Carsten Habenicht
email: habenicht@ifw-dresden.de

Lorenzo Sponza

Roman Schuster

Martin Knupfer

Bernd Büchner

Mapping of the energetically lowest exciton in bulk 1T-HfS2

come

I. INTRODUCTION

HfS 2 is one of the lesser researched members of the family of quasi-two-dimensional, semiconducting transitionmetal dichalcogenides (TMD) which, after the discovery of graphene, have attracted attention due to their interesting electronic, optical and mechanical properties. The crystals grow in parallel stacked layers held together by rather weak Van-der-Waals forces. Each individual layer is made up of a slab of hafnium atoms sandwiched between two layers of sulfur atoms bound by comparatively strong ionic-covalent bonds. For this work, the 1T polytype was used in which the six nearest-neighbor sulfur atoms assume an octahedral coordination around a hafnium atom (space group: P3m1, D 3 3d ) [1][2][3][4]. So far, practical applications of the material have been limited to experimental field-effect [5] and photo [6] transistors. Theoretical considerations also suggest its usefulness in optoelectronic devices [7] and as photocatalyst for water splitting [8]. Computational methods [9][10][11][12][13] were employed to predict some of the characteristics of HfS 2 and it has been investigated by techniques such as Raman spectroscopy [5,6,14,15], optical spectroscopy [2,4,9,[16][17][18], photoemission spectroscopy [19][20][21][22], transport measurements [3,23] and x-ray diffraction [1,3]. However, the extent of current experimental research is significantly smaller compared to other layered semiconductors and a variety of its properties are still waiting to be studied. Based on our knowledge, there has been no systematic examination of the occurrence of ex-citons in HfS 2 . An earlier electron energy-loss study was unable to resolve this type of excitation [24]. Moreover, since the electronic structure of hafnium disulfide differs significantly from that of more extensively researched transition metal dichalcogenides, such as 2H-MoS 2 and 2H-WSe 2 [25][26][27][28][29][30][31][32][33][34][35], findings from studies of excitonic phenomena in the latter materials cannot be applied to HfS 2 . Consequently, a new, comprehensive investigation is necessary to obtain a fundamental understanding of the nontrivial excitonic transitions in this material. Our study presents the exciton's dispersion, effective mass, binding energies, transition origins within the Brillouin zone and an in-plane real space picture of the bound two particle system.

II. EXPERIMENTAL AND NUMERICAL ANALYSIS METHODS

We used transmission electron energy-loss spectroscopy (EELS) to map the dispersion of the excitons. EELS is an electron scattering technique whose doubly differentiated cross section is proportional to the loss function

L(q, ω) = Im - 1 (q, ω) = 2 (q, ω) 2 1 (q, ω) + 2 2 (q, ω) (1)
where 1 (q, ω) and 2 (q, ω) represent the real and imaginary parts of the momentum q and energy ω dependent dielectric function (q, ω) [36,37]. The method is well suited to investigate the momentum-dependence of electronic excitations in semiconductors [33,[38][39][40][41][42][43], such as excitonic transitions or collective excitations. We applied this technique to the 1T polytype of bulk single crystal hafnium disulfide, purchased from HQ Graphene. The material was exfoliated ex situ up to a thickness of approximately 100 nm using adhesive tape. The film thickness was estimated by comparing the transparency and color of the cleaved films under an optical microscope with those of samples characterized by AFM. The chosen thickness was a trade-off between the count rate, which decreases with the number of layers, and the negative effects of multiple scattering, which intensify with the layer count. The films were put on platinum transmission electron microscopy grids, placed in the transmission electron energy-loss spectrometer and aligned in situ relative to the probe electron beam based on electron diffraction. The diffraction patterns were also used to verify the quality of the crystal structure. The purposebuilt spectrometer [37,44] has a primary electron energy of 172 keV and an energy and momentum resolution of ∆E = 82 meV and ∆q = 0.04 Å -1 , respectively. It is equipped with a helium flow cryostat. The measurements were performed at 20 K to reduce thermal broadening.

To determine if the electron beam caused damage to the sample, diffraction patterns and energy-loss spectra with low momentum transfer values were measured repeatedly and found to be insensitive to the beam exposure. Electron energy-loss spectra between 0.2 eV and 7 eV were measured for momentum transfer values q parallel to ΓK (ΓM) ranging from 0.1 Å -1 through 1.3 Å -1 (1.0 Å -1 ) in 0.1 Å -1 increments (see Fig. 2). Based on the experimentally determined diffraction patterns, the distances between the Γ and K points as well as the Γ and M points were determined to be 1.15 Å -1 and 1.0 Å -1 , respectively. Those numbers deviate by not more than 0.5% from the values derived from the crystal parameters measured by other authors [1][2][3].

The loss spectra reflect the superposition of all EELS responses corresponding to a chosen momentum transfer vector -that is, a particular momentum difference and direction -across the whole scattering plane, which, in our setup, is parallel to the in-plane crystal layers of the real and reciprocal lattice. Consequently, the spectral features cannot be associated with specific points in the band structure solely based on the momentum transfer value. Moreover, an interpretation based on the band structure alone does not provide information about oscillator strengths, exciton binding energies and their momentum dependence. Therefore, theoretical simulations were used to verify the excitonic nature of the observed excitations and their properties. The same approach has been used successfully to analyze the excitonic phenomena in h-BN [45]. We employed the ABINIT simulation package (a plane wave code [46]) to compute the band structure according to density functional theory (DFT) using the Perdew-Burke-Ernzerhof (PBE) approximation for the exchange-correlation potential with Troullier-Martins pseudopotentials (with 4 electrons per hafnium atom and 6 electrons per sulfur atom). The lattice parameters of the simulated 1T -HfS 2 crystal were a = 3.63 Å and c a = 1.61. The density has been computed on a 11 x 11 x 5 Γ-centered grid with a cutoff energy of 50 Ha. These and the subsequent Bethe-Salpeter computations of the dielectric functions were performed without spin-orbit coupling because its inclusion had a negligible effect on the band dispersion (see Appendix A for details). The optical properties have been computed with the EXC code [47] in two ways: within the Random Phase Approximation neglecting local fields and by solving the Bethe-Salpeter equation. In either case, three valence and three conduction bands have been included and the Brillouin zone was sampled with a 36 × 36 × 4 Γ-centered k-mesh. A cutoff energy of 50 eV defines the dimension of the polarizability matrix, and 100 eV is the cutoff setting the size of the basis to represent the independent particle (IP) matrix elements. The static screening entering the electron-hole interaction has been computed in the Random Phase Approximation with local fields including 100 bands and using a cutoff of 270 eV for the matrix dimension and 135 eV for the wave-functions. Due to the typical underestimation of the gap at the DFT level [48], a rigid shift of the empty states (scissor operator) of 1.4 eV was used to account for the quasiparticle correction. More elaborate GW corrections required heavier pseudopotentials [49] which would have increased the computational workload significantly. The scissor correction was determined by comparing the position of the energetically lowest lying exciton in the calculated and experimental loss spectra. The theoretical spectra were convoluted with a Gaussian of width 0.04 eV. The loss functions for the ΓM and ΓK directions were computed from the real and imaginary parts of (q, ω) according to Equ. 1 (see Fig. 3).

