Collaborative Performance Research Articles

The Impact of Relationship Intelligence on Team-based Practice: An Application for the Private Sector in Mainland China

This article aims to examine the impact of developing and applying relationship intelligence in team-based practice to provide a clear understanding of the role relationship intelligence plays in team productivity in mainland China. Relationship intelligence, a component of emotional intelligence, focuses on interpersonal interactions that are crucial in team-building but receives less attention. Exploratory qualitative research was undertaken, with emotional intelligence as the theoretical framework and the input-process-output criteria indicating group effectiveness. Data collection was based on semi-structured interviews with managers and staff of the private sector based in mainland China after utilizing the Strengths Deployment Inventory 2.0 as a learning tool. The findings identify three ways in which the integration of relationship intelligence positively affected team practice: high overall performance, a positive work climate, and improved attitudes toward coworkers and work-related tasks. The positive outcomes provide researchers and practitioners with valuable information on how relationship intelligence might be leveraged to enhance communication, collaboration, and overall team performance.

Human-in-the-Loop Cooperative Control of a Walking Exoskeleton for Following Time-Variable Human Intention.

This article presents a human-in-the-loop cooperative control of a walking exoskeleton to provide assistance to the user and enhance human mobility. First, a dynamic mathematical model of the human-exoskeleton system is derived, and then, a human-in-the-loop cooperative control framework is proposed in two ways: separable cooperative control (SCC) and interactive cooperative control (ICC), respectively. The SCC introduces a space division of the human and the exoskeleton, while the ICC allows the robot to perceive the human intention and follows human motor, thereby improving the collaborative performance. The ICC formulates the impedance connection between the human and the exoskeleton in the divided orthogonal subspaces of walking, such that the robot is able to modify its position of center of mass (COM) when its motor trajectory deviates the one of the human. In addition, a novel adaptation control is proposed to deal with the unmodeled dynamics and trajectory tracking. Finally, to validate the effectiveness of our proposed controller, a series of experiments are conducted in three adults in gait at different speeds. It shows that the proposed controller can preserve the periodic walking gait and inherit the robustness of dealing with perturbations during walking.

The Influence of AI-CRM Adoption and Big Data Analytical Capability on Firm Performance of Large Enterprises in Thailand

Purpose: This research explores the impact of adopting AI-CRM on integration capability, team collaboration, and firm performance. Additionally, it investigates whether big data analytical capability significantly influences integration capability, team collaboration, and competitive advantage. The study delves into whether integration capability is a driver of team collaboration and assesses the influence of competitive advantage on firm performance. Design/methodology/approach: A quantitative approach was employed using a questionnaire as the primary tool. Online Google forms were distributed to 450 middle-high level management professionals across ten large enterprises in Thailand that have implemented AI-CRM and big data analytics. Sampling techniques included judgmental, convenience, and snowball sampling. Data analysis utilized Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to validate model fit and test hypotheses. Findings: The study revealed that the adoption of AI-CRM significantly impacts integration capability, team collaboration, and firm performance. Big data analytical capability has a significant influence on team collaboration and competitive advantage but lacks a significant effect on integration capability. Furthermore, integration capability is significantly associated with team collaboration. However, the study did not find support for the relationship between competitive advantage and firm performance. Research limitations/implications: Findings are specific to the 450 middle-high level managers in ten large Thai enterprises; caution is needed when applying results to a broader population. Furthermore, the study focused on key factors, but additional variables may influence relationships. Future research could explore a broader range of factors. The study did not deeply investigate potential mediating mechanisms between variables. Further research could explore detailed pathways connecting studied variables. Originality/value: The study provides actionable insights for businesses, particularly those in Thailand, that have implemented AI-CRM and big data analytics. Practical implications may guide strategic decisions to enhance integration, collaboration, and overall performance.

인간-AI 공동창작 공연예술 특성 연구

With the advent of the AI era, various artistic experiments utilizing generative AI are actively unfolding in the art world. In the performing arts industry, performances co-created and performed by humans and AI are being announced one after another. Consequently, there is a growing interest and debate on whether AI can be considered an equal artistic creator alongside humans and whether AI can replace human creativity. This study aims to examine the artistry, potential, and validity of AI performing art by identifying the characteristics of human-AI collaborative creation performing art based on the premise that AI performing art is the result of collaboration between humans and AI. To achieve this, we will first briefly examine fundamental concepts to understand artificial intelligence and artificial creativity. Following that, we will briefly review trends and creative processes in domestic human-AI collaborative performance arts, divided into dance <Beyond Black>, music <Sangmyung AI+X Project I AI Concert>, and theatre <Paphos 2.0>, among other examples from various performing arts fields. Lastly, we will examine the characteristics of contemporary human-AI collaborative performance arts from the perspectives of existence and aura.

