Design data for the 6.2M126 [13].
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"3627",leadTitle:null,fullTitle:"Mechatronic Systems Applications",title:"Mechatronic Systems",subtitle:"Applications",reviewType:"peer-reviewed",abstract:"Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing.\r\n\r\nMechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers.\r\n\r\nChapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. 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The wind is a powerful and abundant source of renewable energy. Wind power installations, both onshore and offshore, have expanded rapidly over the world to harness the energy from wind. However, these wind power plants are not cheap for offshore installations. Hence, the inception of a wind farm entails many considerations and careful planning to justify the associated high cost. As a result, alternative consideration is the wind farm layout that can be optimized to increase the profitability [1].
However, the optimization of the layout is a complicated and challenging process due to a phenomenon known as wake effect. After a turbine extracts wind energy [2], the downstream wind will be turbulent and reduced in speed. It is essential to consider the wake losses when positioning the turbines to maximize energy production taking into account the additional costs for infrastructure if they are spaced far apart. By using modeling and simulation as a tool, analysis can assist in managerial and technical decisions [3]. It is to be mentioned that manufacturer-specific models are sometimes not easy to obtain [4].
Presently, there are few studies performed on wind turbine modeling. Some examples of these studies are available [5, 6, 7]. The focus of their works was primarily on the wind turbine dynamics and its efficiency. They did not consider the implementation of a wind farm or multiple wind turbines. Nevertheless, there are a few researches conducted on wind farm simulation. For instance, the wake loss was not taken [8] into account. Hence, there is a need to systematically model the wind farm instead of just a wind turbine with the wake losses. The wind farm simulation tool should be widely available to users to simulate the feasibility of the wind turbines on the specific site before actual implementation. The approach adopted in this study encompasses the different wind speed for each turbine with consideration of wake losses. The main contribution is to provide a systematic approach to model the output power from the wind farm by considering the wake losses before implementing the wind turbines on the actual site for smart cities.
The chapter is organized as follows. In Section 2, a proposed modeling methodology is presented followed by Section 3 on a simulation model of NordseeOst wind farm. Section 4 describes the results and discussion followed by the conclusion in Section 5.
The NordseeOst, an operational offshore wind farm in Germany, will be used here as a case study, and the wind turbines are modeled based on the governing equations that can be implemented through the function block available in the Simulink Library. The parameters for the wind turbine model will be based on manufacturer Senvion 6.2M126 used in the German farm. The turbine model is duplicated and positioned to follow the layout of NordseeOst wind farm. By considering the wake losses of each turbine using the Jensen wake model, the wind speed input for the turbines can be computed. The wind conditions and temperature in the wind farm site will be obtained from an online resource. For any given timeframe, the output power of the simulated farm can be determined and verified with the real-time data obtained from the actual farm.
Multiple wind turbine models were created to form the wind farm. From a modeling viewpoint, the turbine model can be broken down into three blocks, aerodynamic, mechanical, and electrical, as shown in Figure 1. When the passing wind interacts with the rotor blades, lift and drag forces create the rotation of the turbine blades. This turning force is high in torque, but the speed is low. The speed of the rotating rotor shaft will be increased using a gearbox. The purpose of increasing the rotational speed is to match the requirements of the generator. The turbine and generator shaft is coupled as a drivetrain to transmit the driving force to the generator. The electrical block consists of the generator that creates a magnetic field from the rotor into electrical energy. Each block is modeled using few differential and algebraic equations that describe their functions.
Wind turbine model [4].
The aerodynamic block is characterized by three subsystems, namely, the tip speed ratio, power coefficient, and rotor torque, that are developed. The maximum power that can be extracted from the wind is 59.26% [9]. The power coefficient CP expresses the ratio of extractable power by the rotor to the available power in the wind. The amount of power that can be extracted is given as follows:
where ρ is the density of air in kg/m3, A is the rotor swept area in m2, v is the wind speed in m/s, and CP is the power coefficient.
The density of air can be determined from the turbine elevation above sea level [3]:
where ρo is the density of air at the sea level at a specific temperature in kg/m3 and H is the hub height in m.
The efficiency of the rotor can be described by power coefficient CP since it is a ratio of extracted power to available wind power. It is usually expressed as a function of tip speed ratio and blade pitch angle. The ratio of linear speed at the rotor blade tip to the wind speed is defined as tip speed ratio λ as shown:
where ωr is the rotor angular velocity in rad/s, r is the radius of the rotor (blade length + hub radius) in m, and v is the wind speed in m/s.
For a specific airfoil type, the power coefficient may be expressed as a function of the tip speed ratio and blade pitch angle as follows [8]:
where c1 = 0.5176, c2 = 116, c3 = 0.4, c4 = 5, c5 = 21, and c6 = 0.0068 with λi defined as
where λ is the tip speed ratio and β is the blade pitch angle.
The rotor torque developed by the turbine can be calculated as follows:
where Γr is the torque developed at the rotor in Nm, Pr is the power developed by the rotor in W, and ωr is the rotor angular speed in rad/s.
The purpose of a drivetrain is to transmit the torque generated by the rotor blades from the rotor hub to the generator. The torque generated is filtered by the drivetrain via the gearbox and ultimately drives the generator shaft. In this mechanical block, the drivetrain is modeled by a two-lumped-mass model as shown in Figure 2. The two-mass model [10] is accurate enough for the analysis of transient stability in wind power generation systems when compared with higher-order drivetrain model with three or six masses:
Two-mass model [10].
The stiffness and damping of the low-speed shaft are represented by a spring and damper with coefficient kls and cls, respectively. The equation of motion for the turbine rotor is expressed as follows:
where Ωr is the rotor angular speed in rad/s, Ta is the aerodynamic torque developed by rotor in Nm, Γls is the torque of the low-speed shaft in Nm, cf is the frictional damping, and Jr is the inertia of rotor in kgm2.
The mechanical torque of the shaft is modeled as follows:
where Tls is the torque of low-speed shaft in Nm, kls is the stiffness coefficient, θr is the rotor angular position in rad, θls is the angle of low-speed shaft in rad, cls is the damping coefficient, Ωr is the rotor angular speed in rad/s, and Ωls is the angular speed of low-speed shaft in rad/s.