Solving the BSE expressed in a basis of IP transitions, the spectral intensity I λ (q)

I λ (q) = k,v,c ρkvc (q)A qvc λ 2 = k,v,c M kvc λ (q) 2 (2)
of the λ-th exciton can be expressed as the squared modulus of a linear combination of all the dipole matrix elements ρkvc (q) between the occupied (also referred to as valence or initial) states v, k⟩ and the empty (also referred to as conduction or final) states c, k + q⟩ in the dipole approximation with the same q . The coefficients of the linear combination A qvc λ are IP components of the excitonic wave function. M kvc λ (q) can be used to visualize the individual particle transitions contributing to the formation of the excitonic spectral features and, thereby, allowing the identification of the origins of the excitonic transitions in reciprocal space. This approach is described in more detail in Ref. [45]. Individual particle transition maps, which plot log( ∑ v,c M kvc λ (q) I λ (q) ) for k and k+q in k-space, depict the involved valence and conduction states, respectively.

III. RESULTS AND DISCUSSION

The DFT band structure is presented in Fig. 1. The valence band maximum exhibits only a weak dispersion close to the Γ point of the Brillouin zone. The absolute conduction band minimum is at the M point yielding an

K Γ M K -2 0 2 4
Energy (eV)

Figure 1: (Color online) DFT band structure calculated without spin-orbit coupling for bulk 1T -HfS 2 before (black solid lines) and after (red dashed lines) applying the scissor operator.

indirect DFT band of 0.84 eV (2.24 eV after applying the scissor operator). There are also less pronounced minima at the Γ point and roughly halfway between Γ and K. The direct DFT gap at Γ is 1.61 eV (3.01 eV with scissor operator). Pronounced valence band maxima and conduction band minima at the same position in reciprocal space, a feature that gives rise to the direct excitonic transitions in MoS 2 and WSe 2 around their K points, are missing. As a result, HfS 2 exhibits electronic properties that are very distinct from those of the aforementioned two substances.

A. Loss Spectra

The EELS spectra measured with q parallel to the ΓK and the ΓM direction are depicted in Fig. 2 (a) and (b), respectively. The graph for q = 0.1 Å -1 parallel to ΓK shows an energy gap up to approximately 2.6 eV followed by a peak at 2.84 eV. This feature disperses to lower energies upon increasing the momentum transfer until it reaches 2.45 eV at q = 0.7 Å -1 . For larger q , the peak shifts back to higher energies and the peak intensity grows. The BSE loss spectra (Fig. 3 (a)) mirror this behavior of the energetically lowest peak. In contrast, the features do not exist in the spectra derived from the Random-Phase-Approximation (see Appendix B), which ignores electron-hole interactions, confirming that the peaks are of excitonic origin. The plots of the energy peak positions vs. the momentum transfer values extracted from the spectra are plotted in Fig. 4 (a). The experimental and computed parabolic curvatures display a remarkable degree of agreement. The lowest energy position resulting from the calculations is at 0.67 Å -1 . The measured loss spectra for q || ΓM (Fig. 2 (b)), reveal that the exciton peak monotonically red-shifts down to 2.17 eV as q increases from 0.1 Å -1 through 1.00 Å -1 . As before, the computed spectra (Fig. 3 (b)) follow the experimentally found trend closely (Fig. 4 (b)).

The experimentally determined dispersion of the exciton in Fig. 4 (a) was fitted using the effective mass approximation [50] (EMA) to estimate the effective exciton mass m * from the experimental energy E (q ) and momentum values around the momentum q v associated with the energy minimum E (q v ):

E(q) = E(q v ) + ̵ h 2 2m * (q -q v ) 2 .
(

) 3
The fitted EMA function is plotted in Fig. 4 (a) as a dashed gray line with E (q v ) = 2.46 eV, q v = 0.70 Å -1 and an effective exciton mass of 3.75 m 0 (m 0 represents the electron mass). The dispersion curvature derived from the BSE simulation leads to slightly smaller effective mass of 3.16 m 0 .

B. Exciton Transition Origins

IP-transition maps were produced for three exemplary momentum transfer values parallel to the ΓM (Fig. 5) and ΓK (Fig. 6) directions in the ΓKM plane [START_REF]) for a depiction of the Brillouin zone and the labeling conventions[END_REF], respectively. Because equivalent plots for the AHL plane [START_REF]) for a depiction of the Brillouin zone and the labeling conventions[END_REF] showed no relevant differences compared to the ones for the ΓKM plane, they are not presented in this work. The locations in the maps with the highest log( ∑ v,c M kvc λ (q) I λ (q) ) values add the most to the formation. Those are the valence and conduction states where the exciton's hole and electron are located, respectively.

For the momentum transfer values parallel to the ΓM direction, the q -orientation in the transition maps is 30°c ounterclockwise from the positive horizontal axis as indicated by the black arrows in the maps depicted in Fig. 5. For q = 0.17 Å -1 , the most intense occupied states are 0.28 Å -1 away from the Γ point along the ΓM high symmetry line parallel to q (Fig. 5 (a)). The predominant conduction states are along the same path a distance of 0.44 Å -1 away from the center of the Brillouin zone (Fig. 5 (b)). In Fig. 5 (c), that location information is transferred to a band structure plot to also visualize the approximate energy positions of the transition. For clarity, the transition information in reciprocal space are also summarized in the inset in Fig. 5 (c). For increasing momentum transfer values, the relevant valence states advance towards the Γ point and the conduction states to M (Fig. 5 (d) and(e)) until the exciton bridges the indirect quasi-particle gap between Γ and M for q = 1.00 Å -1 (Fig. 5

(g) and (h)).

The formation of the exciton with q parallel to the ΓK direction is comparatively more complicated. For small exchanged momenta (q = 0.29 Å), the major contributing occupied states are along the ΓK high symmetry lines adjacent to the one parallel to q (Fig. 6 (a)). In contrast, the predominant conduction states are approximately midway along the neighboring ΓM lines (Fig. 6 (b)). As before, Fig. 6 (c) shows the projection of the transition information onto the band structure and the Brillouin zone. At q = 0.67 Å -1 , the calculated dispersion reaches its minimum energy value. The valence states for that momentum originate from the ΓM lines perpendicular to q (Fig. 6 (d)) while the unoccupied states shift closer to the M points (Fig. 6 (e)).

