample of 282 respondents was used for final analysis. The study employed regression analysis to arrive at the results. The results confirm that digital transformation has a significant positive effect on customer experience and IT innovation. These results were significant at a 1% level. The results also confirm that digital transformation has a significant positive effect on firm performance, with a significance level of 1%. Moreover, the significant positive impact of customer experience and IT innovation was confirmed. Therefore, the significant positive impact of digital transformation on firm performance was found viz-a-viz direct as well as indirect route.
Digital transformation refers to strategic activities undertaken by organizations to improve and simplify their process and even alter their business models with abreast to enhance firm performance. Thus, the aim of this study was to analyze the impact of digital transformation on organizational performance among the Jordanian commercial banks listed on the Amman Stock Exchange. The descriptive research design was used in this quantitative study. Primary data were collected to achieve the objectives of the study. The target population was employees (managers and non-managers) of Jordanian commercial banks listed on the Amman Stock Exchange. The sample size was selected using Krejcie and Morgan rule; after data cleaning procedures, the final sample of 282 respondents was used for final analysis. The study employed regression analysis to arrive at the results. The results confirm that digital transformation has a significant positive effect on customer experience and IT innovation. These results were significant at a 1% level. The results also confirm that digital transformation has a significant positive effect on firm performance, with a significance level of 1%. Moreover, the significant positive impact of customer experience and IT innovation was confirmed. Therefore, the significant positive impact of digital transformation on firm performance was found viz-a-viz direct as well as indirect route.
This study proposes an AI-based paradigm for travel review analysis. Travel reviews influence customer choices and the tourist business. The framework includes data collection and preprocessing, sentiment analysis, feature extraction and topic modeling, deep learning, and insight production. Data collection and preparation provide diversified, high-quality trip review data for analysis. Sentiment analysis and opinion mining classify reviews to determine travelers happiness. Feature extraction and topic modeling reveal traveler preferences and interests by extracting keywords, topics, and features from reviews. Deep learning methods like CNN and LSTM networks capture complicated travel review structures and dependencies, improving sentiment analysis and opinion mining. These methods help contextualize passengers feelings and preferences throughout time. The proposed approach yields travel industry-wide information. They assist in data-driven decision-making, personalized suggestions, destination marketing and management strategies, service quality, market research and competitive analysis, and product creation. AI travel review analysis presents obstacles and considerations. These include data quality and availability, privacy and ethical considerations, bias mitigation and fairness, interpretability and explainability, technical skills and resources, user acceptability and adoption, and continual learning. By adopting the proposed conceptual framework and overcoming the obstacles, travel companies can gain deeper insights from travel reviews, improve customer experiences, and make informed decisions to stay competitive in the dynamic and growing travel market. Travel review analysis, artificial intelligence, conceptual framework, sentiment analysis, opinion mining, deep learning, data collection, preprocessing, feature extraction, topic modeling, insights generation, decision-making, personalized recommendations, destination marketing.
Purpose
Customer experience management (CXM), which aims to achieve and maintain customers' long-term loyalty, has attracted the attention of many organizations. Improving customer experience management in organizations requires that, first, their relevant capabilities be evaluated. The present study aimed to offer a set of key performance indicators for evaluating customer experience management in commercial banks.
Design/methodology/approach
The study, first, attempted to identify the components of evaluating customer experience management by reviewing the related literature and conducting interviews with experts. Then, the extracted components were transformed into assessable metrics using the goal question metric method, and the key performance indicators relevant to customer experience management in commercial banks were selected according to the experts' opinions and the Fuzzy Delphi method.
Findings
According to the findings of the study, 21 key performance indicators were identified for customer experience management in commercial banks, and customer satisfaction, the mean number of calls to resolve an issue in customer journey touchpoints, the NPS, and the ratio of the budget allocated to the CXM department to the budget of the marketing department were found as the most significant performance indicator according to banking experts.
Originality/value
The present study was among the first research projects intended to evaluate CXM and offer key performance indicators that could help the managers of commercial banks assess the maturity levels of their CXM.