Assuming an ideal gearbox, the step-up ratio of the transmission speed is shown:
where Γls is the torque of the low-speed shaft in Nm, Ths is the torque provided to the generator in Nm, Ωg is the angular speed of generator shaft in rad/s, Ωls is the angular speed of the low-speed shaft in rad/s, θg is the angle of generator shaft in rad, and θls is the angle of the low-speed shaft in rad.
The equation of motion for the shaft at induction generator is written as shown:
where Ths is the torque provided to the generator in Nm, Tem is the electromagnetic torque in Nm, cg is the damping experienced at the generator, Ωg is the angular speed of generator shaft in rad/s, and Jg is the inertia generator in kgm2.
The ideal induction generator dynamic model consists of three stators and three winding rotors. The differential equations are derived from the state space vector representation using synchronous reference frame for the d-q model. The modeling is simplified and preferable when represented as DC values. In order to do so, the stator and rotor are each transferred onto a direct and quadrature axis (d-q model). The direct and quadrature axes for the stator are represented by ds and qs, respectively. The same axis for the rotor can also be represented by dr and qr. To simulate the induction generator, an equation to represent both the direct and quadrature axis voltages for the stator Vds, Vqs and current Ids, Iqs is required. Eqs. (11)–(18) are the models of an induction generator expressed in a d-q reference frame [3].
The stator and rotor magnetic flux linkages are given as follows:
where Xs is the stator reactance in Ω, Xr is the rotor reactance in Ω, Xm is the mutual reactance in Ω, Ids is the stator current at d axis in A, Iqs is the stator current at q axis in A, Idr is the rotor current at d axis in A, and Iqr is the rotor current at q axis in A.
The stator and rotor voltage equations are given by Eqs. (15)–(18). During start-up, the rotor in an induction generator does not require a supply of voltage. Hence the rotor voltage Vdr and Vqr are equal to zero [3]:
where Rs is the stator resistance in Ω, Rr is the rotor resistance in Ω, Ids is the stator current at d axis in A, Iqs is the stator current at q axis in A, Idr is the rotor current at d axis in A, Iqr is the rotor current at q axis in A, φds is the stator magnetic flux at d axis in weber, φqs is the stator magnetic flux at q axis in weber, φdr is the rotor magnetic flux at d axis in weber, φqr is the rotor magnetic flux at q axis in weber, ωs is the synchronous speed in rad/s, and s is the slip.
The rotor slip can be calculated as follows:
where ωs is the synchronous speed in rad/s and ωr is the rotor speed in rad/s.
The electrical torque developed by the generator is given as follows:
where φqr is the rotor magnetic flux at q axis in weber, Idr is the rotor current at d axis in A, φdr is the rotor magnetic flux at d axis in weber, and Iqr is the rotor current at q axis in A.
The power equations for the generator can then be expressed by Eqs. (21)–(23). The true power can be computed as shown:
The reactive power is calculated as follows:
The apparent power can be written:
where Vds is the stator voltage at d axis in V, Vqs is the stator voltage at q axis in V, Ids is the stator current at d axis in A, and Iqs is the stator voltage at q axis in V.
The Jensen wake model was used to calculate the wind speed after subjecting to wake loss. The model is simplified for a single wake where the diameter of the wake is assumed to be expanding linearly. For a single wake model, the resulting wake of a wind turbine is treated to be turbulent where the near field behind the turbine is neglected. The spread of the resulting wake can be represented by the linear dimension (radius r) which is proportional to the downwind distance, x as shown in Figure 3. The start of the wake, u, that is directly behind the turbine is assumed to be equal to the turbine diameter:
Linear expansion of wake cone for a single wake model.
A balance of momentum gives the following equation:
where r0 is the rotor blade length in m, u is the wake speed in m/s, r is the radius of wake cone in m, v0 is the incoming wind speed in m/s, and v1 is the resultant wake speed in m/s.
The radius of the wake cone r that represents the path of incoming wind after passing through the turbine is shown:
where r0 is the rotor blade length in m, α is the dimensionless scalar, and x is the distance from a wind turbine in m.
The speed of wake expanding with distance which depends on the dimensionless scalar α is defined as follows:
where z is the hub height in m and z0 is the surface roughness.
The surface roughness constant is dependent on the characteristics of the local terrain, while the dimensionless scalar α depends on both the local terrain and wind climate conditions. The paper [2] had mentioned that a value of 0.04 can be used for α for free stream wind which has yet to pass through any wind turbine or otherwise the value of 0.08 can be assumed.
By solving Eq. (25) in terms of v1, the velocity of the wake at a downwind distance x from the wind turbine can be calculated:
where v1 is the wake speed in m/s, v0 is the incoming wind speed in m/s, CT is the thrust coefficient, r0 is the rotor blade length in m, and r is the radius of wake cone in m.
A thrust coefficient is a dimensionless number that defines the thrust of a wind turbine. The value of thrust coefficient varies with wind speed, and the paper [11] has shown that it has a maximum value of 1.
NordseeOst is a 295 MW offshore wind farm located 35 km northeast of Heligoland, a German archipelago in the North Sea region. The wind farm consists of 48 turbines [12]. The site has an average wind speed of 9.77 m/s and an area of approximately 36 km2. The site coordinates are 54° 26′ 24″ N and 7° 40′ 48″ E that are used to determine the wind condition. The layout of the wind farm used in the simulation is shown in Figure 4.
Wind turbine layout of NordseeOst (RWE international SE, 2016).
The wind turbines used in the German wind farm was manufactured by Senvion. The 6.2M126 wind turbine model has a rated power of 6.15 MW and a designed rotor diameter of 126 m. The parameters for the wind turbine model presented are based on the design data provided by the manufacturer in Tables 1 and 2. The data from Table 1 are the specifications for the wind turbine used in the aerodynamic and mechanical blocks. The data in Table 2 are the parameters of the electrical system used in the induction generator block.
Nominal power (kW) | 6150 |
Cut-in wind speed (m/s) | 3.5 |
Nominal wind speed (m/s) | 14 |
Cutout wind speed (m/s) | 25 |
Tip speed (m/s) | 79.8 |
Rotor diameter (m) | 126 |
Rotor area (m2) | 12,469 |
Rated rotor speed (rpm) | 12.1 |
Hub height (m) | 92 |
Blade length (m) | 61.5 |
Gear ratio | 1:97 |
Design data for the 6.2M126 [13].