Balancing Humans and Machines: A Study on Integration Scale and Its Impact on Collaborative Performance

In the evolving artificial intelligence domain, hybrid human-machine systems have emerged as a transformative research area. While many studies have concentrated on individual human-machine interactions, there is a lack of focus on multi-human and multi-machine dynamics. This paper delves into these nuances by introducing a novel statistical framework that discerns integration accuracy in terms of precision and diversity. Empirical studies reveal that performance surges consistently with scale, either in human or machine settings. However, hybrid systems present complexities. Their performance is intricately tied to the human-to-machine ratio. Interestingly, as the scale expands, integration performance growth isn't limitless. It reaches a threshold influenced by model diversity. This introduces a pivotal `knee point', signifying the optimal balance between performance and scale. This knowledge is vital for resource allocation in practical applications. Grounded in rigorous evaluations using public datasets, our findings emphasize the framework's robustness in refining integrated systems.

Empowering collaborative knowledge construction through the implementation of a collaborative argument map tool

Collaborative knowledge construction has been used in higher education to support student groups' collaborative learning activities through students' exchange, negotiation, and reflection of perspectives through peer communications. To support this process, collaborative learning analytics tools have been designed to collect and analyze collaborative process and performance data with a goal to provide actionable feedback and improve learning quality. However, few tools have demonstrated the mechanism and details about how students develop their perspectives during the collaborative knowledge construction process in higher education. To fill this gap, this research proposed a tool named Collaborative Argument Map (CAM) that creatively visualized different types of perspectives students proposed from the individual, peer, and group levels. This tool was further implemented in a graduate-level course in online collaborative writing activities in China's higher education, with a goal to support students' knowledge construction with peers. The summative and process-oriented learning analytics approaches were conducted to reveal the effects of CAM on students' collaborative perceptions, processes, and final products. Results showed that most students made substantive use of the CAM tool and reported positive perceptions of the tool. Further examinations verified the tool's positive effects on improving the students' cognitive engagement levels and the quality of their final collaborative writing products. This research provided practical implications for future CLA tool design and instructional implications for using this type of tool in collaborative learning in higher education.

An auto-weighted enhanced horizontal collaborative fuzzy clustering algorithm with knowledge adaption mechanism

Among the multi-source data clustering tasks, there is a kind of frequently encountered tasks where only one of the multi-source datasets is available for sake of privacy and other reasons. The only available dataset is called local dataset, and the other are called external datasets. The horizontal collaborative fuzzy clustering (HCFC) model is a typical one that can deal with such clustering tasks. In HCFC, each external dataset is used through the knowledge mined from it rather than itself. The knowledge expressed as a knowledge partition matrix is fused into the clustering process of the local dataset. Reviewing the existing HCFC models, we can find three issues that need improvement. Firstly, the existing HCFC models quantify the collaboration contribution of each external knowledge by a hyperparameter at dataset-level, and moreover, do not distinguish the collaboration contributions of objects in the same external dataset. This may lead to counterintuitive clustering results. Focused on this issue, this paper proposes an enhanced HCFC (EHCFC) algorithm that extends the collaboration from dataset-level to object-level, and assigns different weights to objects based on the information amount provided by objects. Through EHCFC, a more flexible collaboration and a more intuitive clustering result can be reached. Secondly, the collaboration mechanisms of the existing HCFC models require that the dimensionalities of the partition matrices of external datasets and local dataset are the same, which makes the HCFC algorithms unable to work in many real situations. Focused on this limitation, a knowledge adaption mechanism based on relative entropy and spectral clustering is proposed resulting in a further refined EHCFC-KA algorithm, i.e., EHCFC with knowledge adaption. The proposed knowledge adaption mechanism makes both the HCFC algorithms and the EHCFC algorithm effective and successful in more application scenarios. Finally, we define two indexes in terms of consistency (the consistency of the clustering result with external knowledge) to evaluate the performance of collaborative clustering. Experiments on synthetic datasets and UCI public datasets demonstrate that the proposed EHCFC and EHCFC-KA algorithms outperform the existing HCFC algorithms and achieve significantly better intuitive collaborative clustering performance.

