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This study aims to propose and test an appraisal theory-based framework that depicts the impact of negatively valenced brand personality dimensions on brand hate development and the resultant outcomes of non-purchase intention and anti-brand actions. The study also delineates the moderation effect of Big Five consumer personality traits on brand personality-brand hate relationship.

An international sample of 370 brand haters was collected and analysed with partial least square-based structural equation modelling.

Both negatively valenced brand personality dimensions of responsibility and activity are significant predictors of brand hate in consumers, and this brand hate leads to outcomes of anti-brand actions and non-purchase intention of consumers. Moderation analysis delineates that the consumer personality trait of neuroticism significantly moderates the relationship between both brand personality dimensions and brand hate. The study also established the importance of national culture in defining consumers’ behavioural intentions.

This study provides a novel appraisal theory-based integrated framework to understand the relationship between cognition of brand personality, brand hate emotional state and behavioural actions of consumers. To the best of the authors’ knowledge, this study is the first to consider the impact of these personality dimensions on brand hate development while assessing the interaction effect of consumer and brand personality dimensions on brand hate development.

As mobile payment systems have been in constant demand and are increasing in recent years, the various stakeholders involved in the process need to be identified. The study mainly includes ease of use, perceived usefulness, facilitating conditions, social influences, adoption readiness and intention to use financial technology (FinTech) in India. This paper aims to discuss FinTech and its dynamic changes in the banking sector. This study tests the mediation influence of perceived trust on adoption readiness and intention to use FinTech in India. The proposed impact of mediation of perceived trust was significant but small on adoption readiness and intention to use FinTech.

A questionnaire survey was administered to mobile payment users, and 349 responses were collected. Structural equations are analyzed to determine the proposed variables’ direct effects on adopting them. Additionally, to examine indirect effects, the mediation test is used.

The analysis results support the proposed model and thus help explain the impact of adoption readiness, trust and intention to use digital payment. The study’s significant findings will enable mobile payment institutions to understand the variables related to the growing use of technology in customers’ contexts. The study reveals the significance of ease of use, perceived usefulness and facilitating the service condition, which means that vendors prefer to use compatible devices that are easy to use with other devices. The analysis also explores the critical influence of perceived usefulness on customer behavior, enabling software designers to encourage accessible processes and device advantages for customers.

Mobile payment systems offered by financial institutions until currently become a variety of mobile payment services due to rapidly evolving information technology. This work is a groundbreaking attempt to apply technology acceptance in recent years to the emerging framework of mobile banking systems. This study examines the adoption of mobile payment technologies by proposing an extended technology acceptance model (TAM) to capture the issues associated with adoption in India through banking users. Now customers are aware that cyberattacks on banking networks and data breaches quickly through media and social networks. Also, they know that the industry has done little to avoid or mitigate these attacks.

This study examines the adoption of mobile payment technologies by proposing an extended TAM to capture the issues associated with adoption in India through banking users.

The mechanical performances of 3D-printed parts are influenced by the manufacturing variables. Many studies experimentally evaluate the impact of the process parameters on specimens’ static and dynamic behavior with the aim of tailoring the mechanical response of the prints. However, this experimental approach is hampered by the very large number of parameters, 3D printers and materials, the development of computer simulation models being thus required. In the context, this study aims to fill a gap by experimentally investigating the influence of infill related parameters over the vibrations of 3D-printed specimens, as well as to propose and validate a parametric finite element (FE) model for the prediction of eigenfrequencies.

A generally applicable FE model is not yet available for the 3D printing technology based on the material extrusion process due to the large number of parameters settings that determine a large variability of outcomes. Hence, the idea of developing numerical simulation models that address sets of parameters and assess their impact on a certain mechanical property. For the natural frequency, the influence of the infill density and infill line width is studied in this paper. An FE script that automates the generation of the model geometry by using the considered set of parameters is developed and run. The results of the modal analysis are compared to the experimental values for validating the script.

Based on the experimental results, a linear regression between the weight of the part and the first natural frequency is established. The response surfaces indicate that the infill density is the most significant parameter of influence. The weight-frequency function is then used for the prediction of the natural frequency of specimens manufactured with other infill parameters and values, including different infill patterns.