Objective-In light of the later headways in Huge Information and manufactured insights (AI) innovations inside the domain of buyer relationship taking care of, this record presents a comprehensive survey of the field. It points to distinguish existing holes and propose potential roads for future inquire about. Methodology-We conducted a thorough look of the Scopus database and recovered a add up to of 212 articles with evaluations from others disseminated between 1989 and 2020. Two bibliometric methods, bibliographic coupling and watchword co-occurrence examination, were utilized for our examination. Results-The discoveries from our bibliometric examination permitted us to portray three essential subfields inside the AI writing relating to Client Relationship Administration: (1) Huge Information and Client Relationship Administration as a database, (2) AI and machine learning methods connected to Client Relationship Administration forms, and (3) the key administration of AI-CLIENT RELATIONSHIP ADMINISTRATION integrative. For each of these subfields, we recognized promising bearings for future improvement. Also, this consider presents a three-step conceptual demonstrate for the execution of AI in CLIENT RELATIONSHIP ADMINISTRATION, which can serve as a important asset for both analysts looking for to dig more profound into this space and supervisors arranging cohesive techniques. Novelty/Significance-To the leading of our information, this ponder speaks to the primary orderly endeavour to synthesize and examine the writing on the collaboration between AI and CLIENT RELATIONSHIP ADMINISTRATION through the focal point of bibliometric investigation. As a result, both scholastic researchers and industry professionals stand to advantage from our discoveries, as they divulge later basic headings in CLIENT RELATIONSHIP ADMINISTRATION inquire about and hone.
The chapter on new and future trends in research explores the latest developments in research on innovation capabilities and entrepreneurial opportunities in the context of smart working. The chapter highlights emerging research trends, including the use of AI and machine learning for smart working, the impact of smart working on employee well-being, and the intersection of smart working with sustainability goals. The implications of these trends for innovation and entrepreneurship are explored, including challenges and opportunities such as keeping up with technological advancements, balancing benefits and drawbacks, and enhancing innovation capabilities while reducing environmental impact. Smart working research offers opportunities to improve work-life balance and contribute to sustainability goals by reducing commuting and associated emissions. By staying on the cutting edge of research, organizations can position themselves to capitalize on emerging opportunities and remain competitive in the future.
Telecom network often encounters large number of tweets based on the user experience for a network. This huge amount of raw data can be used for industrial or business purpose by organizing them according to our requirement and processing. The aim of this paper is to address the social media review challenges in telecom companies. The methods include; extracting tweets, analyzing them and segregating them into various categories to help the company understand the concerns of their customer. This can help save millions and prevent customer churn. In other to build a robust model, the dataset was pre-processed by checking and removing Nan values. After the pre-processing, stage the cleaned data was tokenized. In tokenization process, each word was divided into tokens, for easy training. After the tokenization process, we performed an exploratory data analysis on the dataset to understand the pattern of the dataset. After the explorative data analysis stage, we trained a random forest classifier to predict/classify the customer's satisfaction into positive, negative, and neural.
In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely.KeywordsCustomer Journey MiningCustomer Journey MappingRobotic Process AutomationProcess MiningPrediction
The insights that firms gain from big data analytics (BDA) in real time is used to direct, automate and optimize the decision making to successfully achieve their organizational goals. Data management (DM) and advance analytics (AA) tools and techniques are some of the key contributors to making BDA possible. This paper aims to investigate the characteristics of BD, processes of data management, AA techniques, applications across sectors and issues that are related to their effective implementation and management within broader context of BDA. A range of recently published literature on the characteristics of BD, DM processes, AA techniques are reviewed to explore their current state, applications, issues and challenges learned from their practice. The finding discusses different characteristics of BD, a framework for BDA using data management processes and AA techniques. It also discusses the opportunities/applications and challenges managers dealing with these technologies face for gaining competitive advantages in businesses. The study findings are intended to assist academicians and managers in effectively quantifying the data available in an organization into BD by understanding its properties, understanding the emerging technologies, applications and issues behind BDA implementation.