For q = 1.15 Å -1 || ΓK, the major contributions from the valance states continue to move another 30°to roughly the center the following ΓK symmetry lines (Fig. 6 (g)). The related occupied states shift approximately to the KM lines 0.1 Å -1 away from the M point (Fig. 6 (h)). The energy difference between those states decreases again leading to the parabolic dispersion curve.

C. Binding Energies and Exciton Size

Another interesting property of the excitons is their binding energy E B and its dependence on momentum. A priori, it cannot be assumed that this energy difference between the onset of the single particle continuum and 0 0.25 0.5 0. the exciton's quasi-particle band is constant in momentum space. For example, it has been shown that the curvature of the exciton dispersions in h-BN can be different from that of the single particle transitions leading to momentum dependent changes in the binding energy [45]. To determine that as a function of q , we compared the energy-momentum dispersion of the IPtransitions with that of the energetically lowest feature of the imaginary part of the dielectric function calculated from the BSE. The resulting dispersion relations are depicted in Fig. 7. For q || ΓK, the binding energy varies between 0.14 eV and 0.17 eV. The values for ΓM are similar and range from 0.13 eV through 0.17 eV. This shows that the binding energies in HfS 2 are quite momentum independent. For comparison, the reported values for the energetically lowest excitons in bulk 2H-MoS 2 and 2H-WSe 2 are more than two times lower, that is, 0.05 eV -0.06 eV [START_REF] Beal | [END_REF][53][54][55].

The binding energies lead to the question whether we are dealing with a rather localized Frenkel exciton or a delocalized Wannier-Mott exciton. To picture the spatial extent and character of the bound electron-hole pair in greater detail, we determined the excitonic wave function from the diagonalized BSE for q = 0 Å -1 . The calculation was done as a function of the electron position with the hole localized on a sulfur atom. This assumption is justified because the top valence band is largely made up of sulfur p-states. The outcome of the simulation is presented in Fig. 8. The electron distributions are clustered exclusively around the hafnium atoms even though sulfur has empty 4p states that could hold additional electrons. The reason is that p → p transitions are prohibited in the dipole approximation so excitations from the top valence band, which is largely of p-character, will populate mainly hafnium d-states instead of sulfur p-states. More importantly, the electron density for the bound electron is highest around the hafnium atoms surrounding the location of the hole at a distance of up to 1 to 2 in-plane lattice constants and, consequently, the exciton size exceeds that of a primitive cell. This observation agrees with the rough estimate of the real space size of the exciton r λ=1 according to the three-dimensional Mott-Wannier model [55]:

r λ=1 = e 2 2E B √ ∥ (4)
Based on our calculations, the binding energy is 170 meV for the direct transition at the Γ point. The out-of-plane and in-plane dielectric constants were computed by other authors to be = 5.8 and ∥ = 10.4, respectively, for the bulk material [56]. Consequently, the exciton radius is on the order of 5 Å which is somewhat in excess of the in-plane lattice constant a ≈ 3.6 Å [1-3]). For comparison, the radius in bulk 2H-WSe 2 is ∼ 18 Å. The larger size is consistent with the smaller binding energy of 55 meV ( [55]) in tungsten diselenide suggesting that the dielectric screening is weaker in HfS 2 . However, besides the mentioned core probability distribution around the hole, there is also noticeable electron probability density adjoining the hafnium atoms along the six directions originating from the sulfur atom accommodating the hole and running perpendicular to the sides of the Wigner-Seitz cell surrounding that atom. The radius of those extensions is approximately 20 Å. In contrast, the atoms along the lines passing though the corners of that primitive cell show hardly any electron probabilities associated with the exciton beyond the span of two lattice constants. This star-like shape indicates a mixed Frenkel-Wannier character of the exciton in the plane, which can be rationalized by close inspection of the band structure. The direct transitions are strongest in the vicinity of the Γ point. In that region, the valence band is largely flat (see inset in Fig. 8). The same is true for the conduction band in the ΓM direction so the exciton is localized (Frenkel-type) along this path. In contrast, the conduction band in the ΓK direction has a larger curvature making the exciton more delocalized (Wannier-like). Because the orientation of the reciprocal hexagonal primitive cell is rotated by 30 ○ relative to the one in real space, the in-plane real space extent of the exciton is larger for directions emanating from the hole through the centers of (a) q = 0.17 (c) q = 0.17 ) (i) q = 1.00 1/ Å Energy (eV) the calculations where log( ∑ v,c M kvc λ (q) I λ (q) ) = 0. The gray lines trace the high symmetry lines. The orientation and length of the black arrows beginning at the center of the plots symbolize the direction and magnitude of the momentum transfers employed in the computations. The plots in the third column depict the DFT band structure after applying the scissor operator and the IP-transitions derived from the IP-transition maps for the indicated momentum transfer values parallel to ΓM. The insets show the same transitions in the ΓKM plane of the Brillouin zone.

K Γ M -1 0 1 2 3 Γ K K' M M' (f) q = 0.56 1/ Å Energy (eV) (g) q = 1.
the sides of the Wigner-Seitz cell as observed in Fig. 8.

IV. SUMMARY

For the first time, we have been able to identify the energetically lowest excitons in bulk 1T -HfS 2 . The electron energy-loss spectra reveal a parabolic exciton dispersion with an energetic minimum at 0.7 Å -1 for momentum transfer values parallel to the ΓK direction. Based on this, we calculated an effective exciton mass of 3.75 m 0 . In contrast, the dispersion in the ΓM direction decreases monotonically with increasing momentum exchange up to 1.00 Å -1 . To further analyze the data, we calculated the loss functions along the same directions by solving the ab initio Bethe Salpeter equation. The experimental and computational results matched very well. The momentum dependent binding energies were determined to be between 130 meV and 170 meV and the exciton and IP dispersions are very similar. In addition, the excitonic wave function was presented and exposed the mixed Frenkel-Wannier character of the exciton in the crystal planes. Moreover, we studied how the IP-transitions combine to form the excitons and found that the excitations with momentum parallel to the ΓM high symmetry line transition along that path. In contrast, the picture for the exciton with q || ΓK is more complex. Its point of origin moves across a number of high symmetry lines as the momentum transfer changes while transitions end more and more away from the same ΓM line. This work is a further example in which the analysis of the combination of the involved IP-transitions helped reveal nontrivial exciton properties and, therefore, showed that it is a powerful tool for the interpretation of excitonic processes. The DFT band structure calculations presented earlier in this work were performed without including spinorbit coupling. To evaluate the effect of spin-orbit coupling, we also calculated the band structures with and without spin-orbit coupling using Hartwigsen-Goedecker-Hutter (HGH) pseudopotentials instead of Troullier-Martins (TM) pseudopotentials and an energy cutoff of 70 Ha. Employing different pseudopotentials was necessary because the BSE simulation could not be launched from HGH data due to technical input-output issues.