As the malfunctions or mechanical damages can be caused by the resonant vibration of parts during use, this research develops a FE-parameterized model that evaluates and predicts the eigenfrequencies of 2D printed parts to prevent these undesirable events. The targeted functional applications are those in which 3D-printed polymer parts are used, such as drone arms or drone propellers.

This research studies the influence of process parameters on the natural frequency of 3D-printed polylactic acid specimens, a topic scarcely addressed in literature. It also proposes a new approach for the development of parameterized FE models for sets of parameters, instead of a general model, to reduce the time and resources allocated to the experimental tests. Such a model is provided in this paper for evaluating the influence of infill parameters on 3D prints eigenfrequency. The numerical model is validated for other infill settings.

Cutting tool wear is known to affect tool life, surface quality, cutting forces and production time. Micro-milling of difficult-to-cut materials like Inconel 718 leads to significant flank wear on the cutting tool. To ensure the respect of final part specifications and to study cutting forces and tool catastrophic failure, flank wear (VB) has to be controlled. This paper aims to achieve flank wear prediction during micro-milling process, which fills the void of the commercial finite element software.

Based on tool geometry structure and DEFORM finite element simulation, flank wear of the micro tool during micro-milling process is obtained. Finally, experiments of micro-milling Inconel 718 validate the accuracy of the proposed method for predicting flank wear of the micro tool during micro-milling Inconel 718.

A new prediction method for flank wear of the micro tool during micro-milling Inconel 718 based on the assumption that the wear volume can be assumed as a cone-shaped body is proposed. Compared with the existing experiment techniques for predicting tool wear during micro-milling process, the proposed method is simple to operate and is cost-effective. The existing finite element investigations on micro tool wear prediction mainly focus on micro tool axial wear depth, which affects size accuracy of machined workpiece seriously.

The research can provide significant knowledge on the usage of finite element method in predicting tool wear condition during micro-milling process. In addition, the method presented in this paper can provide support for studying the effect of tool flank wear on cutting forces during micro-milling process.

This paper aims to predict tool wear and reveal the relationship between feed per tooth and tool wear in micro-milling Inconel 718 process.

To study and solve the tool wear problem in micro-milling of Inconel 718 micro components, in this paper, the investigation of micro-milling Inconel 718 process was implemented based on DEFORM finite element simulation, and tool wear depth of micro-milling cutter acted as output.

Different from the traditional macro milling process, diameter reduction percentage and average flank wear length decreased with the increase of feed per tooth; tool wear depth decreased when the feed per tooth was less than the minimum chip thickness.

At present, research on the prediction of tool wear in micro-milling of Inconel 718 has never been publicly reported. This study is significant to reveal the relationship between cutting parameters (feed per tooth) and tool wear in micro-milling Inconel 718.

The present study aims to explore the influence of green marketing functions (green activities, corporate communication of green activities and green image) on corporate image in the hospitality sector. Further, the study tries to explore the consumer’s intention to visit the hotels practicing green marketing.

Using snowball sampling approach, a total of 220 usable responses were obtained from the consumer via an internet survey. Data were analyzed using structural equation modeling to evaluate the strength of relationship and model fit.

The findings show that green/eco-friendly activities and green image significantly influence the corporate image of the hotel, which in turn results into significant positive impact over consumer’s intention to visit the hotel. No significant influence of corporate communication of green activities on corporate image of the hotels was found, which may be because of the low level of communication of green practices by the hotels in India.

The findings offer insight to the managers regarding the impact of green marketing practices adopted by the hotels on their corporate image which in turn results into increased visit/revisit intention.

The study deals with the topic “the influence of green marketing on corporate image”, which has received scant attention by researchers. The paper attempts to provide empirical evidence in this area. The study yields several implications that can be helpful for managers while devising green marketing strategies for the hospitality sector. The study is among the initial studies exploring the influence of green marketing on corporate image in the Indian context.

The purpose of this paper is to develop an interpretive structural modeling (ISM) model to investigate the critical success factors (CSF) and the extent of CSF's influence in the implementation of lean industry 4.0 in manufacturing supply chain.