The article is devoted to the development of strategies of business process digitalization in local government institutions. The author proves that the model of business based on the network of interaction, the domain of the digital platform, is absolutely efficient in the digital economy and provides its companies with an inapplicable competitive advantage. Progressive companies of traditional business, whose business model cannot be transformed, seeking to maintain their business and acquire competitive advantages in the digital environment, found an outlet in a hybrid approach that suggests the unification of several types of business models. Digital technologies have a significant impact on the possible kinds of digital business models. New business models define new operating models, so prioritizing the latest inhibits the digital transformation of the company: the integration of digital technologies into existing operating models can increase the efficiency of business, but will not lead to a breakthrough increase in income. New business models based on digital technologies and, among other things, on digital platforms provide companies with unique competitive advantages in the transforming economy and have only limited success in traditional business.
A decade has passed since the global financial system introduced a new phenomenon, cryptocurrencies and blockchain technology. That is evolving in Turkey where there is strong involvement with world finance capital. In this paper, we first investigate main dynamics of cryptocurrencies. Their characteristics, usage, functions and role in globe briefly. The remainder of the chapter devoted solely Turkish case. How the legal system fits into those new instruments. We also searched the drives of Turkish citizen’s demand for these new assets and their ability to cope with this investment opportunity. In the end, we do share our humble opinions for the future trends of cryptocurrency for Turkish economy.KeywordsCryptocurrencyBlockchaine-commercee-moneyTurkeyTurkish economyTurkish financial system
The World of Business has been surrounded by a whirlwind of activities, From industrial revolution Marketing 1.0 the product-centric era where Marketing as a concept was new to the world, it was confined to selling and pushing the product to the consumers, to the age of Information technology Marketing 2.0 the customer-centric era where consumers became the priority rather than the product, and marketers realized that profitable opportunities are disguised as customers unfulfilled needs and wants. In a decade, we witness the Markets undergoing several shifts in the quest to find an elixir that could turn prospects into customers and the kind of customers who keep coming back for the product or services overlooking all other competitors, and with relevant data and thorough analysis, we would like to prove with this research paper that we are living in a human-centric era the age of Marketing 3.0 a marketing technique that creates value for the customer that is beyond materialistic needs, A value that is centered towards customers belief system and their emotional sentiments. In this study, we aim to establish the impact of Value-driven marketing strategies on B2B Markets.
Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE prediction. As a consequence, dimensionality issues occur in QoE prediction models. This paper gives an overview of the used dimensionality reduction technique in QoE modeling and proposes modification and use of Active Subspaces Method (ASM) for dimensionality reduction. Proposed modified ASM (mASM) uses variance/standard deviation as a measure of function variability. A straightforward benefit of proposed modification is the possibility of its application in cases when discrete or categorical IFs are included. Application of modified ASM is not restricted to QoE modeling only. Obtained results show that QoE function is mostly flat for small variations of input IFs which is an additional motive to propose a modification of the standard version of ASM. This study proposes several metrics that can be used to compare different dimensionality reduction approaches. We prove that the percentage of function variability described by an appropriate linear combination(s) of input IFs is always greater or equal to the percentage that corresponds to the selection of input IF(s) when the reduction degree is the same. Thus, the proposed method and metrics are useful when optimizing the number of IFs for QoE prediction and a better understanding of IFs space in terms of QoE.
La doctrine dominante de la connaissance suppose une connaissance a priori, indépendante de l’expérience sensible et logiquement antérieure. Inversement, l’empirisme considère l'expérience comme source d’une connaissance a posteriori. Le pragmatisme fait de l'expérience son concept central, et ne dissocie pas la connaissance de la situation. Dans les sciences informatiques la connaissance a priori est un objet central et l’expérience est limitée à la représentation du vécu d’un agent. Les recherches sur le calcul de situations sont limitées. La gestion de l’expérience client des organisations hérite de cet état qui limite le concept d’expérience. Or les données concrètes (a posteriori) sont aussi importantes que les relations d'idées (a priori). A. N. Whitehead propose un modèle dit « organique » de l’expérience, basé sur un calcul méréotopologique d’événements spatio-temporels intra-reliés. À partir de données vidéo en entrée, cette thèse propose un calcul d’expérience basée sur la méréotopologie whiteheadienne ; le Multiple Objects Tracking pour accéder aux régions spatio-temporelles des vidéos ; un algorithme de calcul des relations méréotopologiques entre régions avec le référentiel RCC8 ; l’utilisation de la méthode Louvain (graph clustering) pour obtenir les événements dans le graphe ; les complexes simpliciaux pour dégager les associations d’événements. Les données obtenues ne sont pas sémantisées et révèlent la structure spatio-temporelle de l’expérience. Elles sont réutilisables par des systèmes plus complexes (interprétation ou reconnaissance) pour la gestion de l’expérience client.