As can be seen in Fig. 9, including the spin-orbit coupling has only a negligible effect on the band dispersion. Therefore, it was not required to include it in the Bethe-Salpeter computations. Moreover, the band dispersion is not significantly affected by the use of TM pseudopotentials compared to the HGH pseudopotentials (see Fig. 9). The difference in the band gap produced by the two approaches is irrelevant because of our choice to use the scissor operator.

B. APPENDIX: COMPARISON OF LOSS SPECTRA BASED ON THE BETHE-SALPETER EQUATION AND THE RANDOM-PHASE-APPROXIMATION

To verify that the energetically lowest peaks in the energy-loss spectra obtained from the BSE calculations are of excitonic nature, the spectra were also computed based on the Random-Phase-Approximation (RPA). The RPA neglects the electron-hole interactions that give rise to excitons. Therefore, excitonic phenomena will not be reflected in its results. A comparison of the outcomes of the two methods is presented in Fig. 10 showing that the features in question are not present in the RPA-based loss spectra confirming that they are of excitonic origin.

Figure 2 :

 2 Figure 2: (Color online) Electron energy-loss spectra for bulk 1T -HfS 2 measured at 20 K with q parallel to the a) ΓK and b) ΓM directions for the momentum transfer values indicated in the plots. The solid spectral lines represent the binomially smoothed data (smoothing factor 1) and the black dotted lines signify the measured data points. The measured intensities were normalized at 5 eV and subsequently offset for clarity. The dashed, black lines serve as a guide to the eye to identify the energetically lowest exciton.

Figure 3 :

 3 Figure 3: BSE simulation of the loss functions for bulk 1T -HfS 2 and q parallel to the (a) ΓK and (b) ΓM directions blue-shifted by 1.4 eV. The plots are offset along the intensity axis for clarity. The dashed, black lines serve as a guide to the eye to identify the energetically lowest exciton.

Figure 4 :

 4 Figure 4: Comparison of the dispersion of the energetically lowest energy-loss peak based on EELS measurements (×) and BSE simulations (○) for q parallel to the (a) ΓK and (b) ΓM directions. The gray dotted line in (a) represents the EMA fit of the EELS data.

Figure 5 :

 5 Figure 5: (Color online) The first and second show the IP-transition maps for the valence and conduction states, respectively, for the energetically lowest exciton (λ = 1) and the indicated momentum transfer values parallel to the ΓM direction in the ΓKM plane. The black dots outlining the Brillouin zone represent the k -points used inthe calculations where log( ∑ v,c M kvc λ (q) I λ (q) ) = 0. The gray lines trace the high symmetry lines. The orientation and length of the black arrows beginning at the center of the plots symbolize the direction and magnitude of the momentum transfers employed in the computations. The plots in the third column depict the DFT band structure after applying the scissor operator and the IP-transitions derived from the IP-transition maps for the indicated momentum transfer values parallel to ΓM. The insets show the same transitions in the ΓKM plane of the Brillouin zone.

Figure 6 :

 6 Figure 6: (Color online) Same as Fig. 5 but for q || ΓK.

Figure 7 :

 7 Figure 7: Energy-momentum dispersions of the IP-transitions (○) and the energetically lowest exciton (×) for q parallel to the (a) ΓK and (b) ΓM directions. All points have been blue-shifted by 1.4 eV.

Figure 8 :

 8 Figure 8: (Color online) Modulus of the exciton wave function (depicted in red) of the energetically lowest exciton in bulk HfS 2 for q = 0 Å -1 . The hole is located on a sulfur atom (black sphere) in the center of the image. The green hexagon represents the hexagonal primitive cell (Wigner-Seitz cell). The inset shows the DFT band structure around the Γ point and the location of the direct excitonic transition associated with the wave function.

Figure 9 :Figure 10 :

 910 Figure 9: (Color online) (a) DFT band structure for bulk 1T -HfS 2 with (red dashed line) and without (black solid line) including spin-orbit coupling using Hartwigsen-Goedecker-Hutter pseudopotentials. The blue dotted line represents the band structure without considering spin-orbit coupling using Troullier-Martins pseudopotentials. Panels (b) and (c) show close-ups of the conduction and valance bands around the Γ point. Panel (d) depicts the Brillouin zone associated with the material.

Acknowledgments

We thank R. Hübel, S. Leger and M. Naumann for their technical assistance. R. Schuster and C. Habenicht are grateful for funding from the IFW excellence program. L. Sponza acknowledges the European H2020 Framework Program under Grant agreements No. 696656 Graphene Core 1 and No. 785219 Graphene Core 2 for the funding received.


David Harborth
email: david.harborth@m-chair.de

Sebastian Pape
email: sebastian.pape@m-chair.de

Privacy Concerns and Behavior of Pokémon Go Players in Germany

Keywords: privacy concerns, augmented reality, Pokémon Go, concerns for information privacy (CFIP), privacy calculus, privacy behavior

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The location-based augmented reality (AR) smartphone game Pokémon Go (cf. Fig. 1) is amongst the most successful smartphone applications of all time and led to a major increase in public awareness about AR [START_REF] Kh | Augmented Reality Gets Boost From Pokemon Go[END_REF][START_REF] Nicas | Augmented Reality Gets Boost From Success of Pokémon Go[END_REF]. The game has broken several records [START_REF] Swatman | Pokémon Go catches five new world records[END_REF] and it was shown that its users develop a strong attachment to the game [START_REF] Nedelcheva | Analysis of Transmedia Storytelling in Pokémon GO[END_REF]. Pokémon Go poses relatively strong privacy threats compared to other smartphone applications, due to the AR functionalities and the location-based nature. There is almost no research on privacy issues with regard to AR technologies [START_REF] Harborth | Augmented Reality in Information Systems Research: A Systematic Literature Review[END_REF]. Thus, we investigate privacy concerns about organizational information privacy practices and privacy-related behaviors of active Pokémon Go players in Germany. Three research questions arise: First, are privacy concerns a relevant issue for Pokémon Go players and do they differ in magnitude between different groups of players? Second, is there a relationship between the different dimensions of privacy concerns and the actual use behavior? Third, what are active players doing to protect their privacy on their smartphone? Fig. 1: Pokémon Go on iOS [START_REF] Bbc | Pokemon and the power of nostalgia[END_REF] The success of Pokémon Go [49] allows it to address these questions for an AR technology based on a large scale user study for the first time. Understanding the heterogeneous perceptions on privacy is necessary since many experts predict that AR will become one of the next big technological innovations with a massive market potential [START_REF] Bellini | Virtual & Augmented Reality: Understanding the race for the next computing platform[END_REF][START_REF] Hyman | Augmented-Reality Glasses Bring Cloud Security Into Sharp Focus[END_REF]. Privacy aspects are especially important for AR because of its pervasiveness associated the advancements of wearable AR technologies (e.g. head-mounted displays). This leads to a situation where the user is continuously provided with context-sensitive information about her or his environment [START_REF] Grubert | Towards Pervasive Augmented Reality: Context-Awareness in Augmented Reality[END_REF]. This, in turn, makes it necessary to continuously gather and process all kinds of data. Privacy violations can happen to actual users of a system -due to the increasing collection of several different data types [START_REF] Hyman | Augmented-Reality Glasses Bring Cloud Security Into Sharp Focus[END_REF] -or to the users' direct environment. The case of the social environment could be observed in the past for Google Glasses with several reports about angry civilians who had the feeling of being filmed by the wearer and bars which prohibited entry when wearing the glasses. This partly led to the failure of the device in the consumer market [48]. This case emphasizes the need to understand privacy concerns and behaviors of users in respect to AR technology even more. We investigate the privacy concerns based on a sample of 683 active players of Pokémon Go in Germany. The results indicate that privacy concerns are relatively strong throughout different demographic groups (cf. Table 1). This is a surprising result, considering that the participants are all active players of the game. Thus, in the second stage of the research, a second online survey with 199 participants is conducted to figure out specific measures how players protect their privacy.