Nominal power (kW) | 6150 |
Nominal voltage (kV) | 20/30/33 |
Nominal frequency (Hz) | 50 |
Stator voltage (kV) | 6.6 |
Nominal speed (rpm) | 1170 |
Speed range (rpm) | 750–1170 |
Parameters for the generator [13].
With all the structure and framework laid out, the next step is to implement the model in the Simulink environment. The mathematical model for each block was created and coupled together to form a wind turbine as shown in Figure 5. Within each respective block, few mathematical equations governing their functions as described in Section 2.1 were modeled. The parameters available from both Tables 1 and 2 were used in the equations.
Wind turbine model in Simulink.
The same approach was applied to the Jensen wake model. In the Simulink environment, the wind turbine model in blue and the wake model in red were duplicated and arranged to mimic the NordseeOst in Figure 6. The wake model was used when there is a wind turbine directly ahead of another. There is a spacing of one or two grid lines that corresponds to 914 m and 1828 m, respectively, with reference to Figure 4. For instance, the first column of wind turbines is not affected by any wake losses; hence the wake model is not required. The wind speed for the second column of turbines that are influenced by the wake effect from the first column is calculated from the first wake model block in Figure 6. The process is repeated for the remaining wind turbines. With a user-defined wind input, the simulation will compute the output of each turbine and collectively will be summed up to give the total power production of the wind farm.
Simulated NordseeOst in Simulink.
The wind data is gathered from an online resource known as Earth. It is a visualization of global weather and ocean conditions forecasted by supercomputers. The platform is a hub of information from collective national agencies. The weather and the wind information were obtained from the National Weather Service in the United States of America. Using the platform, the wind speed for the simulation input at any time frame was gathered from the NordseeOst coordinates as shown in Figure 7.
Wind speed at NordseeOst from earth online.
The live production data of NordseeOst in Figure 8 is available online through an interactive map by RWE Innogy, the owner of the mentioned wind farm. This value is used as cross-reference in conjunction with the simulated results. The accuracy and functionality of the simulation can be validated by comparing the results.
Real-time output of NordseeOst [14].
A simulation is performed over a range of wind speed 3–25 m/s at an interval of 1 m/s. The range is determined by the specified cut-in and cutout wind speed of Senvion 6.2M126 offshore wind turbine. The projected power output for the simulated farm can be seen in Figure 9. It indicates an upward trend over the increase of wind speed. From a logical standpoint, the higher the wind speed, the higher the power is generated. In the simulation, it shows the power increases with the speed of the wind. The graph shows a steady rise from the specified cut-in speed of 3 m/s where the turbine can produce useful work. It is evident that the gain begins to stabilize and reach the specified cutout speed of 25 m/s. The peak power of 300 MW is quite close to the capacity of NordseeOst at 295 MW.
Projected power output of simulated wind farm.
Theoretically, the 295 MW should be attained at the nominal wind speed of 14 m/s according to the Senvion 6.2M126 offshore wind turbine data sheet. However, the graph included the wake losses. With the required additional wind speed of 11 m/s to achieve a rated output, it is evident that wake losses play a significant role in power efficiency. It emphasizes the importance of optimizing the wind turbine position to reduce the effect of wake losses. From the results gathered in the study of NordseeOst, it can be observed that the wind farm layout optimization can be further improved.
As seen in Table 3, a test run was performed with a wind speed of 5.10 m/s in a particular period. The resulted power output of the simulation is 8099 kW, while the actual power from the wind farm is 7816 kW. There exists a difference of 3.5% in the results. Due to the limitation of the Earth where the weather forecast was updated every 3 hours, reading was taken every 3 hours for 18 hours to check for consistency. The average difference of 5.11% can be seen in Table 3. There are several possible reasons for the discrepancy. One possible reason could be due to the sizing of components in the mechanical and electrical systems. With the absence of a physical system, the parameter of inertia, damping and stiffness coefficient in the drivetrain, and components such as resistance, inductance, and reactance in the induction generator are not readily available.
Time (24 hours) | Wind speed (m/s) | Simulated (kW) | Actual (kW) | Difference (%) |
---|---|---|---|---|
0800 | 5.1 | 8099 | 7816 | 3.49 |
1100 | 7.4 | 35,958 | 33,339 | 7.86 |
1400 | 10.5 | 112,555 | 110,678 | 1.70 |
1700 | 10.0 | 99,691 | 95,611 | 4.27 |
2000 | 6.4 | 23,686 | 22,434 | 5.58 |
2300 | 6.6 | 25,104 | 23,297 | 7.76 |
Average | 5.11 |
Three hourly data log.
Another possible reason is the lapse in representing wake losses in a wake model. The Jensen wake model provides a simple and reasonably accurate representation. The orientation of turbine layout and wind direction will affect the direction and position of the wake cone. As a result, it caused the interaction of wake cones and altered the effect on wake loss. It is indicative that the Jensen wake model may have underestimated the effect of wake loss. Hence, further studies in improving the Jensen wake model are needed.
Another reason is the limitations of the wind forecast that is updated only every 3 hours. It is not synchronized with the live production data from RWE. It could cause some slight deviation in the simulated results. The availability of the actual wind speed at the wind farm site will be useful. It will allow a better and more accurate model validation.
However, the results attained are valid and have the potential for simulating and analyzing a wind farm. This methodology offers a solution in modeling the wind farm before any site implementation. It can contribute to future wind energy-related studies that have not been addressed in the literature of wind farm modeling and simulation for smart cities.
The systematic approach in simulating an offshore wind farm model was presented. The simulation tool was used to predict the output power projection with consideration of the wake loss using Jensen wake model. The wind turbine model was adapted from Senvion 6.2M126 offshore wind turbine. Based on the case study of NordseeOst wind farm in Germany, the wind farm was replicated with multiple completed wind turbines with consideration of the wake model. The application of the wind farm was validated with wind speed ranging from 3 to 25 m/s. The simulated results were compared with the real-time data from the NordseeOst wind farm. The average difference is approximately 5.11%. The same approach can be used in the design and developmental phase of any wind farm to predict the output power for smart cities.
