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This study aims to analyze the capacity of ecotourists to exhibit behavior that aligns with the ecotourist scale using the Rasch model measurement.

The data was gathered using an online survey incorporating the five tenets of ecotourism using a seven-point rating scale on domestic tourists in Indonesia. Descriptive statistics, cross-tabulation and Rasch model measurement were used to analyze the data.

The ecotourist identification scale measurement items were reliable and satisfactory. The most challenging behavior for ecotourists was using the services of a tour guide who was concerned about the environment. Meanwhile, respecting cultural differences around the tourist destination was the most accessible behavior. Most respondents demonstrated a fit response pattern and satisfactorily met the validity and reliability criteria.

This study did not compare ecotourists’ ability to behave by the type of conservation visited as its limitation. However, it provides a significant methodological contribution to developing a measurement of ecotourist behavior implemented in well-established behavioral theories.

Integrating ecotourism into education, incentivizing eco-friendly tourism practices, promoting awareness, supporting local businesses, respecting local values and ensuring safe travels.

To the best of the authors’ knowledge, this study is the first of its kind to be conducted in Indonesia. It uses a unique and innovative method to reveal the unobserved variables in ecotourists’ behavior. The findings confirm that tourists’ behaviors align with the five tenets of ecotourism.

This paper aims to investigate the direct and combined impacts of entrepreneurial orientation (EO) and conservative orientation (CO) on perceived business performance (PBP) of small- and medium-sized enterprises (SMEs) under strategic-hybrid orientation (SHO) theory.

The data collected from the SABI NEO international database has 90 companies in 13 medium-to-high and high-tech activity sectors. The authors used partial least squares structural equation modelling to test the research model.

Business strategies match a SHO that includes both orientations, i.e. EO and CO. Moreover, as expected, the authors found evidence that each orientation produces performance-related sign-opposite significant impacts. Finally, the hypothesis regarding the positive synergistic effect of both orientations (EO and CO) on PBP was also supported.

One stems from the study’s cross-sectional nature, requiring a longitudinal approach. Another one resides in the absence of further examinations concerning multigroup analysis. Another restraint is the limitedness of data, focused on firms with med/high-tech intensity. For last, while the use of results in the initial stages of theory development can be beneficial, it is important to note that such results cannot be simply extrapolated or generalized to other industrial sectors without careful consideration of the contextual factors at play.

This study humbly endeavours to contribute to the finality of SMEs’ more steady and prosperous existence concerning the consciousness of the need to improve labour stability and wage fairness, conditions such as requiring a continuous commitment.

In this study, the authors aimed to investigate the impact of SHO on SMEs’ PBP. To this end, the authors simultaneously used two different strategic orientations (SOs): EO, which is widely studied in the literature, and CO, which has been less researched. The authors also examined their synergistic effects on PBP. The authors’ approach is based on Venkatraman’s strategic orientation of business enterprises model and the comparative paradigm of SOs.

The present study addresses the literature gap on cultural linkages, traditions, cultural recognition and the perceived benefits of consumers in the orange economy. The article focuses on analyzing the impact of traditions and cultural linkages on the relationship between cultural recognition and the perceived benefits of consumers.

The empirical study is conducted on a sample of 393 consumers from the city of Arequipa, Peru utilizing structural equation modeling (SEM) with Smart PLS software.

The research results demonstrate that traditions and cultural ties are important factors that entrepreneurs must consider in the production and marketing process to enhance the benefits perceived by consumers by providing cultural identity within the products.

The study provides theoretical implications that fill the existing gap in understanding the relationship between cultural recognition and perceived benefits. Additionally, it offers practical implications for the development of the cultural ecosystem, involving artisans, artists, cultural managers, entrepreneurs, businessmen, government authorities, non-governmental organizations and international organizations.

This study aims to investigate the potential positive effects of cause-related marketing (CRM) campaigns that show evidence of commitment to espoused causes. It examines whether consumers respond positively when a CRM campaign promises to deliver proof of the espoused cause.

This study adopted the grounded theory approach to conceptualize the promised impact evidence construct. A promised impact evidence scale was developed and validated using robust qualitative and quantitative methods, including item response theory estimates.

The study provides evidence for promised impact evidence as a reflective second-order latent construct. The promised impact evidence scale demonstrates strong internal consistency, reliability and validity. In addition, this study posits that promised impact evidence is an antecedent of advertising trust, purchase intention, advertising credibility and persuasive and selling intent.

This study positioned the promised impact evidence scale against the theoretical underpinnings of the persuasion knowledge model. Specifically, this scale contributes to existing knowledge because it applies the persuasion knowledge model in CRM campaigns by adopting an acceptance focus, as opposed to the rejection focus used in developing persuasion knowledge model scales.

The aim of this research was to investigate fans’ responses toward a sponsor that has had a long-standing sponsorship deal with a club and decided also to sponsor the club’s rival.

A long-term sponsorship deal between a retsina wine company and a popular football club and a newly established deal between the company and the main rival club were selected as the research setting. Data were collected from a total sample of 302 participants, fans of the two teams, using an online survey and PLS-SEM was employed to test the relationships of the proposed structural model.

The results provided evidence for the importance of the inclusion of perceptions of fit for both teams to the model as it impacted the responses in the joint sponsorship. Team identification emerged significant for improving fans perceptions of fit between the sponsor and their favorite club but also led fans of the long-term sponsored club to feel betrayed from the sponsor. The sense of betrayal impacted the level of fit, the rejection of sponsorship but did not emerge significant for driving negative responses toward the sponsor’s brand. The same held for the rejection of the joint sponsorship.

This is the very first study that incorporated the effects of the perceptions of fit of two rival clubs to test the effect of sponsorship for a sponsor brand of a deal that includes a longtime sponsored football club and its rival as a newly sponsored one. It is also one of the first attempts that explores relationships between perceptions of fit, sense of betrayal and rejection of a joint sport sponsorship in a rivalry context, highlighting the importance of preventing fans' betrayal.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

This study aims to analyze and synthesize the design features of existing digital transformation maturity models with a developed classification scheme and propose a generic maturity model development wireframe based on design science research.

A systematic literature review is conducted on digital transformation maturity models in peer-reviewed journals, including the Emerald Insight, Science Direct, Scopus, Taylor & Francis and Web of Science databases, which resulted in 21 studies. A concept-centric tabular approach is used to analyze the studies, and intersectional demonstrations are used to synthesize the findings regarding the design features.

The classification scheme derived from the tabular concept-centric approach and iteratively evolved results in three main and 25 subcategories related to the design features. Analysis and synthesis of the studies reveal the granularity of the existing digital transformation maturity models concerning the design features. Furthermore, considering the design features in the classification scheme, a generic maturity model development wireframe is proposed to guide the researchers.

The generic maturity model development wireframe and the classification scheme that represents the design features of existing maturity models guide the researchers for the maturity model development roadmap.

The existing literature review studies do not focus on the design feature of digital transformation maturity models within a systematic literature review perspective. A unique classification scheme derived from the tabular concept-centric approach aims to analyze the granularity level of the existing models. Furthermore, the generic maturity model development wireframe includes the guidelines and recommendations of design science studies and presents a roadmap for maturity model researchers.

This study assesses performance measurement (PM) system implementation efforts across various organizational contexts and investigates which factors are critical to achieving implementation success (IS).

An empirical field study was conducted to refine a framework of PM system IS that consists of 5 dimensions of success and 29 factors. A survey questionnaire was used to investigate actual organizational practice and exploratory factor analysis was conducted to refine constructs corresponding to potential factors and dimensions of IS. The resulting variables were then investigated using multiple regression analysis to identify critical success factors for implementing PM systems.

The survey was completed by representatives from 124 organizations and the exploratory factor analysis results indicated that there are three underlying dimensions of IS (i.e. Use of the System, PM System Performance, and Improved Results and Processes) and 12 factors. Of the factors, nine can be considered critical success factors having a significant relationship with at least one dimension of IS: Leader Support, Design and Implementation Approach, Reward System Alignment, Organizational Acceptance, Organizational Culture and Climate, Easy to Define Environment, IT Infrastructure Capabilities, PM System Design Quality, and PM Participation and Training.

The results show that there are distinct dimensions of IS and, although some factors are associated with all dimensions, most are more closely related to only one dimension. This suggests that different strategies should be utilized based on the types of challenges experienced during implementation.

Due to land resource scarcity, sustainable urban development in high-density cities has long been challenging. As such, many cities are formulating plans to “dig deep”, resulting in more citizens working and/or staying underground for longer periods of time. However, owing to the particularities of underground space, the factors involved in the creation of a healthy environment are different from those involved in aboveground developments. This study thus aims to investigate the influences of various underground environment factors on users' health through a holistic approach.

To achieve this aim, 12 underground sites and 12 corresponding aboveground sites are selected for a large-scale questionnaire survey, resulting in 651 survey samples. The survey covers post-occupancy evaluation of health (physical and psychosocial), underground environmental quality (visual, thermal, acoustic comfort, indoor air quality and ventilation), space design and greenery. Independent-sample T-test, Pearson correlation, multiple regression modelling and structural equation modelling are used to investigate whether significant differences exist between health of underground and aboveground users, and to develop an underground environment-health model for unveiling the significant associations between underground environment factors and users' health. To cross validate the results, an objective field measurement study is further conducted on six underground sites. The objective measurement results are used to cross validate the survey results.

The questionnaire results provide the following evidence: (1) health of underground users is significantly poorer than that of their aboveground counterparts; (2) underground development users' health is significantly affected by space design, greenery and environmental quality in terms of thermal comfort, indoor air quality, ventilation and acoustic comfort but not visual comfort; and (3) amongst the various identified factors, space design has the strongest predicting effects on human health. The field study echoes the survey findings and further unveils the relationships between different environmental factors and human health.

The results shed light on the importance of distinguishing between underground developments and aboveground ones in various guidelines and standards, especially those related to space management.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.