Big data (BD) is one of the emerging topics in the field of information systems. This article utilized citation and co-citation analysis to explore research articles in the field of BD to examine the scientific development in the area. The research data was retrieved from the WOS database from the period between 2005 and June 2016, which consists of 366 articles. In the citation analysis, this article relies on the degree centrality and betweenness centrality for identifying 38 important papers in BD. In the co-citation analysis, a principal component factor analysis of the co-citation matrix is employed for identifying six major research themes: foundations, BD applications, techniques and technologies, challenges, adoption and impacts and literature review. This literature review is one of the first studies to examine the knowledge structure of BD research in the information systems discipline by using evidence-based analysis methods. Recommendations for future research directions in BD are provided based on the analysis and results of this study.
This review article focuses on scholarly literature pertaining to the field of the application of big data in solutions for business. With the emergence of big data as the next frontier in technological evolution, there has been a significant attempt to study how businesses may use it to improve their performance and profits. In order to conduct a search of relevant literature with regard to this, a protocol was designed. The open source website Google Scholar was used to maximise results, with a final total of 210 research works being examined before a final list of 5 were selected using a set of filters and parameters devised based on the original intention of this systematic review. The final five works that were further analysed showed that four out of five focused on improving specific aspects of businesses using big data, such as improving overall performance, customer behaviour and experience, designing business intelligence solutions, and traditional versus improved marketing. One article focused on a specific industry, i.e. healthcare. As the search methodology and parameters in this systematic review were aimed to find works that were solutions-based, many of the authors of the final five works made proposals or recommendations to industries and business managers on how big data could improve their practices or their operations in very concrete and specific ways.
Big data is an unstructured data set with a considerable volume, coming from various sources such as the internet, business organizations, etc., in various formats. Predicting consumer behavior is a core responsibility for most dealers. Market research can show consumer intentions; it can be a big order for a best-designed research project to penetrate the veil, protecting real customer motivations from closer scrutiny. Customer behavior usually focuses on customer data mining, and each model is structured at one stage to answer one query. Customer behavior prediction is a complex and unpredictable challenge. In this paper, advanced mathematical and big data analytical (BDA) methods to predict customer behavior. Predictive behavior analytics can provide modern marketers with multiple insights to optimize efforts in their strategies. This model goes beyond analyzing historical evidence and making the most knowledgeable assumptions about what will happen in the future using mathematical. Because the method is complex, it is quite straightforward for most customers. As a result, most consumer behavior models, so many variables that produce predictions that are usually quite accurate using big data. This paper attempts to develop a model of association rule mining to predict customers’ behavior, improve accuracy, and derive major consumer data patterns. The finding recommended BDA method improves Big data analytics usability in the organization (98.2%), risk management ratio (96.2%), operational cost (97.1%), customer feedback ratio (98.5%), and demand prediction ratio (95.2%).
This study examines the influence of firm cluster collaboration and the utilization of big data on the expansion of open innovation. We also investigate the effect of open innovation on business performance and test the moderating effect of social capital. We surveyed 409 global firms (193 large enterprises and 216 medium enterprises) located in Asia, Europe, North America, and Africa over a one-month period. We limited the scope of the survey to employees working at the level of team leader (or executive) or above in the R&D department. This study used SmartPLS (Version 3.3.3) to perform the statistical analysis. Our results indicated that, first, corporate cluster collaboration had a positive effect on the expansion of open innovation. Second, firm use of big data had a positive effect on the expansion of open innovation. Third, firm expansion of open innovation had a positive effect on business performance (market and financial performance). Fourth, in the relationship between cluster collaboration and open innovation expansion, social capital had a negative moderating effect. This research proved that cluster collaboration and big data could accelerate open innovation, ultimately improving business performance. However, there are limitations regarding SEM analysis.