For future works, the wind turbine model can be enhanced by incorporating details and intricate representative of various systems used in the modeling. The simulation results can be verified by comparing with other simulation tools such as Wind Atlas Analysis and Application Program (WAsP). An improved wake loss model and layout optimization will be considered in future studies.
The author would like to express his sincere gratitude to the staff of Newcastle University in the United Kingdom for their continuous support and encouragement.
There is no conflict of interest in this chapter.
Although tourism is generally understood as people traveling for pleasure, it is nevertheless a complex activity that involves a number of issues that intersect over several branches in the economic and social sectors [1]. Defined by the World Tourism Organization (UNWTO) [2] as “a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes”, tourism today is recognized as one of the world’s largest economic sector, supporting more than 313 million jobs in 2017 and generating 10.4% of global GDP [3]. Considering the predictions of increased global growth and the unique connectedness of tourism sector, it is clear why UNWTO has recognized tourism as one of the drivers for realization of the sustainable development goals [4] and even dedicated a separate website to the promotion of the idea and mission [5]. It has long been established that short-term market success might in fact lead to the deterioration of the destinations and therefore natural environment. Sustained market competitiveness requires a balance of growth orientation and environmental commitment at an acceptable rate of return to all industry partners involved in the marketing of a destination [6]. For this reason, it is critical for future of the tourism development to adapt marketing in tourism to preserve both natural and cultural heritage. In achieving this, the appropriate use of the key enabling technologies plays the crucial role.
In this book chapter we introduce the largest government-sponsored research project in Slovenian tourism called Tourism 4.0, together with the main marketing challenges of the project itself and solutions developed in it. Slovenia is a small country located in the south of Central Europe with a long history of tourism. For instance, records of people traveling to the spas of Rogaška Slatina go back to the middle of the seventeenth century [7], while modern cave tourism in Postojna began in 1818 [8]. Hence, it is not surprising that the Tourism 4.0 partnership has been established here. Its ambition is to create a testbed in order to transform the tourism industry into an innovation-driven economy by enriching tourism experiences with the use of technology and data. The term originates from the modern paradigm in industry, known as Industry 4.0 [9] and aims to improve the added value to tourism through innovation, knowledge, technology and creativity. This is planned to affect every field around it by creating an ecosystem, in which physical and digital space, infrastructure, personnel and technology behind it merge into one seamless experience of many personalized outputs. The knowledge, expectations and experiences of tourists, defined as Tourist 4.0 in the project frame, will be utilized to build the new services and products to rise up the satisfaction with the experiences of all stakeholders in the tourism ecosystem.
By uniting managers of physical spaces in the ecosystem (towns, municipalities) with leading Slovene research and educational institutions, as well as technology experts and developers, a collaborative ecosystem is being created, which treasures the history and knowledge of tourist business and at the same time transfers it into the digital world. The result will be a new format of dynamic collaboration system among all tourism stakeholders sprouting a new generation of tourist applications and services, which are built on real tourist needs and wishes by using the key enabling technologies from Industry 4.0 harmonized with the Tourism 4.0 principles.
Subsequent to this introductory section, the following chapter is organized as follows: Section 2 provides the description of the future tourism we want to create with Tourism 4.0, in Section 3 the implementation and ambition of the project is shared, Section 4 discusses the marketing challenges as well as introduces the Twirl marketing model of a paradigm shift and in the last section the chapter is reviewed.
Tourism is ought to be an enjoyable experience for visitors, while at the same time providing a substantial source of income for many destinations and even entire countries. Unfortunately, several studies, even dating back to the 1960s [10, 11] and 1970s [12, 13, 14], have indicated the existence of many worrisome consequences of tourism. The areas negatively affected have found out to be [15, 16]:
Environmental—increased consumption of electricity and water, extra waste production, overcrowding, reduction in accessibility, increase in population density, etc.
Economic—increase in real estate and land price, shift from market prices to higher tourist prices [17], employment fluctuation, economic dependence on a single industry, etc.
Social—illegal goods and people trafficking, excessive commercialization, loss of cultural identity, etc.
Since the number of annual international tourists travels has more than doubled in the last 20 years (to over one billion, shown in Figure 1) reaching 1323 million in 2017 [18], it is essential to ensure sustainable growth of tourism in order to avoid the deteriorating effects on both the social and natural habitat.
Number of international tourist travels over the last decade. Source: World Tourism Organization, Yearbook of Tourism Statistics [19].
Many cases of extreme ramifications of tourism can put pressure on resources and the host communities, hence leading to a decline in the quality of living. All of this brought about the issue of overtourism, defined as “the excessive growth of visitors leading to overcrowding in areas where residents suffer the consequences of temporary and seasonal tourism peaks, which have enforced permanent changes to their lifestyles, access to amenities and general well-being” [20].
In 2017, Slovenia recorded more than 4.9 million tourist arrivals and almost 12.6 million overnight stays, which is 13% more than in 2016 [21]. As shown in the Table 1, the growth is continuous for 4 years already, with this tendency expected in 2018 and also predicted for the near future. By making a total contribution to 11.9% of GDP in 2017, with a forecast of rise to 14.5% in 2028 [22], tourism is becoming a more and more important economic activity.
Year | Tourist arrivals | Overnight stays | ||
---|---|---|---|---|
Total | Foreign | Total | Foreign | |
2008 | 3,083,713 | 1,957,691 | 9,314,038 | 5,351,282 |
2009 | 2,984,828 | 1,823,931 | 9,013,773 | 4,936,293 |
2010 | 3,006,272 | 1,869,106 | 8,906,399 | 4,997,031 |
2011 | 3,217,966 | 2,036,652 | 9,388,095 | 5,463,931 |
2012 | 3,297,556 | 2,155,612 | 9,510,663 | 5,777,204 |
2013 | 3,384,491 | 2,258,570 | 9,579,033 | 5,962,251 |
2014 | 3,524,020 | 2,410,824 | 9,590,642 | 6,090,409 |
2015 | 3,927,530 | 2,706,781 | 10,341,699 | 6,614,443 |
2016 | 4,317,504 | 3,032,256 | 11,179,879 | 7,342,118 |
2017 | 4,948,080 | 3,586,038 | 12,591,562 | 8,572,217 |
Tourist arrivals and overnight stays in Slovenia from 2008 to 2017. Data taken from the SURS—Statistical Office of the Republic of Slovenia.