Improved control-oriented polarization characteristic modeling for proton exchange membrane water electrolyzer with adaptive hunting game based metaheuristic optimization

The proton exchange membrane water electrolyzer has garnered increasing attention for the large-scale consumption of intermittent renewable energy to produce high purity “green hydrogen”, contributing to the realization of futural carbon neutrality goals. The necessity of control-oriented polarization characteristic modeling with accurate parameter identification remains a significant challenge for the performance evaluation of this type of water electrolyzer. In this paper, an improved control-oriented polarization characteristic modeling for proton exchange membrane water electrolyzer with adaptive hunting game based metaheuristic optimization is proposed to foster collaborative performance enhancement between model improvement and optimizer investigation. The inspiration behind model improvement lies in the nonlinear mechanism linkage between partial pressures of liquid water supplying and anode gas separator. Then, an adaptive hunting game based metaheuristic optimizer is proposed to address the inherent local multi-extremum distribution problem in the established model. Experimental data from a commercial water electrolyzer system is used to validate the effectiveness and practicability of the proposed model-optimizer pair. Detailed comparisons reveal that the improved model development, with its consideration of critical mechanisms, ensures enhanced metaheuristic efficiency in the designed optimizer, both in terms of robust intensity evaluation and efficient quasi-Newton based local search. Statistically, the collaboration performance enhancement reaches up to 7.33% when compared with the other model-optimizer.

Exploring Organology and Learning Techniques of Dongxiao Chinese Folk Musical Instruments in Shandong Province

Back ground and Aims: This study explores the organology and learning skills of Dongxiao Chinese folk musical instruments in Shandong Province, a traditional Chinese folk musical instrument with deep historical and cultural significance, particularly in Shandong Province. The aim is to provide a comprehensive understanding of Dongxiao music culture, ensuring its preservation and development alongside contemporary music cultures in Asia. Materials and Methods: The study is conducted through fieldwork in Heze, Shandong, China, involving data collection, identification of key informants, and systematic data collation. Observations and interviews are used as research tools to gain insights into Dongxiao materials, organology, and playing techniques. Data analysis includes quantitative and qualitative methods to identify patterns and themes related to Dongxiao. Results: The study reveals the organology of Dongxiao, highlighting materials such as bamboo, hardwood, jade, metal, and PVC, along with their advantages and disadvantages. It categorizes the instrument's components, including the mouthpiece, body, finger holes, and barrel holes, elucidating their roles in sound production. Additionally, the study explores the learning techniques for Dongxiao, emphasizing playing postures, finger hole techniques, and lip positioning. Conclusion: Dongxiao, with its rich history and cultural significance, plays a vital role in traditional Chinese music. Understanding its organology and learning techniques is essential for its preservation and promotion. Recommendations include the establishment of music education centers, digital learning platforms, collaborative performances, music festivals, and documentation efforts. Further study avenues include acoustical analysis, pedagogical approaches, regional variations, cross-cultural studies, contemporary compositions, and exploring the psychological and health benefits of Dongxiao.

The cooperative performance of iodo and copper in a Zr-based UiO-67 metal-organic framework for highly selective photocatalytic CO2 reduction to methanol

Photocatalytic CO2 reduction (PCR) to fuels using metal-organic frameworks (MOFs) offers promising solutions for both enhancing energy supply and mitigating global warming. However, research addressing the selectivity towards MeOH production, the role of CO2 uptake in productivity, and the identification of intermediates remains limited. Herein, we present an iodo/copper functionalization strategy for the Zr-based UiO-67 MOF. The resulting bifunctional Cu/I2-Zr-BPDC/BPyDC exhibited remarkable selectivity in light-driven CO2 reduction to MeOH, revealing the first investigation of halogen bonding's effect on PCR. Crucially, Cu/I2-Zr-BPDC/BPyDC surpassed not only pristine Zr-BPyDC, I2-Zr-BPDC, and Cu-Zr-BPyDC, but also boasted the best catalytic performance among reported MOFs. Furthermore, Cu/I2-Zr-BPDC/BPyDC demonstrated a CO2 uptake capacity of 48.6 mg g−1, 1.7 times greater than non-modified Zr-BPyDC, suggesting a direct correlation between CO2 adsorption efficiency and CO2 conversion. In addition to assigning CuII ↔ CuI catalytic cycle, collaborative performance of I and Cu functions in CO2 activation was traced by infrared spectra that elucidate the CI…OCO interaction and Cu…CO2/CuCO2H complex formation. This work provides valuable insights for engineering selective and high-performance PCR catalyst through the rational incorporation of functionalities within MOFs.