The remainder of the paper is structured as follows. A brief background on Pokémon Go, AR and related work on privacy is given in Sect. 2. The methodology is described in Sect. 3 and the results are presented in Sect. 4. In Sect. 5 results and their limitations are discussed. Section 6 concludes this work.

Theoretical Background

In the following part, we provide theoretical background on Pokémon Go, augmented reality and the current literature on privacy.

Pokémon Go and Augmented Reality

Pokémon Go [3] is a location-based augmented reality (AR) smartphone game developed by Niantic, a former Google owned company [3,30]. Many people see Pokémon Go as the unofficial successor of Ingress [START_REF] Gregory | Ingress: A Game, Lifestyle and Social Network in One![END_REF], another location-based smartphone game, also developed by Niantic. Up to now, no homogeneous opinion, of whether Pokémon Go matches all criteria of AR is formed. However, there is broad agreement that it is a first important step towards AR [START_REF] Evans | Pokémon Go May Not Truly Be Augmented Reality, and That's OK[END_REF][START_REF] Gstoll | A love letter from augmented reality to Pokémon Go[END_REF][START_REF] Lang | Pokémon Go' is Where I Draw the Line on "Augmented Reality[END_REF][START_REF] Mason | Pokemon Go or: How I Learned To Stop Worrying About The Definition And Love Augmented Reality[END_REF]. Thus, we approach Pokémon Go as an AR application for the course of our research.

AR is defined in multiple ways, whereas the definition by Azuma et al. [4, p. 34] provides a comprehensive understanding of the technology. They define AR in a way that "[...] an AR system [...] combines real and virtual objects in a real environment; runs interactively, and in real time; and registers (aligns) real and virtual objects with each other". The differentiation towards virtual reality (VR) is currently not always done in the public discussion. Milgram et al. [START_REF] Milgram | Augmented Reality: A class of displays on the reality-virtuality continuum[END_REF] illustrate the dimensions of mixed reality (MR) based on a x-axis (cf. Fig. 2). Based on this, it is important to distinguish whether the environment is real (AR) or virtual (VR). Up to now, research on AR mainly focused on technical aspects and not on the user behavior [START_REF] Harborth | Augmented Reality in Information Systems Research: A Systematic Literature Review[END_REF][START_REF] Swan | Survey of User-Based Experimentation in Augmented Reality[END_REF]. Since AR is expected to be one of the upcoming technologies [START_REF] Bellini | Virtual & Augmented Reality: Understanding the race for the next computing platform[END_REF], it is important to investigate user behavior and privacy issues.

Privacy and the Privacy Paradox

The definition of privacy in the literature consists of a variety of different perspectives [START_REF] Bélanger | Privacy in the Digital Age: A Review of Information Privacy Research in Information Systems[END_REF][START_REF] Smith | Theory and Review Information Privacy Research: An Interdisciplinary Review[END_REF]. An often cited definition is given by Warren and Brandeis in 1890. They say that privacy is "the right to be left alone" [47]. In the context of this paper, the privacy definition provided by Culnan [10, p. 344] is used, where "privacy is the ability of an individual to control the access others have to personal information". The notion of control plays a crucial role in the privacy literature [START_REF] Belanger | Trustworthiness in electronic commerce: the role of privacy, security, and site attributes[END_REF] and for the concerns about organizational practices. Thus, we choose this definition for our research context. Previous literature shows that privacy concerns play an important role for the usage of internet services. For example, Tang et al. [START_REF] Tang | Gaining Trust Through Online Privacy Protection: Self-Regulation, Mandatory Standards, or Caveat Emptor[END_REF] argue that retailers can improve privacy and trust if they send clear signals that they will protect the privacy of the customers. Culnan and Armstrong [11, p. 107] provide a framework where users provide their personal data willingly, if they perceive the firm's information processes as "fair". Fair means in this context that these processes "provide individuals with control over the disclosure and subsequent use of their personal information". Differences in privacy behavior with regard to demographics [START_REF] Culnan | How Did They Get My Name?": An Exploratory Investigation of Consumer Attitudes Toward Secondary Information Use[END_REF][START_REF] Culnan | Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation[END_REF][START_REF] Bartel | An Investigation of Gender Differences in On-line Privacy Concerns and Resultant Behaviors[END_REF][START_REF] Tschersich | On Gender Specific Perception of Data Sharing in Japan[END_REF] and cultural differences [START_REF] Dinev | Internet Users' Privacy Concerns and Beliefs About Government Surveillance: An Exploratory Study of Differences Between Italy and the United States[END_REF] are investigated in previous literature as well. But it is shown that the majority of literature focuses on student samples based in the United States [START_REF] Bélanger | Privacy in the Digital Age: A Review of Information Privacy Research in Information Systems[END_REF]. In summary, it can be stated that privacy plays an important role for the usage of online services. However, it is important to mention that "privacy concern is only one of a number of factors affecting Internet and e-services use" [16, p. 51].