The rapid rising of numbers put Slovenia ahead of new challenges. While each destination has to constantly strive to improve and innovate in order to stay competitive, local communities (not just tourist service providers and the government) must play a key role in the future development of tourism. Another issue in need to be addressed is the fact that there has been a lack of research in Slovenian tourism [23]. Existing studies pointed toward outdated business methods, lack of cooperation between public and private sector tourism managers, a low level of awareness by the government on the importance of responsible tourism practices as a major impediment in improving competitiveness [23, 24]. Fortunately, in the last years this attitude is changing and in 2017, the efforts of Tourism and Hospitality Chamber of Slovenia in the area of sustainable tourism have finally been recognized and included in the development areas of the smart specialization strategy [25]. With this action, the tourism industry has been set up alongside health and medicine, circular economy, factories of the future and others as a prospective field justified for government co-financing on research and development projects. The specific objectives aimed to be achieved are: an increase of energy efficiency in tourist facilities for 20% by 2021; an increase in the number of the fast-growing companies in tourism from 29 in 2015, to 50 in 2021; a raise in the level of knowledge and quality in Slovenian tourism and a raise in the added value in tourism for 15%.
Accordingly, the prevailing approach needs to change. Therefore, the aim of Tourism 4.0 is to develop a model of collaboration that minimizes the negative impact of tourism, while at the same time improving the overall experience. This goal will be obtained by using the concepts and tools provided by smart tourism [26, 27, 28, 29]—a phenomena describing the convergence of information and communications technology (ICT) with tourism experience, expanding them even further, and integrating them into a platform that will involve all stakeholders active in the tourism sector: the local community, government, tourist service providers and of course tourists—presented in Figure 2.
The core of the Tourism 4.0 concept is to integrate all the stakeholders (local community, tourists, tourist service providers and government) in the sector within a collaborative innovation process centered around the local community.
The main ambition is to build an interactive platform based on the state-of-the-art technology infrastructure, guaranteeing the sustainable development of services and products, accessible to everyone at any time. It will facilitate the tourist’s integration into the destination, increase the quality of the experience and support individual’s interaction to develop tailored products and services. In other words, we want to foster the leap that was enabled by high technologies from Industry 4.0 at products level, by implementing them in tourism services.
For successful implementation of the project goals, the platform will integrate four main pillars or modules, which are at the same time different areas of research for understanding the tourism ecosystem:
Technology module. With the use of cutting-edge technologies enriched tourist experiences will be delivered. This includes:
Internet of things (IoT) for enabling networks of smart device for monitoring and collecting information;
High-performance computing systems (HPC) with artificial intelligence (AI) for the purpose of solving most complex challenges involving data;
Virtual and augmented reality (VR and AR), which can elevate or enhance the overall experience;
Blockchain solutions for a safe and transparent way of delivery.
Innovation module. Research and development of practices and methodologies dedicated to the creation and diffusion of innovation in the ecosystem with the aim to raise the level of competences among all stakeholders.
Ecosystem module. Understanding the tourism ecosystem and all its stakeholders in both the physical and digital world in order to create the space for the new generation of tourism.
Application module. Enabling systems and applications that improve and enrich the real and digital experiences in tourism with the focus on mobility and services (and processes behind) for persons with the purpose of holidays, leisure or recreation, business, health, education or other.
In the Tourism 4.0 ecosystem the local community is in focus while Tourists 4.0 are very active participants in all processes. They have a direct impact on their own experience and act also as co-creators of the tourist offer. With this in mind, we should not forget that the change is only possible by data sharing of all stakeholders of a given ecosystem, thereby acting as a driver for all of the processes on the platform. Furthermore, the data need to be analyzed, if they should be turned into useful information that can be used for strategic decisions and marketing purposes, such as people flow management.
After the initiative was launched by the company Arctur, lack of readiness level to embrace the use of the key enabling technologies, especially by small and medium enterprises in the tourist sector, has been identified. As a consequence, the partnership for Tourism 4.0 has been established to enable collaboration of any stakeholder in the world to participate in research or development of tourism of the future. It brings together a consortium of highly relevant industrial organizations, top research organizations in tourism and the leading research organizations in computer and informatics technology.
The partnership is growing continuously and already boasts with a number of prominent members, such as Slovenian Ministry of Economic Development and Technology as well as Ministry of Public Administration, Tourism and Hospitality Chamber of Slovenia, Association of Towns and Municipalities of Slovenia, University of Ljubljana, University of Maribor, University of Primorska and many partners joining from all over the world. Not only research, also projects focused on validation and deployment of the technologies are planned in order to transform Slovenia into a testbed, Tourism 4.0 Living Lab Slovenia. Further activities in the area of Alps, Danube, Balkans and Mediterranean are set to spread the concepts to an international level.
Sustainable tourism [30, 31, 32] is at the core of Tourism 4.0 that is aspiring to using technology to encourage a positive environmental, social and economic impact and collaboration between all stakeholders in the ecosystem. Following the slogan of Slovene tourism: ‘Green, active, healthy’, new ways of motivating not only individual tourists but all stakeholders will be studied in order to encourage as much of positive contribution as possible for the local environment while traveling and visiting places. This will be achieved by establishing a system of motivation and rewarding for positive behavior i.e. behavior with positive impact on social, environmental and economic elements in a destination—both from tourists and other stakeholders. In this way tourism truly becomes the driver of the sustainable development of the whole society. These are heavily dependent on the quality and intensity of communication, if they are to be reached. For this reason, marketing has a key role in this endeavor since reshaping of the tourism ecosystems with the focus on the local community will not just happen even though this is a crucial step toward the tourism of the future.
The model, that has been so far only theoretically constructed, is named Collaboration impact model (CIM). For its implementation certain tools need to be developed to help with collection, understanding and integration of data. This includes data such as weather prediction, traffic density, number of tourists in a defined area, energy consumption in tourism, etc. In an interactive way, through gamification, we want to reduce the negative and encourage the positive behavior and promote positive attitude to inspire more responsibility among citizens of the globe. The CIM model will use the data of the ecosystem to help tourists with suggestions for tourist activities. When using this model, the tourists will receive real-time information and recommendations based on their own profile, which will help them decide, for example what places to visit or what activities to partake in. Furthermore, users will be able to switch between their own avatars since interest of tourists vary significantly according to their purpose of traveling, which is not limited to for business and pleasure only.