The study has been carried out with the help of the latest literature followed by brainstorming sessions with experts. The experts were the managers from the industries, assistant professors, and research scholars from academia working in this domain. Finally, a structured model is formed using ISM methodology for the analysis of the CSFs followed by matrice d'impacts croisés multiplication appliquée á un classment (MIAMAC) Analysis for the validation of the model.

The study identifies robotics, virtual and augmented reality and cloud computing as the main CSFs which are responsible to drive all the identified CSFs. However the CSF professional training and development (PTD) has been identified as the weakest driver but having the highest dependent power.

The study has included nine CSFs and the contextual relationships between the CSFs are based on the knowledge and experience of the experts, which may be biased. Moreover, the paper has covered the ISM approach, and the same thing can be validated using the fuzzy-ISM and other multi-criteria decision-making (MCDM) techniques.

This investigation of the CSFs in the lean industry 4.0 is original and the identified CSFs are the result of the literature reviews and an extensive discussion from the experts. The paper uses the complete experience of the respective experts to make this work more effective and original.

This study aims to examine the effect of dataveillance on resistance towards online payment. Using a moderated-mediation framework, the study also investigates the mediating effects of perceived privacy and security concerns and how these mediating effects are moderated by corporate credibility, consumer scepticism and consumer empowerment.

A scenario-based experimental design was performed to examine the proposed hypotheses. Analysis of covariance and PROCESS macro were used to examine the hypotheses by analysing 312 collected responses.

The results indicated the dataveillance positively affects consumer resistance towards online payment. The results also suggested that corporate credibility and consumer scepticism significantly moderates the association between dataveillance and resistance towards online payment.

The findings of this study will help online retailers to reduce consumers’ perceived privacy and security concerns, thereby reducing consumers’ resistance towards online payment.

Theoretically, the study contributes to privacy, consumer behaviour, online payment and cognitive-motivational-relational theory literature.

Amid increasing frequency of disaster across the globe, humanitarian supply chain (HSC) has gained significant attention in recent times. This work aims to contribute towards improving the decision-making capabilities of relief organisations by offering more comprehensive understanding of the critical success factors (CSFs) concerning HSC. Hence, the current work attempts to classify CSFs as cause-and-effect factors and explore their relative importance in the stated significance.

Current work takes an explorative and deductive approach. It uses literature and experts' input to identify the CSFs for HSC and to develop a structural model for assessing these factors. Intuitionistic fuzzy DEMATEL (IF-D) is employed for modelling and analysing the cause-effect linkages among the CSFs. IF-D method is chosen as it is robust to vagueness of data and small samples.

The findings indicate that “motivated and committed employees” is the most influencing causal factor followed by “IT infrastructure”, and among effect factors, “physical network” carries the most significance followed by “anticipation capabilities.”

Relief organisations and stakeholders at various levels may put more emphasis on cause group factors with more influence on most critical effect factors to build more efficient and effective HSC to execute more impactful relief programs.

Current work explores the cause–effect relationships among the CSFs concerning HSC by implementing IF-D, which can be considered as the original contribution.

The present study aims to explore the barriers to Lean Six Sigma (LSS) implementation in the healthcare sector and develop the ranking of finalized barriers using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach under a fuzzy environment.

The LSS barriers are identified through the literature review and validated by the expert's opinion and statistical analysis. A total of 124 experts were identified through the purposive sampling method for conducting this study. A questionnaire survey method is used to collect the data related to identified LSS barriers in the healthcare sector. The screened barriers are ranked through the Fuzzy DEMATEL approach.

In this study, a total of 21 barriers were identified with the help of a systematic literature review and screened 13 significant barriers by the expert opinions of healthcare personnel. The result reveals that “Lack of top management commitment and support, lack of awareness about LSS”, “resistance to culture change and inadequate resources emerges as the most critical barriers”. The prioritization of barriers facilitates the managers to make effective policies and guidelines for LSS implementation in healthcare organizations.

To avoid LSS implementation failure, the practitioners and researchers need to focus on LSS barriers as per suggested ranking more conventionally and make plans and adoption policies accordingly.

This study is unique in terms of investigation and empirical analysis of LSS implementation barriers in the healthcare sector in the Indian context. The outcomes of the present study will help the managers of healthcare organizations to make the strategies and policies for LSS implementation as per the recommended LSS barriers.