Closely related to the discussion on privacy concerns and behavior is the so called "privacy paradox". This phenomenon describes the divergence of the actual behavior of users compared to the stated attitudes when dealing with privacy issues [START_REF] Acquisti | Privacy and rationality in individual decision making[END_REF][START_REF] Berendt | Privacy in e-commerce[END_REF][START_REF] Norberg | The Privacy Paradox: Personal Information Disclosure Intentions versus Behaviors[END_REF][START_REF] Spiekermann | E-privacy in 2nd Generation E-Commerce: Privacy Preferences versus actual Behavior[END_REF]. The privacy paradox is a well-known topic in information systems research. Spiekermann et al. [START_REF] Spiekermann | E-privacy in 2nd Generation E-Commerce: Privacy Preferences versus actual Behavior[END_REF] experimentally show that participants reveal a multitude of information, which enables providers to construct a detailed profile during an online-shopping tour, although they stated to be concerned about their privacy before the experiment. The paper by Berendt et al. [START_REF] Berendt | Privacy in e-commerce[END_REF] is built on the previously mentioned paper and shows the existence of the privacy paradox by using an e-commerce experiment, too. Acquisti and Grossklags present another point of view on the privacy paradox. They show that while people have high standards regarding their privacy attitudes, their decision process is influenced by psychological factors like "incomplete information, bounded rationality and systematic psychological deviations from rationality" [1, p. 29]. These limitations lead to a trade-off of "long-term privacy for short-term benefits" [1, p. 24]. Norberg et al. [START_REF] Norberg | The Privacy Paradox: Personal Information Disclosure Intentions versus Behaviors[END_REF] investigate why this divergence of actual behavior and attitudes towards privacy exists. The results support the hypothesis that risk perceptions of users when disclosing personal information influence the intentions to provide personal information. However, the second hypothesis about the effect of trust on the disclosure behavior could not be confirmed.

In summary, it is important to recognize that people do not always behave in the way they state that they would do. Therefore, all results that deal with attitudes of users, in particular regarding to privacy, have to be treated with caution if the goal of the research is to make valid statements for decision choices. Thus, we include the second survey on the actual privacy measures in this work.

Methodology

The methodology presents the design and data collection of the first and the second survey. The second survey on actual privacy related behavior was conducted after we conducted the first one on privacy concerns.

Questionnaire

Survey I We conducted the study with a German panel, thus all items had to be translated into German. As we wanted to ensure content validity of the translation, we followed a rigorous translation process [46]. First, the English questionnaire was translated into German with by a certified translator (translators are standardized following the DIN EN 15038 norm). Afterwards, the German version was given to a second independent certified translator who retranslated the questionnaire to English. This step was done to ensure the equivalence of the translation. Third, a group of five academic colleagues checked the two English versions with regard to this equivalence. All items were found to be equivalent. In a last step, the German version of the questionnaire was administered to students of a Masters course to check preliminary reliability and validity.

Privacy concerns with regard to organizational information privacy practices are represented by the variables pcollection, errors, unauthorized secondary use and improper access. As these variables cannot be measured directly (latent variables), they have to be operationalized in order to quantify the concerns via a user study. We choose the privacy constructs by Smith et al. [START_REF] Smith | Information Privacy: Measuring Individuals' Concerns About Organizational Practices[END_REF], as they are widely tested with regard to validity and reliability (cf. Stewart and Segars [START_REF] Stewart | An Empirical Examination of the Concern for Information Privacy Instrument[END_REF]). The constructs are built by calculating mean sum scores of the single items belonging to the respective construct (cf. Appendix A). Collection is defined as the concern of people that too much data about them is collected over time. Errors represent users' concerns about inaccurate or false personal data in databases. Unauthorized secondary use measures the concern that personal data is used for another purpose than initially disclosed without the user's authorization. Improper access captures concerns about unauthorized people having access to the user's personal data [39, p. 172].

Survey II The questionnaire of the second survey covers questions about actual privacy protecting measures, which active Pokémon Go players undertake. The questionnaire contains the same demographic questions about age, gender, education and smartphone experience, as well as actual use behavior of Pokémon Go as the first survey. The steps are derived from internet search [START_REF] Fitzgerald | Concrete Steps to Take to Minimize Risk While Playing Pokémon GO[END_REF], as well as valuable feedback from colleagues. Measure 1 (M1) asks about whether users turn off services that potentially collect location data. Measure 2 (M2) deals with the use of a separate e-mail address used only for games. Measure 3 (M3) specifies this furthermore and asks about the use of different e-mail addresses for games and social network sites (sns). Measure 4 (M4) asks the participants whether they review which applications can access other accounts (e.g. Facebook). Measure 5 (M5) asks whether users reset the advertising ID on their phone and measure 6 (M6) deals the camera access rights of Pokémon Go. All measures are formulated as statements and could be answered with "yes", "no", "sometimes" or "I don't know". The specific questions can be found in Appendix B.

Data Collection

Survey I In order to ensure high quality of the sample, a certified sample provider (certified with ISO 26362 norm) was employed to get access to their online panel for Germany. By focusing on German users of the game, we could address two potential problems. First, country-specific differences in privacy concerns are eliminated and controlled. Second, by focusing on one country, we could gather a relatively large data set. The survey itself was administered with LimeSurvey (version 2.63.1) [36]. The panel provider distributed the survey's link to 9338 participants until the aimed sample size of active players was reached. Of 9338 approached participants, 683 active Pokémon Go players remained, excluding participants who dropped out due to wrong answers to test questions and age restrictions (data was only collected for at least 18 year old participants). Table 1 presents summary statistics for this data set. Further information with regard to the demographics can be found in the paper by Harborth and Pape [?].

Survey II Since of the constructs in Survey I only provide information about attitudes and stated concerns, we wanted to consider actual use behavior with regard to privacy protecting measures. Because of anonymized answers in Survey I, we could not ask the same participants. In addition, we could not employ a panel provider as in Survey I due to limited resources. Thus, we created a very brief questionnaire (see Sect. 3.1) and administered it with LimeSurvey (version 2.63.1) [36]. We distributed the link of the online survey in three Pokémon Go Facebook groups (Germany, Frankfurt and Munich). All groups are closed groups with approximately 30,000 members altogether. The questionnaire was online for 4 days and 238 users started it, whereas only 200 participants finished it. One participant's answers were deleted, because he or she stated his/her age to be younger than 18 years. Table 2 presents the summary statistics for this data set.

Demographics

Survey I Table 1 shows that the median age is 32 years and that there is a larger share of women than men in the sample. Furthermore, the secondary school leaving certificate 1 and the A levels certificate 2 are the most common educational qualifications. With regard to these demographics, it can be argued that this data set represents the German population to an acceptable degree. The smartphone experience has a median of 6 years. The privacy constructs have all at least a median value of 5.5, implying that most players agree to the statements made in the constructs' items. The actual use frequency has a median of 5 which stands for playing "several times a week". Improper access has a median of 6.333. 3 Survey II The demographics of the second survey are slightly different to the first survey with regard to age, gender and smartphone experience (cf. Table 2). The participants of the second survey are 6 years younger with regard to the median age and there are slightly more men than women in Survey II. The users in Survey II have one year more smartphone experience. Although the users in both surveys say that they are active Pokémon Go players, the participants in Survey II state that they play Pokémon Go several times a day (median of 7) and players of Survey I state that they play it only several times a week.

