Mohamed Zaki

University of Cambridge | Cam · Department of Engineering

Contextualized in health care service, the authors contribute to service experience theory and practice in three important ways. First, by means of qualitative interviews with patients and health care providers (clinicians and professionals), the authors empirically show that digital technology (mobile application—“app” with its companion portal) c...

Customer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer service agents and conversational artificial intelligence (AI) applications can provide a personal touch and improve the customer experience in customer service. The authors off...

Purpose The purpose of this paper is to investigate how and why some service ecosystems are more resilient and, consequently, more sustainable than others during turbulent times, and how resilience can be cultivated to enable pathways to service ecosystem sustainability. Design/methodology/approach This work integrates disparate literature from mu...

Although self-service data preparation and analytics tools (SSPATs) have become increasingly popular and can provide major benefits to users, barriers associated with the technology and its usage may prevent adoption. Based on prior work on technology acceptance and related theories, this study develops a net effects model and a configurational eff...

It seems paradoxical that studies provide evidence of the advantages of off-site manufacturing, such as reduced delivery time and higher quality, while the off-site manufacturing (OSM) market share is low and the negative perception of outsiders is high. Studies of other industries highlight customer experience as a strong predictor for relevant ou...

In the past decade, we have witnessed the rise of big data analytics to a well-established phenomenon in business and academic fields. Novel opportunities appear for organizations to maximize the value from data through improved decision making, enhanced value propositions and new business models. The latter two are investigated by scholars as part...

This chapter outlines key conceptualizations of Customer Experience (CX) and commonly used ways customer experience is measured, pointing out the advantages and limitations of the methods and metrics. There is no doubt that customer experience is important to both customers and organizations. However, the ways customer experience is measured by a l...

This file contains a link to an online article that discusses the published academic paper. The online article identifies some of the key principles of interest to managers.

The need to address housing supply deficits has driven global efforts to transform the construction industry and in particular to achieve greater use of offsite manufacturing (OSM). Countries like Japan, Germany and Sweden have been more successful than others in driving greater use of OSM to build new housing. How these countries achieved such suc...

Purpose Government initiatives to improve construction have increasingly become more focused on introducing a repertoire of technologies to transform the sector. In the literature on construction industry transformation through policy-backed initiatives, how firms will respond to the demands to adopt and use innovative technologies and approaches i...

Purpose The paper attempts to address the following research questions (RQs): RQ1: What are the main research topics within PSS research? RQ2: What are future trends for PSS research? Design/methodology/approach Twenty years of research (1999-2018) on product-service systems (PSS) produced a significant amount of scientific literature on the topic....

The most common methods of tracking customer sentiments has a big blind spot: They can’t pick up on important emotional responses. As a result, qualitative surveys, like Net Promoter Score, end up missing critically important feedback. Even if they provide a positive score, customers often reveal their true thoughts and feelings in the open-ended c...

Taking an interdisciplinary approach which draws from research into strategy, service, marketing and technology, we set out to explore how manufacturing firms are approaching digital transformation and to develop a blueprint that will help firms formulate successful digitalisation strategies. Of course, the mechanics of strategy formulation – the t...

Recent economic transformations have forced companies to redefine their value propositions, increasing traditional product offerings with supplementary services—the so-called Product-Service System (PSS). Among them, the adoption of Industry 4.0 technologies is very common. However, the directions that companies are undertaking to offer new value t...

The global pandemic has enforced corporations to shift into new working patterns and design upgrades of the physical working space-from packed desks to long-term modifications design, putting well-being at the heart of workplace planning. This paper hypothesises the use of technologies to revolutionise work practices for monitoring the well-being i...

In the current era of Industry 4.0, sensor data used in connection with machine learning algorithms can help manufacturing industries to reduce costs and to predict failures in advance. This paper addresses a binary classification problem found in manufacturing engineering, which focuses on how to ensure product quality delivery and at the same tim...

Customer retention plays in a critical role in a firm’s long-term sustainability. Until recently, however, firms have tended to rely on one-dimensional survey techniques to measure their customers’ loyalty. We know from previous research that this is problematic, masking underlying dissatisfaction with a company’s products and services and that a s...

Not that long ago – in pre-digital times – a company would have a business model and strategy supported by a technology strategy. Today’s world is a lot more complicated, with technology architecture informing business strategy and business model innovation as much as the other way round. In this context, we often talk about ‘digitally-driven busin...

In the current era of Industry 4.0, sensor data used in connection with machine learning algorithms can help manufacturing industries to reduce costs and to predict failures in advance. This paper addresses a binary classification problem found in manufacturing engineering, which focuses on how to ensure product quality delivery and at the same tim...

Purpose Responding to an increasing call for a more comprehensive conceptualization of customer delight, the purpose of this paper is to expand the theory of customer delight and to examine the implications of such an expanded view for service theory and practice. Design/methodology/approach This paper presents the results of three qualitative stu...

This white paper looks at how firms can use digital platforms to develop new business models and revenue streams. To do this, it will answer the following research questions: 1 What is the customer experience paradox and what does it mean for firms? 2 How can a digital platform help solve the customer experience paradox? 3 How can digital platforms...

Customer experience (CX) has emerged as a sustainable source of competitive differentiation. Recent developments in big data analytics (BDA) have exposed possibilities to unlock customer insights for customer experience management (CXM). Research at the intersection of these two fields is scarce and there is a need for conceptual work that (1) prov...

New technologies and new business models have already changed the way organisations interact with their customers.In the not too distant future, developments such as AI, robots and virtual reality will be a completely normal part of the customer experience. However, these new ways of engaging with customers will not replace face-to-face encounters...

Understanding the uncertainty of a neural net-work's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to large numbers of parameters and data. Ensembling NNs provides an easily implementable, scal-able method for uncertainty quantification,...

The purpose of this paper is to offer a step-by-step Text Mining Analysis Roadmap (TMAR) for service researchers. We provide guidance on how to choose between alternative tools, using illustrative examples from a range of different business contexts. We provide a six staged Text Mining Analysis Roadmap on how to use text mining methods in practice....

The purpose of this paper is to discuss digital transformation and its four trajectories – digital technology, digital strategy, customer experience and data-driven business models – that could shape the next generation of services. This includes a discussion on whether both the market and organizations are all ready for the digital change and what...

Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with...

A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e.g. summing a periodic and linear kernel can capture seasonal variation with a long term trend. Despite a well-studied link between GPs and Bayesian neural networks (BNNs), the BNN analogue of this has no...

Ontologies play a key role in all aspects of information management within different application domains, providing a common, agreed and, significantly, usable representation of the domain knowledge. In the context of financial securities and trading, there is a lack of work developing an ontology for stock markets and episodes of fraud and manipul...

Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differing versions of the dataset. The variance of the ensemble's predictions is interpreted as its epistemic uncertainty. The appeal of ensembling stems from being a collection...

Contextualized in postpurchase consumption in business-to-business settings, the authors contribute to customer experience (CX) management theory and practice in three important ways. First, by offering a novel CX conceptual framework that integrates prior CX research to better understand, manage, and improve CXs—comprised of value creation element...

Understanding the uncertainty of a neural network's (NN) predictions is essential for many applications. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to the large number of parameters and data. Ensembling NNs provides a practical and scalable method for uncertainty quantification. Its...

Purpose: This article explores innovations in customer experience at the intersection of the digital, physical, and social realms. It explicitly considers experiences involving new technology-enabled services, such as digital twins and automated social presence (i.e., virtual assistants, service robots). The challenges and opportunities facing serv...

Services comprise over 60% of the gross world product, and represent the fastest-growing sector in emerging economies. On the academic side, the field of Service Science unites researchers in the search for new knowledge and solutions across academic disciplines and across individual institutions (“Service Systems”). Starting with the conference 1....

Purpose-The purpose of this paper is to explore innovations in customer experience at the intersection of the digital, physical and social realms. It explicitly considers experiences involving new technology-enabled services, such as digital twins and automated social presence (i.e. virtual assistants and service robots). Design/methodology/approac...

In recent years, the way corporates innovate has changed significantly. Going from ‘behind closed doors’ innovation to open innovation where collaboration with outsiders is encouraged, companies are in the pursuit of more effective ways to accelerate their innovation outcomes. As a result, many companies are investing to create more entrepreneurial...

IFIP WG 5.7 International Conference, APMS 2018, Seoul, Korea, August 26-30, 2018, Proceedings, Part I In recent years, the way corporates innovate has changed significantly. Going from ‘behind closed doors’ innovation to open innovation where collaboration with outsiders is encouraged, companies are in the pursuit of more effective ways to acceler...

This report highlights the intersection between architectural design research, broadly defined to include the physical space elements of organisational and commercial premises design, and sensor-enabled human behaviour research. Drawing on a multidisciplinary review of scientific literature, the authors identify six groupings at this intersection,...

Deep neural networks are a powerful technique for learning complex functions from data. However, their appeal in real-world applications can be hindered by an inability to quantify the uncertainty of predictions. In this paper, the generation of prediction intervals (PI) for quantifying uncertainty in regression tasks is considered. It is axiomatic...