Collaboration impact model is supported by several technological solutions. The most impactful one is the creation of digital tokens, a boosting and rewarding tool of the positive impact accomplished through collaboration within the ecosystem T4.0. In our proposed system, they are named after the model and thus called collaboration impact tokens (CIT). The currency will be needless to say developed on the basis of the blockchain technology. These tokens will be awarded to stakeholders of the tourism ecosystem as incentives for performing certain actions that are beneficial to the local economy and environment. They could help preserve and promote cultural heritage, reduce the consumption of natural resources and support an effective dispersion of tourism.
Examples of CIT awarding are shown in Figure 3. However, there are many more. With this mechanism, responsible development of ecosystem is enabled by encouraging positive attitudes, using of more ecologically-friendly transportation and stimulating a more favorable allocation of tourists. The latter is a pressing issue as more and more destinations are struggling with negative consequences of overtourism. In the next step, we plan to go further and integrate attitudes that encompass most different aspects of society and include them in the rewarding system. For example, visiting elderly people and spending time with them for few hours or reporting a damaged public trash bin to the municipality could be encouraged by incentives in the form of tokens. We believe that step by step with the technological development as well as by raising awareness through strategic communication rewarding positive impact on wider society will become the norm.
Examples of methods and actions with which a tourist can earn the collaboration impact token—CIT and the attitudes we want to promote.
Anyhow, visitor management techniques can also be applied to select or deselect tourists, control their flows and influence their behavior through promotion and education [33]. With the help of internet of things, it is possible to monitor and thus measure many human activities. The most common example in the tourism sector is the water and energy consumption of each accommodation. Many hotels collect such data but they do not use them. In accordance to our collaboration impact model, the tourists, who exhibit ecologically conscious behavior, i.e. consume less than average consume of water and energy of tourists in that specific area, would receive a reward in the form of CITs. The received tokens can be used for purchase of other tourist products or services. In addition to increasing the sustainably driven attitude preserving the environment is this beneficial for the local economy. Another example of desirable behavior is visit during the low tourist season or places in the touristic periphery. An additional issue of significant importance due to the size of its impact is the mobility. Here good behavior is determined and rewarded by the use of public transport or sharing services besides renting a bike or an electric car. The underlying idea is that tourists are awarded and thus motivated to exhibit behavior with a positive impact more often.
The most important aspect of the collaboration impact token is the assignment of dynamic values according to location and time of its use. For an illustration, we examined the number of tourists’ overnight stays for each municipality in Slovenia in 2017 and normalized it to the municipality’s population density (residents/km2), as seen in Figure 4. The discrepancy can be noticed immediately. Some areas experience a much higher tourist density than others, with some parts already experiencing overtourism. The value given in the calculation is dependent on:
visits to the geographic area—lower in the red and higher value in the green areas;
tourist season—low season means higher value;
strategic development level of the region—higher value in less developed regions;
etc.
The number of tourists overnight stays in 2017 for each municipality normalized by its population density (i.e. residents/km2). The data is taken from the SURS—Statistical Office of the Republic of Slovenia.
As the example of the calculation demonstrates, the use of public data can already enable the first insight into understanding of tourist flows. Building an award-based collaboration ecosystem that helps to manage issues of people flow within a destination, region or even a country is very rational and strategic since it also supports less developed regions in establishing their touristic sites. By implementation of the CIT that provides instant award in discounts, tourism of the beaten path can rise significantly. This does not only reduce the unbearable crowds of tourists in one spot of a destination, but above all immensely improves the tourist experience.
It is the lack of capital, technology and marketing and management expertise that restrains the growth of tourism in many less developed regions in the world [33]. An adoption of societal marketing strategies will thus have to take place, where increased visitations are not the primary concern and visitors are not treated like any other commodity. This will include monitoring of both the tourist and host satisfaction for the purpose of optimizing the of tourism impact on the environment and consequently increasing the benefits for the local community [34, 35]. Since monitoring is dependent on data, access to them is increasingly relevant to the tourism sector as well. It allows for the provision of evidence-based decisions and the development and analysis of future scenarios, which infuse the whole sector with additional efficiency and productivity to face its current challenges. In addition to accessibility, green mobility and connectivity has been identified as a priority area for facilitating the flows of movement, and easing the negative impact of tourism on local environment, along with reducing the effect on climate change.
Moreover, the prevalence of smartphones and mobile devices are swiftly changing the face of technology use for destination marketing as people now have access to the virtual world in palms of their hands [36]. More importantly the new technology enables smaller and peripheral destinations to compete on equal footage with larger and more central ones. The gained competitiveness lies in reduced dependency on intermediaries for the distribution of tourism products. As a consequence, the providers of tourism services are able to improve their negotiation power, which fosters the development of a healthier distribution mix. As aforementioned, this is of even greater importance for remote, peripheral and insular destinations where local principals and authorities have a great dependency on tourism for their income but lack expertise and resources to undertake comprehensive marketing campaigns [31].
Only an advanced ICT infrastructure enables a more dynamic, less rigid, and fast interaction within organizations and between organizations at the tourist destination, which empowers stakeholders through the creation of a technological infrastructure for increased communication and collaboration [34]. Tourist destinations are now, more than ever, building competitive tourist places using state of the art business models. However, without a proper information environment these business models are often subjected to failure. The T4.0 platform facilitates the just-in-time information exchange between various stakeholders from different places or markets using the key enabling technologies available at this time (illustrated in Figure 5). This way, the platform reduces the information and choice overload, which currently presents one of the key barriers in conducting tourism business. What is more, various tourism providers can use the platform to share their services, products or information to users at all levels of the industry (e.g. G2C, B2B, B2C, C2C).
The enabling technologies for project Tourism 4.0.
The Tourism 4.0 platform actually enables the communication between stakeholders and collaboration within the ecosystem where users are involved in the development of new tourist products and provide free access to the collected data, thereby encouraging innovation through the use of modern technology. In the implementation of technological solutions of Tourism 4.0, local communities play a vital role, thus we are putting them at the center of the ecosystem of future tourism. As already presented in Figure 2, the Tourism 4.0 platform is envisaged toward four main target audiences:
The providers of tourism services and packages (e.g. local tourist organizations and tour operators). The interest of this group is in the direct access to their target audiences, which will be enabled through the platform. There is a number of niche web (and mobile) platforms that almost proverbially offer only one solution to the end user (e.g. only hotel reservations, only attraction tickets, etc.). Contrarily, the Tourism 4.0 platform will gather together a number of various but complementary proposers of tourist services that will be interested to promote through the platform due to the considerably reduced margin for the referred business.
The tourist. They will be encouraged to use the platform mainly because of its all-in-one portfolio and also its embedded features. The combination of an all-encompassing offer, accompanied by AI will enable the platform to produce a tailored offer to the user. The AI will have an overview of the interests and preferences of the end-user and the services at offer. By combining the two, the user will get offered first the most interested services for her or him in that moment followed by services listed accordingly to their relevance for the user.
Local inhabitants. Through the collaborative platform, it will be possible to measure, monitor and manage the impact that tourists have on the local environment. This can be used to control the quality of living of the population and the quality of the tourist experience of the guests. In a second step also measuring of attitudes of local inhabitants are planned to be included in order to incentives all stakeholders to strive for the positive impact.
Government. Providing a collection and aggregation of data that can help in understanding and development of appropriate policy regulations, which are not only able to react to disruptions but even boost innovations.
In understanding the ecosystem in such way, a question emerges on who is the consumer in this new paradigm of tourism, as every stakeholder is collaborating, profiting and co-creating. These are the challenges that need to be embraced by the marketing specialists in the near future.
Actually, from the technological and business aspect, tourism and e-tourism have always been in the background, in the sense that tourism was predominantly following innovation. In the latest technological wave, many of the most prominent startups and corporations of the new generation are strongly attached to tourism, mainly through sharing economy. Sharing economy is just the beginning of a trend that indicates how the technology allows the sharing of tangible and intangible elements of all kinds of user experiences, in tourism as well. The tendency will continue with the intensive incorporation of all other current trends (AI, VR, AR, IoT, Smart Mobility, Blockchain, etc.). It will not only be about new ways of sharing and building up these elements. For the economic sector, it is important that these elements are appropriately used in new, innovative business models. Therefore, the technological project outcomes will be adopted to the new and innovative business models in tourism in general, as well as in the areas of destination management and event management.
Here are some examples of the use of the key enabling technologies in tourism that will transform the tourism business in near future:
Virtual reality could help potential visitors to decide which destination they prefer to visit;
Augmented reality could enable a completely new touristic experience that is either historically informative or adventurous in nature;
Combination of external data such as weather forecast with travel information would implement an automatic rebooking of a trip in case of a rainy day.
Such and similar future applications could become a new income stream in tourism, especially in relation to natural landmarks since most of them can be experienced for free. We do not promote limited access to nature because access to it should stay free of charge. On the other hand, interactive experience offered via augmented reality app could be payable and the income acquired could be spent on preserving the nature in a given destination. This way we would not rely solely on demand management for sustaining tourism resources with effective marketing, which can channel tourist flow to places that are more impact-resilient, such as urban and seaside built environments rather than to more fragile wilderness areas [33]. With the income from the enriched tourist experience national parks and heritage sites would be able to invest into preservation of natural environment in addition to managing the ever increasing demand.
In fact, the sustainability of local resources is becoming one of the most important elements of the destination image, as a growing section of the market is not prepared to tolerate over-developed tourism destinations and diverts to more environmentally advanced regions. The degree of consumer satisfaction will depend on the assessment of the perceived overall experience of the destination versus anticipated expectations and perceptions [34]. Smart tourism will become the driver of social change due to its high innovation potential and above all impact on economic, cultural and natural environment. As explained, marketing has a crucial role here. Not only in promoting sustainability in tourism but also in marketing in an attractive way of less popular tourist destinations. Inefficient use of many tourist facilities (hotels, for example, often have annual bed occupancy rates of 50–60% in most countries), indicates that the effective marketing of these tourist resources is of great significance in reducing resource wastage as well [33]. Challenges and opportunities are many, especially because throughout the world tourism services are offered by small and medium tourism enterprises, which tend to be family managed. Hence, the challenge for destination management organizations is to provide leadership in the development of innovative products and create local partnerships for the delivery of seamless experiences [34]. The collaboration impact model can provide that but marketing and communication experts will need to develop new strategies to make better use of the new tools with the purpose of optimizing the positive impact of tourism while increasing the benefits of the local community.
In marketing of new concepts, such as a new paradigm, the first issue everybody is facing is how to build trust and rise interest for complex and new solutions since trust and perceived risk are shown to be direct antecedents of intention to transact, suggesting that uncertainty reduction is a key component in consumer acceptance [37]. Hence, it is crucial to be aware of the lack of readiness level of the target groups in terms of lack of skills and lack of processes, in which research and development spirit could fit into strategies and implementation of changes.
To address this, the Twirl marketing model of a paradigm shift, presented in Figure 6, has been developed that shows in which process the appropriate target groups were identified and how the nature of communication has been growing into collaboration. In this process the communication starts with the informative phase addressing a broad public. It is upgraded in the second step with educational dimension that focuses on a smaller target group, which is then in the third step reduced to the key persons of each specific target group. With them conversations take place in order to find the possibilities to embrace the new concepts. This leads to collaboration, with which the amount of people reached via informational, educational and conversational communication raises again. In the best case, such organic growth occurs through community building. This is the reason for establishment of the Tourism 4.0 partnership, which is open to new interested stakeholders.1 In addition, an online form is available on the website, where anyone can share their idea or solution in the field of Tourism 4.0 and are later connected to potential partners.
Twirl marketing model of a paradigm shift.
How does the Twirl marketing model of a paradigm shift prove to work in practice? In the informative phase all available tools to spread information are used, such as digital presence (website with news section for updates, social media, newsletter, etc.) and participation or organization of events with good media coverage or specific to the topic performed. This enables the reach of the appropriate target groups. Tourism 4.0 as a new paradigm has been presented both at events organized locally and around the globe, in China and Mexico for example, but always accompanied with good national media coverage.2 The goal was to awake interest and give it relevance through international dimension. In the second phase, education in the form of presentations at conferences, forums3 and fairs were given in order to provide knowledge of the paradigm shift and its necessity while getting experts and policy makers in the tourism sector acquainted with the Tourism 4.0 objectives. To achieve this, concrete examples of technologies were presented in the form of vivid storytelling.4 The play of concepts around marrying tourism with technology has been created to help novices with low to limited knowledge of technology to embrace the new concepts and possibilities they bring. From persons targeted during the education phase some individuals, small groups and organizations showed interest for a further conversation, which is the activity of the third phase of the Twirl marketing model of a paradigm shift. This phase includes personal conversations with tourist organizations and institutions and service providers. At the same time, deep conversations will take place through an extensive survey conducted as a part of the
Only after informing the public, educating specific target groups and conversing with key decision makers an inclusive collaboration at a local or global level is possible. In the marketing model of a paradigm shift after performing the first ‘round of process’ further marketing strategy builds on an eternal interviewing of all communication methods from the first three phases for multiplication of collaboration. However, the precondition remains the trust of all stakeholders, which is being built by interaction that hopefully grows into collaboration. This interactivity is enabled by the modern tools for tourism marketing and management that can re-engineer the entire process of developing, managing and marketing tourism products and destinations as well as provide the competitive advantage with the ability to re-develop the tourism product proactively and reactively [39].
Therefore, the next step of the Tourism 4.0 project marketing strategy is testing of above-mentioned solutions with a series of products at TRL 6–8 stage.6 The goal is to test the prototypes in operational environment to support commercial activity in ready state in environments called living labs, where future reality and future tourism in it are simulated. Imagine a playground in which technology service providers can test their gadgets with tourists, who want to be part of such a testing experience accepting the violations of their privacy. At the same time, public decision makers can learn from the reality and adapt the legislation accordingly. Students, waiters and all other personnel in contact with tourists get the possibility to train how to deal with crypto tokens while children will be educated about their water consumption and challenges that we as citizens of the world are facing and how to behave to minimize our negative impact. Key to the marketing a paradigm shift is to understand that the marketing process needs to address all stakeholders. To mention the most crucial fact, we are aspiring to facilitate the break down the silos mentality where stakeholders do not wish to share information and provide feedback with one another. Such trials will connect consumers (both local community and tourists) and tourist providers, (local) government as well as high-tech companies in order to unify the dispersed ideas, experiences, knowledge and expertise with the intent of integrating them in a collaborative manner around the Tourism 4.0 platform and later used in real business environment.
With more than one billion persons traveling yearly around the world, a small change in this sector has a huge impact on the whole society. The new paradigm, Tourism 4.0, build around technologies from Industry 4.0 can path the way of transformation not only in tourism, but also as driver of the UN sustainable development goals. In Slovenia, the biggest research project on tourism in the history of the country called Tourism 4.0, which has already grown into a Tourism 4.0 partnership, gathering academia, business and public partners, is focused on defining the ecosystem, in which these changes could take place. Hence, the main objective is the development of a platform for a new form of active cooperation system among the local community, tourists, tourist service providers and the government.
The system aims to encourage positive behavior among all stakeholders in order to maximize positive impact on the local environment through the collaboration impact model. For its implementation certain tools need to be developed to help with collection, understanding and integration of data. This includes data such as weather prediction, traffic density, number of tourists in a defined area, energy consumption in tourism, etc. In an interactive way, through gamification, encouragement of the positive behavior and promotion of positive attitude to inspire more responsibility among citizens of the globe is foreseen. Digital token, to mention the most impactful one, is a boosting and rewarding tool of the positive impact accomplished through collaboration within the T4.0 ecosystem. The so-called collaboration impact tokens (CIT) is a currency developed on the basis of the blockchain technology. These tokens will be awarded to stakeholders of the tourism ecosystem as incentives for performing certain actions that are beneficial to the local economy and environment. In addition to gamification and incentives, social marketing strategies promoting sustainability and positive impact will be used.
The Tourism 4.0 ecosystem is built around the problems and wishes of local inhabitants and in only in the second step around those of other stakeholders. Hence, we are facing the shift from tourist-centered focus to a tourism-centered focus around the local community. During the development of such an ecosystem a question emerges about who is the consumer in this new paradigm of tourism as every stakeholder is collaborating, profiting and co-creating. As the right answer due to the collaborative nature of the Tourism 4.0 ecosystem is everyone, the Twirl marketing model of a paradigm shift emerged. The model leads to collaboration through the process of finding the right partners that bring new collaboration and help spreading the messages and ideas of the paradigm shift. This occurs by first addressing the broader public, educating the appropriate target groups via innovative marketing tools and event presentations at various conferences, forums and fairs, where interested individuals and organizations are found. This leads to personal conversations with them aimed at finding common goals and their implementation into strategies and actions plans. In this way an environment in which collaboration can emerge and grow is created. And this is the beginning of the story of a new paradigm shift which might have or not, depending a lot on communication and marketing experts, a huge impact on our future.
To conclude, such an ecosystem does not only provide immense opportunities for technological innovation and new business models but also represents an extremely rich environment for identifying and studying new interaction paradigms and forms of value (co-)creation. The stakeholders interact with information and with technologies in new ways that have yet to be identified and understood, the challenges that need to be embraced by the communication and marketing specialists in the near future.
This work is carried out within the framework of the research project Tourism 4.0—enriched tourist experience (OP20.03536), co-funded by the Slovenian Ministry of Education, Science and Sport and the European Regional Development Fund. The consortium is led by Arctur company and includes top experts from three Slovenian universities: the Faculty of Tourism Studies—Turistica (University of Primorska), the Faculty of Tourism (University of Maribor) and the Faculty of Computer and Information Science (University of Ljubljana). We would also like to acknowledge the ongoing support from the Association of Municipalities and Towns of Slovenia (Skupnost občin Slovenije—SOS).
The authors do not declare any conflict of interest.
The authors of this chapter would like to thank Dejan Šuc for providing the graphic designs.
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