1 German: "Realschulabschluss" 2 German: "Abitur" 3 This is possible because of the construct is calculated based on the mean sum score of three items (the other three concern constructs each consist of four items). Apparently, the participants acquired through Facebook groups are rather heavy users compared to the ones acquired with the help of a sample provider. A self-selection mechanism could explain this difference. In these Facebook groups, players exchange information about new offers, versions and places to hunt for special Pokémons. This is especially interesting for highly attached players, which would indicate that those are rather heavy users.

Results

We present the results of Survey I and Survey II in the following sections.

Survey I -Privacy Concerns

The variables collection, errors, unauthorized secondary use and improper access are not normally distributed while actual use is normally distributed according to the Shapiro-Wilk test for normality. In the first part of the empirical assessment, it is investigated whether users' privacy concerns and the actual use behavior (i.e. The threshold for age is not clearly determinable because there are two rationales for dividing the data which are highly interesting to investigate in the context of privacy. The first approach is a median split, a commonly used technique for forming categorical variables in statistics [START_REF] Henseler | Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures[END_REF]. This has the advantage of comparing two groups, similar in size, based on the actual median of the used data set. This results in the groups of participants aged 31 and younger and participants aged 32 and older. A categorical variable is created where 0 stands for participants aged 31 and younger and 1 for participants aged 32 and older. One group contains 341 participants and the second one 342. The second approach deals with the notion of "digital natives" (DN) versus "digital immigrants" (DI) [START_REF] Prensky | Digital Natives, Digital Immigrants[END_REF]. Since there is a vivid discussion on whether the notion of DN is substantial [START_REF] Helsper | Digital natives: where is the evidence?[END_REF], it is interesting in the context of privacy concerns to apply this threshold and investigate, whether it is true that DI are rather privacy sensitive and more concerned than the younger generation. A commonly named threshold for the oldest year of birth of a DN is 1980 [START_REF] Palfrey | Born Digital: Understanding the First Generation of Digital Natives[END_REF]. The resulting two groups contain 446 entries for DN (37 years old and younger) and 237 entries for DI. The median split approach is applied for experience since this is the most meaningful approach, with groups with smartphone experience less than or equal to 5 years and greater than or equal to 6 years. Education is divided into a group with participants without university degree (NU), comprising all participants whose highest educational qualification is the German Abitur (N= 487) and a group with 196 participants holding at least a Bachelor's degree (U). Table 3 summarizes the results for the statistical assessment of whether the group differences in mean values are statistically significant or not.

For collection there are only significant differences for the two different age groups. Users' perceptions of the errors construct differs between younger and older participants as well as between participants without and with university degree. Interestingly, the group comparison of unauthorized secondary use is different for the age groups. For the median split, there is no statistically significant difference in the evaluation between younger and older players, whereas there is one for the case of DN versus DI. The evaluation for this construct differs significantly between women and men. For the improper access construct, statistically significant differences are prevalent for age and gender. The variable actual use is homogeneous across the different characteristics of Pokémon Go players except for smartphone experience. The question of whether privacy concerns influence the use behavior is addressed in the second part of the empirical analysis. Due to the breakdown into four variables, it is possible to assess which kind of privacy concern exerts what kind of influence on use behavior. This question is addressed with a two-stage process (cf. Table 4). First, each privacy concern variable is treated as the independent variable in a simple linear regression model, with actual use behavior as the dependent variable. Second, a multiple regression model, containing all independent variables, is calculated in order to assess the effect of the different dimensions of privacy concerns on use behavior simultaneously.

The results of the regression analysis indicate that privacy concerns have no significant impact on the actual use behavior. Although there are statistically significant relationships, the effect sizes are rather small and therefore not relevant. In summary, the results about privacy concerns indicate that there are significant differences in the different dimensions of concerns between younger and older players. Furthermore, gender matters for two of the four dimensions. An additional regression analysis with the actual use frequency revealed no clear impact of privacy concerns, indicating that players might well be aware of privacy dangers and are concerned about it, but are still playing the game. Thus, although privacy is perceived as important, it does not affect the use of Pokémon Go. The game requires several more types of data compared to other smartphone applications due to its location-based and AR nature. This contrary result could be a case of the privacy paradox [START_REF] Norberg | The Privacy Paradox: Personal Information Disclosure Intentions versus Behaviors[END_REF], where people state that privacy is important for them, but act in the opposite way.

Survey II -Privacy Behavior

The distribution of the answers on the privacy protecting measures are illustrated in Fig. 3. The number of participants who turn off services that collect location data is approximately the same as those who do not and those who sometimes do it. 45% of the players use a separate e-mail address only for games and roughly the same number of players do not. The remaining users (10%) do it sometimes. The distribution for M3 (different e-mail addresses for games and sns) is almost the same as for M2. Interestingly, more than 65% of the Pokémon Go players review the access of applications to other accounts and only 10% do not. The remaining users undertake this measure sometimes. More than 80% of the users do not reset the advertising ID and only 10% do know about it at all. The majority of the players forbid Pokémon Go the camera access (60%) and 10% do it somestimes. The remaining participants do not forbid it.

Discussion

After we discussed the results of the two surveys independently of each other, we combine and interpret our results in the following part. After that, we discuss the limitations of our work.

Interpretation and Implications of the Results

As mentioned previously, there are significant differences in the different dimensions of the concerns between younger and older players. Furthermore, gender matters for two of the four dimensions. Gender differences in privacy concerns were also shown to be prevalent in past literature (e.g. [START_REF] Bartel | An Investigation of Gender Differences in On-line Privacy Concerns and Resultant Behaviors[END_REF][START_REF] Tschersich | On Gender Specific Perception of Data Sharing in Japan[END_REF]). For the age differences, the results indicate that relatively older players of Pokémon Go are more concerned. This is in line with the dominant notion of less concerned younger internet users compared to older users with a higher awareness. The regression analyses do not show any impact of the privacy concern dimensions on the actual use behavior of Pokémon Go. Based on this, it can be said that concerns about the privacy practices of organizations exist amongst players of the game and that they are heterogeneous with regard to the certain demographic characteristics. However, these users still play a game, which has access to several data types and processes them. This behavior is in line with the notion of the privacy paradox. However, this explanation becomes inconclusive, when looking at the actual privacy preserving measures of the players. It can be seen that, depending on the measure, approximately half of the participants of the second survey engage regularly or sometimes in doing these measures. A combination of these results imply that the majority of Pokémon Go players are aware about the risks (since they actively try to preserve their privacy with the measures tested) and almost all players are concerned. However, it seems that playing the game provides so much utility that they willingly agree to the implicit trade-off between playing the game and loosing a certain degree of control over their privacy. This explanation for an opposing behavior with regard to information privacy is known as the privacy calculus [START_REF] Dienlin | An Extended Privacy Calculus Model for SNSs: Analyzing Self-Disclosure and Self-Withdrawal in a Representative U.S. Sample[END_REF][START_REF] Dienlin | Is the privacy paradox a relic of the past? An in-depth analysis of privacy attitudes and privacy behaviors[END_REF][START_REF] Dinev | An extended privacy calculus model for e-commerce transactions[END_REF]. The privacy calculus is often mentioned in the literature as an explanation for the privacy paradox.

