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The purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig.

An effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal.

Singular values of the statistical features can be effectively used for REB classification.

REB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines.

It is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.

The study emphasizes the role of an individual's mindfulness and temperance in making employees fit their organizations by comparing the direct effect of mindfulness and its indirect effect through temperance on the employees' person–organization fit (P-O fit). Data were collected from 185 Indian employees working at managerial positions in manufacturing and service industries through an online questionnaire in a cross-sectional research design. Structure equation modelling (SEM) was used to test the associations, and it was observed that mindfulness among employees is positively related to their P-O fit. Similarly, employees' temperance is also positively associated with their P-O fit. Furthermore, it is observed that temperance acts as a partial mediator between mindfulness and P-O fit. Mindful employees would be more aware of their surroundings, making them aware of the values that the workplace demands. The same awareness would compel the employees to have temperance (self-control) to keep their values in line with organizational values. The study contributes to the virtue theory and the value congruence theory in the organizational context. This study recommends that the management promotes mindfulness and temperance among the employees through various interventions and new technological aids to promote the P-O fit of the employees. To the best of our knowledge, this original work has novelty to investigate the relationship of mindfulness with P-O fit, taking into account the role of temperance of the employee.

This study focuses on the relationship between innovation and customer equity drivers and the moderating effect of advertising appeals on this relationship. First, the authors divided innovation into incremental innovation and radical innovation, and explained the influences of each type of innovation on drivers of customer equity based on literature review. Second, the authors tested the conceptual model using structural equation modeling find out the effects of innovation. Third, the authors also tested the effect of advertising appeal using moderating regression. The results indicate that both incremental innovation and radical innovation can positively influence value equity, relationship equity, and brand equity. Functional advertising appeal is more useful than emotional advertising appeal for radical innovation. On the contrary, emotional advertising appeal is more useful than functional advertising appeal for incremental innovation.

Gamification unlocks unprecedented opportunities in healthcare, wellness and lifestyle context. In this scenario, by leveraging on such an approach, information technologies now enabled gamification-based mobile applications primarily employed in health and wellness contexts, focusing on areas such as disease prevention, self-management, medication adherence and telehealth programs. The synergistic integration of gamification-based methodologies in conjunction with the utilization of digital tools, (e.g. as for Internet of Things, mobile applications) for the realm of digital therapeutics (DTx), thus unveiled powerful approaches and paradigms, yielding innovative applications that, through the harnessing of sensors and software-based systems, transform healthcare maintenance, wellness and lifestyle into an engaging pursuit, as a game. This paper explores the factors influencing individuals' intention to autonomously utilize mobile gamification-based apps for self-care and wellness maintenance.

Through explorative research designs an experiment has been conducted among a sample of 376 participants regarding the use of a fictitious gamification-based DTx solution, consisting in a mobile app namely “Health'n’Fit”.

Findings from an experiment conducted with a sample of 460 participants shed light on the possible antecedents and consequents of gamification. Results of the SEM model indicate that customization (CU), trust (TR), mobility (MO) and social value (SV) are the main determinants, although at a different extent of the playful experience; Moreover, gamification positively impacts attitudes and, in turn, perceived usefulness, intention to use and behavioral intentions.

This paper offers a dual-pronged approach that holds practical significance in the realm of healthcare innovation. First, the authors delve into the antecedents shaping individuals' intention to engage with gamification-based DTx, unraveling the factors that influence user adoption. Beyond this, the authors extend their focus to the realm of healthcare service design. By harnessing the potential of gamification and technology, the authors illuminate pathways to conceptualize and create novel healthcare services. This work not only identifies the building blocks of user engagement but also serves as a guide to innovatively craft healthcare solutions that leverage this amalgamation of technology and gamification, contributing to the evolution of modern healthcare paradigms.

In a social context, the paper introduces pioneering technological synergies that merge gamification and DTx to enhance individuals' health and wellness maintenance. By proposing innovative combinations, the authors present novel avenues for promoting healthier lifestyles and behavior change. This not only underscores the potential of technology to positively impact individuals but also highlights the significance of aligning technological advancements with societal well-being. As the research advocates for these innovative solutions, it reinforces the importance of collaborative technological and marketing endeavors, ultimately contributing to the betterment of society as a whole.

This is the first paper exploring the combined effect of gamification and DTx, by shedding light on the peculiarities of both the antecedents of individuals' intention to use such combined technologies.