Relationship between pesticide research output and GDP: a socio-economic analysis

Purpose The focus of this study was to evaluate the relationship between research publications in the pesticide field, a country’s gross domestic product (GDP) and GDP per capita. The study aims to analyze pesticide use in association with a country’s population and research publications. The purpose of this study is to uncover the country’s contribution to pesticide research and assess the financial resources allocated to it as a percentage of their GDP by exploring these factors. Design/methodology/approach The Web of Science database was used to retrieve data for the period of 2001–2020. The use of scientometric indicators allowed for the analysis of the collaborative patterns and active performance of countries in pesticide research. Socio-economic analysis was used to determine the contribution of countries toward pesticide research. Findings This study demonstrated a strong association (0.952%) between a country’s GDP and its research publications in the field of pesticide research. Countries, such as Denmark, Belgium and Australia, have benefited from global collaboration, which has enhanced their research efforts. Despite ranking lower in pesticide utilization, India focused on pesticide research, as indicated by its high publication/GDP per capita ratio (0.26). Originality/value Research on pesticides directly impacts agricultural practices, which, in turn, influence the economic production of the agricultural sector. Changes in pesticide usage can have inference for crop yields, food price and, eventually, the GDP. Comparative analysis can assist in evaluating the efficiency of regulatory policies in balancing ecological concerns with economic interests. Changes in regulations may impact both pesticide usage and economic outcomes.

The influence of Mn content on Fe2B properties and its impact at the [formula omitted] /Fe2B interface

Fe–B alloy is widely used in the field of wear resistance, and the collaborative performance of their precipitated phases and the matrix is of significant interest. This study utilizes first-principles calculations to investigate the influence of Mn on the thermodynamic and mechanical properties of the Fe2B precipitated phase within Fe–B alloys, as well as the role of Mn in regulating the interface between the matrix αz-Fe16C2 and the Fe2B precipitated phase. The computational results reveal that the formation enthalpy remains negative at varying Mn concentrations. The Poisson's ratio of Fe2B is measured at 0.2245. However, under Mn doping, the Poisson's ratio varies with Mn content, with Fe3Mn5B4 exhibiting the highest Poisson's ratio, reaching 0.3157, representing a remarkable 40.62% increase. Interface performance calculations indicate that the interface energy of αz-Fe16C2/Fe2B is 0.4172 eV/Å2, with an adhesive energy of −0.1984 eV/Å2. Upon Mn doping at the interface, the doped interface energy decreases to −3.5393 eV/Å2, and the doped adhesive energy increases to 1.6975 eV/Å2, significantly enhancing interface binding. In conclusion, Mn has the capacity to improve the ductility of Fe2B and enhance the interface binding between the matrix and the precipitates, consequently increasing the wear resistance of Fe–B alloys.

Performance evaluation of Chronic Kidney Disease Epidemiology Collaboration equations for estimated glomerular filtration rate compared to inulin clearance in Koreans.

A race-free glomerular filtration rate (GFR) estimation equation has recently been developed. However, the performance of the new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations needs to be evaluated in Asian populations. We performed a cross-sectional study at a single center in South Korea. The measured GFR (mGFR) was determined based on systemic inulin clearance. The GFR was estimated using the five CKD-EPI equations: 2009 CKD-EPIcr, 2012 CKD-EPIcr-cys, 2012 CKD-EPIcys, 2021 CKD-EPIcr, and 2021 CKD-EPIcr-cys. The performances of five estimated GFR (eGFR) equations were assessed by bias, precision, and accuracy (percentage of estimates within 30% of mGFR). The median mGFR and interquartile range (IQR) was 53.5 (32.4-80.0) mL/min/1.73 m2. The mGFR better correlated with 2009 CKD-EPIcr (ρ = 0.628) and 2021 CKD-EPIcr-cys (ρ = 0.806) than with 2021 CKD-EPIcr (ρ = 0.623) and 2012 CKD-EPIcr-cys (ρ = 0.801). The median bias of 2009 CKD-EPIcr and 2012 CKD-EPIcr-cys were lower than those of 2021 CKD-EPI equations (2009 CKD-EPIcr, 2.24 [IQR, -8.83 to 17.39] vs. 2021 CKD-EPIcr, 5.40 [IQR, -6.04 to 20.40]; 2012 CKD-EPIcr-cys, 6.74 [IQR, -2.81 to 20.80] vs. 2021 CKD-EPIcr-cys, 10.54 [IQR, 0.30-24.37]; all in mL/min/1.73 m2). The percentage of eGFR values within 30% of mGFR was higher in 2009 CKD-EPIcr and 2012 CKD-EPIcr-cys equations than 2021 CKD-EPI equations. The CKD prevalence in 2009 CKD-EPIcr, 2021 CKD-EPIcr, 2012 CKD-EPIcr-cys, and 2021 CKD-EPIcr-cys was 54.8%, 51.0%, 47.7%, and 44.8%, respectively. Our study demonstrated better performance of the original CKD-EPIcr and CKD-EPIcr-cys equations than the 2021 new CKD-EPI equations. We do not recommend the adoption of the new CKD-EPI equations in Korea.

Collaboration of Heterogeneous Edge Computing Paradigms: How to Fill the Gap Between Theory and Practice

Edge computing paradigms such as cloudlet, mobile edge computing (MEC) and fog computing, are promising to reduce network transmission delay while meeting intensive computing requirements at the network edge. As always assumed in theoretical studies, to expand service capability, it is desirable to integrate edge resources into a unified resource pool and make different edge facilities collaborate. However, this collaboration is difficult in practice since the heterogeneity among paradigms leads to resource isolation. This article focuses on clarifying and shielding heterogeneity among edge computing paradigms for collaboration. First, based on cloudlet, mobile edge computing and fog computing, three main edge computing paradigms, i.e., small cloudy edge paradigm (SmallC), mobile integrated edge paradigm (MobInt) and front ad hoc edge paradigm (FrontAH), are defined. So that the heterogeneity among them can be analyzed, according to which, collaboration challenges in computing resource trading, information communication and resource sharing are identified. Promising techniques such as blockchain-based smart contract, media-independent handover (MIH), and software-defined network and network function virtualization (SDN/NFV) are then introduced and integrated into a complete collaboration solution. Finally, taking the unmanned vehicular navigation as an example, the performance of collaboration is evaluated, considering additional costs introduced by collaborations, including trading, network handoff and data transmission among edge facilities in different paradigms. Simulation results show that compared with existing schemes of the three paradigms, the collaboration can reduce the cost (i.e. weighted sum of energy consumption and delay) by at least 45.6%.

Single-Trial EEG Classification Using Spatio-Temporal Weighting and Correlation Analysis for RSVP-Based Collaborative Brain Computer Interface.

Since single brain computer interface (BCI) is limited in performance, it is necessary to develop collaborative BCI (cBCI) systems which integrate multi-user electroencephalogram (EEG) information to improve system performance. However, there are still some challenges in cBCI systems, including effective discriminant feature extraction of multi-user EEG data, fusion algorithms, time reduction of system calibration, etc. Methods: This study proposed an event-related potential (ERP) feature extraction and classification algorithm of spatio-temporal weighting and correlation analysis (STC) to improve the performance of cBCI systems. The proposed STC algorithm consisted of three modules. First, source extraction and interval modeling were used to overcome the problem of inter-trial variability. Second, spatio-temporal weighting and temporal projection were utilized to extract effective discriminant features for multi-user information fusion and cross-session transfer. Third, correlation analysis was conducted to match target/non-target templates for classification of multi-user and cross-session datasets. The collaborative cross-session datasets of rapid serial visual presentation (RSVP) from 14 subjects were used to evaluate the performance of the EEG classification algorithm. For single-user/collaborative EEG classification of within-session and cross-session datasets, STC had significantly higher performance than the existing state-of-the-art machine learning algorithms. It was demonstrated that STC was effective to improve the classification performance of multi-user collaboration and cross-session transfer for RSVP-based BCI systems, and was helpful to reduce the system calibration time.

The impacts of environmental collaboration on the environmental performance of agri-food supply chains: a mediation-moderation analysis of external pressures

ABSTRACT Although researchers have investigated the relationship between inter-organisational collaboration and firm performance, there is scattered evidence on the relationship between environmental collaboration and environmental performance in agri-food supply chains. This study assesses the effects of environmental collaboration on five dimensions of environmental performance (greenhouse gas emissions management, energy management, food waste management, food safety management, and water footprint management) in agri-food supply chains, particularly when mediated and moderated by customer and regulatory pressures. This study used a survey of Chilean agri-food companies. The data were analyzed using partial least squares structural equation modelling. We found that environmental collaboration positively influences all five dimensions of environmental performance and that customer and regulatory pressures mediate this relationship. The dimension most affected by environmental collaboration was water footprint management. Finally, we provide recommendations for improving the environmental performance of companies in agri-food supply chains through collaboration.

