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Although the Net Promoter Score (NPS) index is simple, NPS has weaknesses that make NPS's interpretation misleading. The main criticism is that identical index values can correspond to different levels of customer loyalty. This makes difficult to determine whether the company is improving/deteriorating in two different years. The authors describe the application of statistical tools to establish whether identical values may/may not be considered similar under statistical hypotheses.

Equal NPSs with a “similar” component composition should have a two-way table satisfying marginal homogeneity hypothesis. The authors compare the marginals using a cumulative marginal logit model that assumes a proportional odds structure: the model has the same effect for each logit. Marginal homogeneity corresponds to null effect. If the marginal homogeneity hypothesis is rejected, the cumulative odds ratio becomes a tool for measuring the proportionality between the odds.

The authors propose an algorithm that helps managers in their decision-making process. The authors' methodology provides a statistical tool to recognize customer base compositions. The authors suggest a statistical test of the marginal distribution homogeneity of the table representing the index compositions at two times. Through the calculation of cumulative odds ratios, the authors discriminate against the hypothesis of equality of the NPS.

The authors' contribution provides a statistical alternative that can be easily implemented by business operators to fill the known shortcomings of the index in the customer satisfaction's context. This paper confirms that although a single number summarizes and communicates a complex situation very quickly, the number is ambiguous and unreliable if not accompanied by other tools.

The purpose of this study is to present and explain a new customer segmentation approach inspired by failure mode and effect analysis (FMEA) which can help classify customers into more accurate segments.

The present study offers a look at the three most commonly used approaches to assessing customer loyalty:net promoter score, loyalty ladder and loyalty matrix. A survey on the quality of restaurant services compares the results of categorizing customers according to these three most frequently used approaches.

A new way of categorizing customers through loyalty priority number (LPN) is proposed. LPN was designed as a major segmentation criterion consisting of customer loyalty rate, frequency of purchase of products or services and value of purchases. Using the proposed approach allows to categorize customers into four more comprehensive groups: random, bronze, silver and gold – according to their loyalty and value to the organization.

Survey will bring a more accurate way of categorizing customers even in those sectors where transaction data are not available. More accurate customer categorization will enable organizations to use targeting tools more effectively and improve product positioning.

The most commonly used categorization approaches such as net promoter score, loyalty ladder or loyalty matrix offer relatively general information about customer groups. The present study combines the benefits of these approaches with the principles of FMEA. The case study not only made it possible to offer a view of the real application of the proposed approach but also made it possible to make a uniform comparison of the accuracy of customer categorization.

In the transformed information environment, the impact and value of the services are not adequately shown using the traditional library metrics. It needs to be supplemented with user-centered ways of measurement. The paper aims to discuss these issues.

The paper is a case study of the new Finna service and the measurement challenges it presents.

The standards guiding the measurement and evaluation of libraries cannot offer a “cook-book” for the organizations to follow. The paper suggests that as a one possible response to this, the Net Promoter Score can be used as one indicator in measuring the impact of new services.

The findings of the paper are preliminary, because so far there is not a wide experience of the use of NPS in libraries. This calls for further study. The results are encouraging, but more testing is needed with different services.

NPS has not been widely used in libraries before.

As educational institutes began to address the challenges posed by COVID-19, e-learning came to the foreground as the best bet left. This study is in quest of revealing engineering student's perceptions of the available e-learning platforms, thus surfacing the underlying bottlenecks. Further, it aims at providing solutions that would help enhance the e-learning experience not only in pandemic times but also in the long run.

This holistic research begins with a comprehensive comparative study about the available e-learning platforms, followed by a primary data analysis through an online survey of 364 engineering students from various colleges and branches. The collected data was analyzed to detect bottlenecks in online learning and suggestions are given for solving some challenges.

On a five-point Likert scale, the available e-learning platforms garnered ratings ranging from 2.81 to 3.46. Google meet was the most preferred platform. However, with a net promoter score (NPS) of 30.36, Microsoft Teams emerged as the most satisfying platform. Technical shortcomings clubbed with psychological and biological factors were found to be taking a toll on e-learning.

This innovative research is based on the perceptions of engineering students hailing majorly from Indian cities, and hence, it may be having educational stream bias and geographical bias. The research could be further extended to cover rural areas and global trends in e-learning.

The research offers a thorough analysis of e-learning platforms, as seen through the lens of engineering students. Furthermore, the analysis does not constrain itself to the technicalities and thus proves to be an all-encompassing one, potent enough to surface critical issues marring the e-learning experience.

Offline retail stores have been working on improving their in-store customer experience; they have begun to realise the physical advantage they have over online channels. Especially sports products have a number of unique features, such as high emotional involvement or a sense of community; additionally, sports customers put emphasis on multisensory brand experience at the point of sale. This study examines the in-store customer experience (ISCX) in offline sports retail stores, taking into account the commercial uniqueness of sport.

A qualitative study (focus groups; n = 16) and quantitative survey (cross-sectional survey design; n = 238) were conducted to measure ISCX in sports retail stores.

The results suggest that the customers' in-store experience has a significant influence on customers' satisfaction with the sports retailer and their likeliness to recommend the store to friends, which, in turn, is significantly affected by customers' satisfaction with the retailer. Moreover, social responses to actors involved in the service encounter, for example, the interaction with employees, play a significant role for the customer in-store experience. Accordingly, sports customers strive not only for functional benefits inherent in the interaction with customers and employees but also for social benefits.

This study extends the knowledge by (1) replicating the ISCX scale, (2) analysing ISCX in a sports retail environment and (3) examining the influence of ISCX on the Net Promoter Score. Moreover, the findings support managers' know-how about in-store setting and help to maintain the customer relationship.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

The purpose of this paper is to present an overview of the development process and outcomes from a six-year collaboration between Halifax Bank (part of the Lloyds Banking Group) and Middlesex University between 2010 and 2016 in the UK. The collaboration involved the construction of work-integrated higher education programmes that were, from the outset, predicated on clear return on investment criteria for the Bank. One unexpected outcome from the collaboration was the emergence of critical reflection as a valued business benefit that, it is argued, has the potential for significant cultural change within the organisation.

This case study discuses how “productive reflection” can lead to an integrated approach to organisational learning. The study is located in the context of Halifax’s specific organisational objectives established following the banking crash of 2008. Quantitative and qualitative evidence is considered to illustrate the extent to which the “return on investment” criteria established by Halifax have been achieved.

The case study indicates that the challenging business context of the financial crash of 2008 provided the impetus for a sustained collaborative development that allowed the potential pitfalls of restricted learning opportunities to be addressed resulting in an integrated approach to organisational learning. In addition to the organisation’s return on investment criteria being met, there is evidence that the work-integrated approach has raised the prospect of productive reflection becoming part of an emerging learning culture.

The scale and sustained period of the university-business collaboration is unique and provides valuable insight into how an organisation’s learning culture can be affected by a work-integrated approach. In demonstrating the perceived business value of productive reflection, the case presented illustrates how learning can start to become considered as a normal aspect of working life.

Based on the assumption of the service-dominant logic (S-D logic) that every exchange is service-for-service and on the relevance of the beneficiary’s role in the co-creation of value, this paper aims to investigate the effects of engagement in the context of social marketing, where the value proposition is an invitation to practice mindfulness.

A field experiment was carried out with 72 volunteers, using a pre-test/post-test control group design. The treatment applied was a set of strategies to increase the engagement of the participants to attain a better result in five dependent variables associated mainly with the benefits of mindfulness practice. Measurements were made from a profile analysis, and submitted to Mann-Whitney and t-tests.

A large effect of group and time factors were observed in the multivariate test, as well as differences in the co-creation of value between groups.

This study can contribute to stimulate experimental transdisciplinary research in humans, using concepts from S-D logic and social marketing to promote positive behavioral change. This approach is probably more efficient at explaining and improving human behavior, given its complex nature.

Globalization and the COVID (post) pandemic continue to pose significant challenges to managing employee performance across geographic locations because there is a need to simultaneously implement procedures that are standardized and yet applicable to different contexts. This study aims to describe five universal principles in performance management that can be adapted to specific contexts to address the performance management standardization vs adaptation dilemma.

Critical literature review of evidence-based recommendations for practice.

This study describe five universal principles in performance management, how they can be adapted to specific contexts around the world and actions that organizations can take to implement them: (1) cultural congruence, (2) strategic congruence, (3) performance evaluation thoroughness, (4) inclusiveness and (5) effective feedback.

This study provide valuable and actionable knowledge for organizations facing performance management challenges around the world.

Human-related risks are practices in a given organization that lead to harmful behaviors that prevent managers and their teams from achieving goals. The purpose of this article is to enable the organization to provide a preventive and simple response to risks in the event that deterioration in employee well-being is detected.

In the literature, many questionnaires based on a variety of metrics have been developed and tested to measure and assess the quality of work life (e.g. stress, commitment, satisfaction, etc.). The approach of this study was to identify the most meaningful items and combine them into a unique score integrated into an effective decision-making module.

A long process of trial and error was necessary to collect confidential information from employees, both anonymously and longitudinally, to measure well-being in the workplace objectively and globally. The unique score generated provides an indication of potential human risk.

This research and its practical implementation have demonstrated the importance of personal-data protection and the need to work harder to maintain employees' digital trust while using a digitized tool.

Development of a new app that was used for the first time to regularly assess ill-being in several companies.

The social implication of this research is to contribute to health policies related to well-being in the workplace.

To the authors’ knowledge, this is the first time that a software module measuring the human risk of an entire company has been embedded in Enterprise Risk Management (ERM).