Promoting the self-determination of students, particularly adolescents, with disabilities has become best practice in special education and transition services. Research documents that students who leave school as more self-determined young people achieve more positive employment and independent living outcomes and experience a higher quality of life. Further, promoting self-determination can provide an entry point to the general education curriculum for students with disabilities, and instruction to promote self-determination can enable students to better engage with and learn in the general education curriculum. This chapter defines the self-determination construct as it applies to the education of students with disabilities, examines the importance of such instruction, and provides information with regard to prevailing practices in assessment and instruction to promote this outcome.

Customer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers.

This study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including “the probability that customers will return in the future” as a key input for CLV calculation.

The CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins.

The integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings.

The purpose of this paper is to identify the drivers of customer satisfaction (CS) and sales performance at wineries in the Finger Lakes region of New York State in the context of changes winery tasting rooms implemented due to the COVID-19 pandemic.

A survey was administered to tasting room visitors at two wineries in October 2020 in the Finger Lakes region of New York State resulting in 215 usable survey responses measuring customer satisfaction. A factor analysis was used to identify primary factors influencing overall CS. The authors then modeled how these primary factors, along with various demographic factors, influence sales metrics. The results are then compared with previous estimates of such drivers in pre-COVID tasting rooms.

The authors identified four main CS factors: Staff Interactions, Wine Tasting, COVID-19 Precautions and Ambience that play a significant role in overall CS. Of these, Wine Tasting was shown to have a positive influence on total amount spent and the number of bottles purchased, whereas COVID-19 Precautions positively impacted the number of bottles purchased. Overall, CS is also shown to positively impact total amount spent and number of bottles purchased. Customers are shown to prefer some changes to the tasting room due to COVID-19, such as having table service and reservations.

This is the first study researching the influence of certain tasting room changes implemented due to the COVID-19 pandemic has had on CS and wine-purchasing decisions in tasting rooms.

Despite service downgrades' undisputed practical relevance, service downgrades (e.g. customers shifting the price tier downward) have received surprisingly little attention from scholars. Previous studies have focussed on either the public policy issue of tiered pricing or optimal pricing by the service provider. Only a few studies have examined why customers shift across different price tiers and how such activities indicate their future behaviour.

Based on customer data collected from a major telecommunications company, the authors use a logistic regression model to investigate how two service modification levers (i.e. transaction- and relationship-level factors) influence the likelihood of service downgrade. The authors apply a survival model to study how service downgrades affect customer churn.

Transaction-level factors such as service usage (e.g. the frequency and recency of underuse experiences) are positively associated with the likelihood of a downgrade. However, relationship-level factors (e.g. relationship duration and customer status) are negatively associated with the likelihood of downgrades. Customers engaging in downgrades are more likely to churn in the future.

The authors focus on downgrade behaviour, which can be perceived as customers' choice to move down the price tier, which likely ruins the service provider's performance. The authors conceptualise two fundamental driving forces behind a service downgrade: the misfits between the actual usage and the service plan chosen and the deteriorating relationships. The authors' empirical findings on the factors influencing downgrades provide insights for service providers seeking to prevent such behaviour.

The current approach to valuing customers is based on the notion of discounted profit generated by the customers over the lifetime of the relationship, also known as customer lifetime value (CLV). However, in the financial services industry, the customers who contribute the most to the profitability of a firm are also the riskiest customers. If the riskiness of a customer is not considered, firms will overestimate the true value of that customer. This paper proposes a methodology to adjust CLV for different types of risk factors and creates a comprehensive measure of risk-adjusted lifetime value (RALTV).

Using data from a major credit card company, we develop a measure of risk adjusted lifetime value (RALTV) that accounts for diverse types of customer risks. The model is estimated using Stochastic Frontier Analysis (SFA).

Major findings indicate that rewards cardholders and affinity cardholders tend to score higher within the RALTV framework than non-rewards cardholders and non-affinity cardholders, respectively. Among the four different modes of acquisition, the Internet generates the highest RALTV, followed by direct mail.

This paper not only controls for different types of consumer risks in the financial industry and creates a comprehensive risk-adjusted lifetime value (RALTV) model but also shows empirically the value of using RALTV over CLV for predicting future performance of a set of customers. Further, we investigate the impact of a firm’s acquisition and retention strategies on RALTV. The measure of risk-adjusted lifetime value is invaluable for managers in financial services.

With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction.

In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting.

Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters.

This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction.