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This study aims to improve the market competitiveness of iron and steel manufacturers in developing countries by reducing their production costs.

The research methodology relies on a case study-based approach. The study relies on six steps. The first is the preparation, then the five steps of the six-sigma – define, measure, analyze, improve, control. The qualitative and quantitative data were considered. The qualitative analysis relies on the experts’ judgment of internal status. The quantitative analysis uses the job floor data from three iron and steel manufacturers. After collecting, screening and analyzing the data, the root causes of the different wastes were identified that increase production costs. Consequently, lean manufacturing principles and tools are identified and prioritized using the decision-making trial and evaluation laboratory method, and then implemented to reduce the different types of waste.

The main wastes are related to inventory, time, quality and workforce. The lean tools were proposed with the implementation plan for the discovered root causes. The performance was monitored during and after the implementation of the lean initiatives in one of the three companies. The obtained results showed an increase in some performance indicators such as throughput (70.6%), revenue from by-products (459%), inventory turnover (54%), operation availability (45%), and plant availability (41%). On the other hand, results showed a decrease of time delay (78%), man-hour/ton (52.4%) and downgraded products (63.3%).

The current case study findings can be utilized by Iron and Steel factories at the developing countries. In addition, the proposed lean implementation methodology can be adopted for any other industries.

The current work introduces an original and practical road map to implement the lean six-sigma body of knowledge in the iron and steel manufacturers.

This work introduces an effective and practical case study-based approach to implementing the lean six-sigma body of knowledge in the iron and steel manufacturers in one of the underdevelopment countries. The consideration of the opinion of the different engineers from different sectors shows significant identification of the major problems in the manufacturing and utility sectors that lead to significant performance improvement after solving them.

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).

Future customer experiences are conceptualized within a three-dimensional space – low to high digital density, low to high physical complexity and low to high social presence – yielding eight octants.

The conceptual framework identifies eight “dualities,” or specific challenges connected with integrating digital, physical and social realms that challenge organizations to create superior customer experiences in both business-to-business and business-to-consumer markets. The eight dualities are opposing strategic options that organizations must reconcile when co-creating customer experiences under different conditions.

A review of theory demonstrates that little research has been conducted at the intersection of the digital, physical and social realms. Most studies focus on one realm, with occasional reference to another. This paper suggests an agenda for future research and gives examples of fruitful ways to study connections among the three realms rather than in a single realm.

This paper provides guidance for managers in designing and managing customer experiences that the authors believe will need to be addressed by the year 2050.

This paper discusses important societal issues, such as individual and societal needs for privacy, security and transparency. It sets out potential avenues for service innovation in these areas.

The conceptual framework integrates knowledge about customer experiences in digital, physical and social realms in a new way, with insights for future service research, managers and public policy makers.

The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts.

The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts.

At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice.

There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

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 are the opportunities that will enable firms to create and capture value though new business models.

Providing services is a proven and effective way to secure a competitive position, deliver long-term stable revenues and open up new market opportunities. However, it is also clear that some organisations are struggling to digitally transform. Therefore, the commentary provides a brief insight into how firms explore the possibilities of digital transformation and navigate these uncharted waters.

Today’s digital technologies affect the organisation outside and in, enabling the creation of new business models and transforming the customer experience. The incumbents are acutely aware that they need to transform strategically – to build new networks and value chains.

This commentary extends earlier work exploring the digital disruption within services to highlight a number of connected areas: the challenges and opportunities of digital transformation at a strategic level, as well as understanding and enhancing the customer experience and seeing how new data-driven business models can underpin service transformation.

This paper aims to monitor, evaluate and adjust the joint quality of dissimilar friction stir welded AA2024-T3 and AA7075-T6 Al alloys.

Taguchi analysis for design of experiments and ANOVA analysis were applied. Tensile test, visual inspection and macro and microstructure investigations were carried out at each welding condition. In addition, the grain size of stir zone and the value of heat input were measured.

Using Taguchi analysis, the optimum values of tool rotary speed, welding speed and axial load were 1,200 rpm, 100 mm/min and 1,300 kg, respectively, yielding the maximum tensile strength of the joints of 427 MPa. ANOVA analysis indicated that the most significant parameter on the joint strength is the tool rotary speed, followed by welding speed and axial load, with contributions of 67, 27 and 2 per cent, respectively. Best mixing between Al alloys in the stir zone with no defects was observed at moderate speeds because of proper heat input and grain size, resulting in high strength.