The internet has upended how consumers engage with brands. It is transforming the economics of marketing and making obsolete many of the functionʼs traditional strategies and structures. For marketers, the old way of doing business is unsustainable. Consider this: Not long ago, a car buyer would methodically pare down the available choices until he arrived at the one that best met his criteria. A dealer would reel him in and make the sale. The buyerʼs relationship with both the dealer and the manufacturer would typically dissipate after the purchase. But today, consumers are promiscuous in their brand relationships: They connect with myriad brands—through new media channels beyond the manufacturerʼs and the retailerʼs control or even knowledge—and evaluate a shifting array of them, often expanding the pool before narrowing it. After a purchase these consumers may remain aggressively engaged, publicly promoting or assailing the products theyʼve bought, collaborating in the brandsʼ development, and challenging and shaping their meaning. Consumers still want a clear brand promise and offerings they value. What has changed is when—at what touch points—they are most open to influence, and how you can interact with them at those points. In the past, marketing strategies that put the lionʼs share of resources into building brand awareness and then opening wallets at the point of purchase worked pretty well. But touch points have changed in both number and nature, requiring a major adjustment to realign marketersʼ strategy and budgets with where consumers are actually spending their time.
It's a manager's perennial question: "How do I get an employee to do what I want?" The psychology of motivation is very complex, and what has been unraveled with any degree of assurance is small indeed. But the dismal ratio of knowledge to speculation has not dampened managers' enthusiasm for snake oil, new forms of which are constantly coming on the market, many of them with academic testimonials. The surest way of getting someone to do something is to deliver a kick in the pants-put bluntly, the KITA. Because of the inelegance of a physical KITA and the danger that a manager might get kicked in return, companies usually resort to positive KITAs, ranging from fringe benefits to employee counseling. But while a KITA might produce some change in behavior, it doesn't motivate. Frederick Herzberg, whose work influenced a generation of scholars and managers, likens motivation to an internal generator. An employee with an internal generator, he argues, needs no KITA. Achievement, recognition for achievement, the work itself, responsibility, and growth or advancement motivate people. The author cites research showing that those intrinsic factors are distinct from extrinsic, or KITA, elements that lead to job dissatisfaction, such as company administration) supervision, interpersonal relationships, working conditions, salary, status, and job security. Managers tend to believe that job content is sacrosanct. But jobs can be changed and enriched. Managers should focus on positions where people's attitudes are poor, the investment needed in industrial engineering is cost-effective, and motivation will make a difference in performance.
At the 1988 workshop we called attention to the “Mind Projection Fallacy” which is present in all fields that use probability. Here we give a more complete discussion showing why probabilities need not correspond to physical causal influences, or “propensities” affecting mass phenomena. Probability theory is far more useful if we recognize that probabilities express fundamentally logical inferences pertaining to individual cases. We note several examples of the difference this makes in real applications.
Digital television (TV) service over Internet Protocol (IP) networks is becoming a crucial element in a network provider's portfolio. However, the limitations of the existing communication networks and the complexity of the service impose many operational, performance, and scalability barriers. Analytic discussion of service quality is non-trivial because of the system complexity as well as the subjective nature of video quality assessment. This paper provides an analysis of IP video service quality metrics with a primary focus on the need for correlation of viewer perception with network performance and operation. It gives a top-down impact analysis from the user's perspective and then categorizes a set of key quality indicators (KQIs). The paper further describes a computational model, called the video quality analyzer, which both verifies and augments the top-down analysis with a bottom-up experimental realization of the KQI definitions and a refinement of the theoretical results. The discussion addresses a set of techniques for measuring video quality with respect to network performance indicators, network configuration, digital video attributes, and video content. © 2008 Alcatel-Lucent.