External drivers of inter-organizational collaboration in megaproject: a perspective based on project governance

PurposeInter-organizational collaboration is the organizational guarantee and key link to achieve the goals of megaproject management. Project governance has always played an important role in the construction of megaprojects, but the relationship between project governance and organizational collaboration is unclear. The purpose of this study is to explore the role paths of different project governance mechanisms in influencing the collaborative behaviors of stakeholders and collaborative performance and to elucidate the mechanism of project governance on inter-organizational collaboration.Design/methodology/approachA conceptual framework was developed based on a comprehensive literature review, termed the structural equation model (SEM). The hypotheses of the model were tested based on data obtained from a questionnaire survey of 235 experts with experience in megaprojects within the construction industry in China.FindingsThe results show that project governance positively contributes to the collaborative behavior of megaproject stakeholders and the collaborative performance of the project team. Collaborative behavior acts as a partial mediator between project governance and the collaborative performance of the megaproject inter-organization alliance. The complexity of the project modulates the relationship between the governance mechanism of the project and the collaborative behavior of the stakeholders, which affects the collaborative performance of the megaproject inter-organization alliance.Originality/valueThe findings provide theoretical and practical implications for promoting positive collaborative behavior among stakeholders in megaproject selection and improving the collaborative performance of megaproject inter-organization alliances.

Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance

Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance

How optimal trust–dependence nexus facilitates relational behavior in contractor–subcontractor collaboration: a configurational analysis

PurposeAlthough trust is generally taken as a fundamental factor in influencing relational behavior in contractor–subcontractor collaboration, the determination of an optimal level of trust is still lacking. Trust with an optimal tipping point that matches dependence best is considered the optimal trust to improve relational behavior between general contractors and subcontractors. To fill the knowledge gap, this study explores how combinations of trust and dependence trigger relational behavior between general contractors and subcontractors through a configurational approach.Design/methodology/approachQuestionnaires were administered to 228 middle management and technical staff members of the general contractor. The data were analyzed using fuzzy-set qualitative comparative analysis (fsQCA), and the inductive analytic method allowed researchers to explore configurations of different dimensions and levels of dependence and trust.FindingsNecessity analysis results indicated that neither dependence nor trust was a necessary condition for facilitating relational behavior. Through sufficiency analysis, four configurations of optimal trust matched with dependence were identified in contractor–subcontractor collaboration. Even if contractors rely only on subcontractors for resources, the optimal trust between contractors and subcontractors should include both institution- and cognition-based trust. In the event that contractor–subcontractor collaboration involves relational dependence, both affect- and cognition-based trust are necessary for the optimal trust.Originality/valueThis study enhances existing research by delving deeper into a nuanced understanding of optimal trust in dependence scenarios, and enriches project governance theory by uncovering the internal transmission of relational governance. Practically, this study offers general contractors guidance on how to establish optimal trust strategies based on the dual dependence level with subcontractors, which can facilitate subcontractors' relational behavior, and ultimately improve contractor–subcontractor collaboration performance.

Collaborative Performances of Wine Tourism Destinations in the Northern Rhone Valley

Purpose: The main aim of this article was to identify the wine tourism destination models set up on collaborative performances driven by Tourism Offices (Destination Marketing Organization, DMO) and wineries (private enterprises) from the appellate areas in the Northen Rhone Valley. Design/methodology/approach: The ethnographical methodology was used, including methods of participating observation (applied by a mysterious tourist method), study tours (to explore the study field), expert analysis (based on knowledge exchange with Tourism Office Managers) and official interviews (with Mangers of small and large wine producers from the areas). Findings: Two of the three surveyed wine tourism destinations from the Northern Rhône Valley (Vienne Condrieu and Rhône Crussol) are distinguished by the very active role of tourism organizations in building collaborative wine tourism offerings, in particular with small wineries. The third destination surveyed (Ardèche Hermitage) is more oriented toward collaborations with large wine producers and their independent role in leading wine tourism marketing. Research limitations/implications: Managers of tourism organizations should be oriented toward collaboration with both: large and small wine producers. Large wineries are helpful in raising the visibility and image of a wine destination. Small producers, on the other hand, offer a distinctive and individual wine tourism experience. Originality/value: The key aspect of wine tourism marketing is the building of collaborative wine tourism offerings led by DMO’s collaboration with both large and small wineries from the destination.