A relation between structure characteristics of the joint, the process parameters and the joint strength was established to control the joint quality.

This paper aims to investigate the relationship between audit partner perfectionism traits and audit quality in Egypt, emphasizing the mediating role of professional skepticism.

A mixed-methods approach was used, combining a questionnaire and scenario-based questions for audit partners with secondary data from audited financial statements. The relationships between study variables were tested using structural equation modeling.

Results denote a significant indirect effect between partner perfectionism traits and audit quality through their professional skepticism. Perfectionism has a significant positive impact on partner professional skepticism, and skepticism influences audit quality.

This study offers opportunities to enhance financial reporting quality, allowing investors to confidently allocate financial market resources. Audit firms can consider the personality traits of auditors in the selection process, team formation and designing training programs. Regulators can use these findings to consider the role of personality traits and attitudes in audit quality when developing regulations and quality assurance systems in Egypt.

To the best of the authors’ knowledge, no studies have examined the effect of partners’ perfectionism traits on professional skepticism and audit quality, especially in Egypt. By examining audit partners, who shape the tone at the top and are accountable for reputation, this study adds a novel dimension to understanding the impact of their qualities on audit outcomes. Moreover, combining survey and secondary data allows us to link these qualities with audit quality, objectively testing our hypotheses.

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.

To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample.

The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework.

A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox.

This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

This paper aims to explore the role of international quality assurance and accreditation on higher education quality improvement and competitiveness, as well as assessing the associated benefits and challenges in the Asian context with reference to Taiwan.

The paper used a qualitative approach to examine the case study of Southern Taiwan University of Science and Technology (STUST) accredited by the Association to Advance Collegiate Schools of Business (AACSB). The paper collected survey responses from service providers (i.e. STUST faculty and AACSB) and focus group discussions with students to bridge the perception-expectation gap.

The qualitative results highlighted 10 key success factors and performance indicators which were later used to build a balanced scorecard (BSC) strategy for STUST quality improvement and competitiveness. Findings show that education quality assurance (via AACSB process) can directly influence the competitive advantage (i.e. for AACSB, STUST and Students) or indirectly via education quality enhancement. The results from faculty and students are consistent with the value co-creation trend to achieve continuous quality improvement more effectively and efficiently.

This research paper is unique as the first qualitative in-depth study to discuss assurance related factors that positively or negatively affect competitiveness and quality improvement for Higher Education Institutions (HEIs) in Asia and Taiwan. The paper also contributed by designing a BSC framework and strategy-map applicable to HEIs.

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 offer a conceptual framework delineating how text-based customer service communication should be designed to increase relational personalization.

This paper presents a systematic literature review on conversation styles of conversational AI and integrates the extant research to inform the development of the proposed conceptual framework. Using social information processing theory as a theoretical lens, the authors extend the concept of relational personalization for text-based customer service communication.

The conceptual framework identifies conversation styles, whose degree of expression needs to be personalized to provide a personal touch and improve the customer experience in service. The personalization of these conversation styles depends on available psychological and individual customer knowledge, contextual factors such as the interaction and service type, as well as the freedom of communication the conversational AI or customer service agent has.

The article is the first to conduct a systematic literature review on conversation styles of conversational AI in customer service and to conceptualize critical elements of text-based customer service communication required to provide a personal touch with conversational AI. Furthermore, the authors provide managerial implications to advance customer service conversations with three types of conversational AI applications used in collaboration with customer service agents, namely conversational analytics, conversational coaching and chatbots.

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?

Twenty years of research (1999–2018) on product-service systems (PSS) produced a significant amount of scientific literature on the topic. As the PSS field is relatively new and fragmented across different disciplines, a review of the prior and relevant literature is important in order to provide the necessary framework for understanding current developments and future perspectives. This paper aims to review and organize research contributions regarding PSS. A machine-learning algorithm, namely Latent Dirichlet Allocation, has been applied to the whole literature corpus on PSS in order to understand its structure.

The adopted approach resulted in the definition of eight distinct and representative topics able to deal adequately with the multidisciplinarity of the PSS. Furthermore, a systematic review of the literature is proposed to summarize the state-of-the-art and limitations in the identified PSS research topics. Based on this critical analysis, major gaps and future research challenges are presented and discussed.

On the basis of the results of the topic landscape, the paper presents some potential research opportunities on PSSs. In particular, challenges, transversal to the eight research topics and related to recent technology trends and digital transformation, have been discussed.