It's a manager's perennial question: "How do I get an employee to do what I want?" The psychology of motivation is very complex, and what has been unraveled with any degree of assurance is small indeed. But the dismal ratio of knowledge to speculation has not dampened managers' enthusiasm for snake oil, new forms of which are constantly coming on the market, many of them with academic testimonials. The surest way of getting someone to do something is to deliver a kick in the pants-put bluntly, the KITA. Because of the inelegance of a physical KITA and the danger that a manager might get kicked in return, companies usually resort to positive KITAs, ranging from fringe benefits to employee counseling. But while a KITA might produce some change in behavior, it doesn't motivate. Frederick Herzberg, whose work influenced a generation of scholars and managers, likens motivation to an internal generator. An employee with an internal generator, he argues, needs no KITA. Achievement, recognition for achievement, the work itself, responsibility, and growth or advancement motivate people. The author cites research showing that those intrinsic factors are distinct from extrinsic, or KITA, elements that lead to job dissatisfaction, such as company administration, supervision, interpersonal relationships, working conditions, salary, status, and job security. Managers tend to believe that job content is sacrosanct. But jobs can be changed and enriched. Managers should focus on positions where people's attitudes are poor, the investment needed in industrial engineering is cost-effective, and motivation will make a difference in performance.
Companies spend lots of time and money on complex tools to assess customer satisfaction. But they're measuring the wrong thing. The best predictor of top-line growth can usually be captured in a single survey question: Would you recommend this company to a friend? This finding is based on two years of research in which a variety of survey questions were tested by linking the responses with actual customer behavior--purchasing patterns and referrals--and ultimately with company growth. Surprisingly, the most effective question wasn't about customer satisfaction or even loyalty per se. In most of the industries studied, the percentage of customers enthusiastic enough about a company to refer it to a friend or colleague directly correlated with growth rates among competitors. Willingness to talk up a company or product to friends, family, and colleagues is one of the best indicators of loyalty because of the customer's sacrifice in making the recommendation. When customers act as references, they do more than indicate they've received good economic value from a company; they put their own reputations on the line. And they will risk their reputations only if they feel intense loyalty. The findings point to a new, simpler approach to customer research, one directly linked to a company's results. By substituting a single question--blunt tool though it may appear to be--for the complex black box of the customer satisfaction survey, companies can actually put consumer survey results to use and focus employees on the task of stimulating growth.
: We note the main points of history, as a framework on which to hang many background remarks concerning the nature and motivation of Bayesian/Maximum Entropy methods. Experience has shown that these are needed in order to understand recent work and problems. A more complete account of the history, with many more details and references, is given in Jaynes (1978). The following discussion is essentially nontechnical; the aim is only to convey a little introductory "feel" for our outlook, purpose, and terminology, and to alert newcomers to common pitfalls of misunderstanding. HERODOTUS 2 BERNOULLI 2 BAYES 4 LAPLACE 5 JEFFREYS 6 COX 8 SHANNON 9 COMMUNICATION DIFFICULTIES 10 IS OUR LOGIC OPEN OR CLOSED? 13 DOWNWARD ANALYSIS IN STATISTICAL MECHANICS 14 CURRENT PROBLEMS 15 REFERENCES 17 ? Presented at the Fourth Annual Workshop on Bayesian/Maximum Entropy Methods, University of Calgary, August 1984. In the Proceedings Volume, Maximum Entropy and Bayesian Methods in Applied Stat...
Market Insight: Improving CSP Customer Experience with New Monitoring Solutions
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Transforming the Brand Through Improved Customer Experience – Service Provider
- Alcatel-Lucent
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High-Tech Tuesday Webinar: Big Data Opportunities in Vertical Industries
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Customer Experience Management: A Revolutionary Approach to Connecting with Your Customers
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- Apache Software
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Getting Value from Big Data
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Insights Lead to Retention and Monetization, Yankee Group
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The Mathematics of Changing Your Mind Book Review
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Service Providers Use CEM for Competitive Advantage
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