Creating a strong customer experience is a strategic priority for organizations. Com-panies are leveraging new technologies such as mobile applications, social media plat-forms, virtual reality, drones and the Internet of Things to provide smart services and enable a seamless customer experience. The complexity of using these technologies within an...

Classification is a major constituent of the data mining tool kit. Well-known methods for classification are either built on the principle of logic or on statistical reasoning. For imbalanced and noisy cases, classification may however fail to deliver on basic data mining goals, i.e., identifying statistical dependencies in data. In this article, w...

Classification is a major constituent of the data mining tool kit. Well-known methods for classification are either built on the principle of logic or on statistical reasoning. For imbalanced and noisy cases, classification may however fail to deliver on basic data mining goals, i.e., identifying statistical dependencies in data. In this article, w...

The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread. However, the current justification is intuitive rather than analytical. This work proposes one minor modification to the normal ensembling methodology, which we prove allows the ensemble to perform Bayesian inference, hence converging to th...

Wearable sensor technology presents businesses with compelling opportunities to shed new light on human behavioural processes, within the broader spheres of ubiquitous computing and the internet-of-things (IoT). In B2C markets, wearable sensors are being put to use in areas such as personal health and fitness monitoring, often connected to cloud da...

While we know that seamless customers’ journeys that span several channels are important, research on how firms can best manage and (re-) configure their customers’ journeys in a flexible manner is still unclear. At the same time complexity of customer journeys as well as the speed with which both technology such as artificial intelligence (AI) app...

This book constitutes the proceedings of the 9th International Conference on Exploring Services Science, IESS 2018, held in Karlsruhe, Germany, in September 2018. The 30 papers presented in this volume were carefully reviewed and selected from 67 submissions. The book is structured in six parts, each featuring contributions describing current rese...

Manufacturing digitalisation and the growth of big data promises to foster more responsive supply chains and to close gaps between manufacturers and consumers, leading to highly-connected manufacturing operations, mass customisation and more sustainable production. There is widespread recognition that manufacturing in broad terms is entering a new...

Digitalisation and the growth of big data promise greater customisation as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasise mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with...

There is a growing recognition in industry and research that in order to realise their sustainability ambitions and leverage new enabling technologies, organisations have to innovate not only products or services but the entire business model. This is also the case within the emerging context of re-distributed manufacturing (RdM), where there is an...

Manufacturing digitalisation and the growth of big data promises to foster more responsive supply chains and to close gaps between manufacturers and consumers, leading to highly-connected manufacturing operations, mass customisation and more sustainable production. There is widespread recognition that manufacturing in broad terms is entering a new...

The theme of Redistributed Manufacturing (RdM) has gained in interest over recent years. While much research has taken place into the effects of RdM on current manufacturing models very few people have proposed new business models for this concept. The RdM studio is a new approach to business model development that will allow future users to dynami...

Purpose This research aims to better understand customer experience, as it relates to customer commitment and provides a framework for future research into the intersection of these emerging streams of research. Design/methodology/approach This research contributes to theoretical and practical perspectives on customer experience and its measuremen...

Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizat...

Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizat...

2016, © Emerald Group Publishing Limited. $\textbf{Purpose:}$ The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM a...

Purpose The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study. Design/methodology/approach To de...

Nettavisen is a Norwegian online start-up that experienced a boost after the financial crisis of 2009. Since then, the firm has been able to increase its market share and profitability through the use of highly disruptive business models, allowing the relatively small staff to outcompete powerhouse legacy-publishing companies and new media players...

In this case study the authors present the current strategy of news online start-up – Nettavisen – to survive and compete against large incumbent publishing firms and new media players, such as Facebook and Google, in a rapidly growing market for online news. Nettavisen is moving towards a data-driven business model (DDBM). In particular, the firm...

Traditional animal production techniques are usually labour intensive and driven by very slim margins. These margins are subject to variables such as meat and milk prices, growth rates of animals, governmental policy changes and seasonal changes in cereal and crop prices, coupled with the volatile risk of infectious disease resulting in livestock l...

In this paper the authors present an integrated framework that could help stimulate an organisation to become data-driven by enabling it to construct its own Data-Driven Business Model (DDBM) in coordination with the six fundamental questions for a data-driven business. There are a series of implications that may be particularly helpful to companie...

Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for ma...

This paper proposes a diagnostic framework for optimising and improving complex services in asset heavy firms. The purpose of the proposed framework is to help asset heavy organisations understand the key factors: enablers, barriers, value and benefits, and key dimensions of data necessary to optimise the delivery of their complex services. The ini...