Limitations

The main limitations concern the sample characteristics. Both samples contain relatively more younger Pokémon Go players. With respect to this, the sample is not representative for the German population. This skewness might also be caused by the fact that digital games are played rather by younger users than older ones. In addition, our survey is only conducted with German players. Thus, the results could possibly differ from surveys conducted in other countries or cultural regions. But, this focus brings along advantages for this research (cf. Sect. 3.2), which can outweigh the limitations. Another limitation relates to the German translation of the English constructs. The constructs might have been understood differently by the participants than originally intended. This is always a possible threat when adapting original constructs from a language to another. The last limitation emerges due to the fact of analyzing two different samples. As described in Sect. 3.2, we could not ask the same participants from the first survey about their privacy preserving measures. Therefore, we are not able to compare the relationship between concerns and actual privacy behavior on an individual level. In addition, it is possible that people lie about their actual behavior in surveys. Thus, asking people what they actually do is also prone to errors that can hardly be controlled for in an online survey.

Conclusion and Future Work

We contributed to the literature on privacy and augmented reality in several ways. First, we contributed to the body of literature on information privacy by doing an empirical, not a normative work, that is based on a non-student sample with participants, who are not located in the United States [START_REF] Bélanger | Privacy in the Digital Age: A Review of Information Privacy Research in Information Systems[END_REF][START_REF] Smith | Theory and Review Information Privacy Research: An Interdisciplinary Review[END_REF]. Second, many studies on privacy only include perceptions and attitudes of users and no actual privacy-related behavior. By conducting the second survey, we are able to determine that relatively many Pokémon Go players act in a privacy-friendly way. By merging all the insights of both studies, we suggested that the majority of the Pokémon Go players are well aware of privacy risks and measures, but willingly trades-off benefits of the game against a higher level of privacy. Third, this research is one of the first to investigate privacy issues related to AR applications.

These insights indicate that users will abandon the protection of their privacy, if smartphone applications provide a level of utility that is high enough to outweigh the concerns. This conclusion is indepedent of the knowledge about and usage of privacy-preserving measures.

Future work should consider to include questions about privacy concerns and privacy measures in one questionnaire to include all three dimensions. First, the actual use behavior of the application. Second, the privacy concerns. Third, the actual privacy preserving measures undertaken by each participant (cf. Appendix B). In addition, our research on Pokémon Go and privacy could be conducted in other countries with different cultural values. This is especially interesting for the case of privacy perceptions and privacy preserving behavior. Another interesting dimensions is the investigation of this topic along different points in time. It could be investigated whether differences in the perception about Pokémon Go and associated privacy dimensions occur over time. Err2. Companies should take more steps to make sure that the personal information in their files is accurate. Err3. Companies should have better procedures to correct errors in personal information.

Err4. Companies should devote more time and effort to verifying the accuracy of the personal information in their databases.

Unauthorized Secondary Use USU1. Companies should not use personal information for any purposes unless it has been authorized by the individuals who provided the information. USU2. When people give personal information to a company for some reason, the company should never use the information for any other reason. USU3. Companies should never sell the personal information in their computer databases to other companies. USU4. Companies should never share personal information with other companies unless it has been authorized by the individuals who provided the information.

Improper Access IA1. Companies should devote more time and effort to preventing unauthorized access to personal information. IA2. Computer databases that contain personal information should be protected from unauthorized access -no matter how much it costs. IA3. Companies should take more steps to make sure that unauthorized people cannot access personal information in their computers.

Use Behavior

Please choose your usage frequency for Pokémon Go:

-Never -Once a month -Several times a month -Once a week -Several times a week -Once a day -Several times a day -Once an hour -Several times an hour -All the time The frequency scale is adapted from Rosen et al. [START_REF] Rosen | The Media and Technology Usage and Attitudes Scale: An empirical investigation[END_REF]. All other items are measured with a seven-point Likert scale, ranging from "strongly disagree" (coded as 1) to "strongly agree" (coded as 7). Male participants are coded as 0 and females as 1.

B Questionnaire II

Which of the following options are you using while playing Pokmon Go? M1. If possible, I turn off services that can be used to collect location data. M2. I have a separate e-mail address, which I only use for games. M3. I use different e-mail addresses for games and social networks. M4. I review, which apps are authorized to access my other accounts (Twitter, Google, Facebook). M5. I reset the advertising ID on my smartphone at least once per month. M6. I forbid Pokémon Go to have access to my smartphone camera and use it without the Augmented Reality functionality. The participants were able to answer the questions with "yes" (coded as 1), "no" (coded as 2), "sometimes" (coded as 3) and "I don't know" (coded as 4). In addition, the same questions as in Survey I were asked with regard to users' demographics and use behavior.

Fig. 2 :

 2 Fig.2: The Reality-Virtuality Continuum[START_REF] Milgram | Augmented Reality: A class of displays on the reality-virtuality continuum[END_REF]

Fig. 3 :

 3 Fig. 3: Relative frequency of the measures taken by Pokémon Go players

Table 1 :

 1 Descriptive Statistics Survey I (N=683)

		Mean Median Std. Dev. Min. Max.
	Age	34.539 32	(11.531) 18	66
	Gender	0.572 1	(0.495) 0	1
	Educational Qualification	3.977 4	(1.207) 1	7
	Smartphone Experience	5.958 6	(2.359) 0	10
	Collection	5.349 5.5	(1.172) 1	7
	Errors	5.355 5.5	(1.198) 1	7
	Unauthorized Secondary Use 6.015 6.5	(1.071) 1	7
	Improper Access	5.971 6.333 (1.090) 1	7
	Frequency of Actual Use	5.517 5	(1.619) 1	10

Table 2 :

 2 Descriptive Statistics Survey II (N=199)

		Mean Median Std. Dev. Min. Max.
	Age	28.884 26	(8.695) 18	58
	Gender	0.477 0	(0.501) 0	1
	Educational Qualification 4.261 4	(1.142) 1	7
	Smartphone Experience	7.156 7	(2.279) 2	11
	M1	2.050 2	(0.827) 1	4
	M2	1.658 2	(0.654) 1	3
	M3	1.668 2	(0.689) 1	3
	M4	1.563 1	(0.838) 1	3
	M5	2.221 2	(0.652) 1	4
	M6	1.492 1	(0.658) 1	3
	Frequency of Actual Use	6.729 7	(1.783) 1	10

Table 3 :

 3 Two-sample Wilcoxon rank-sum and two-sample t test (for actual use) Therefore, categorical variables for group comparisons are created by dividing the scale of continuous variables into two meaningful groups. Categorical variables for group comparisons are created based on the variables age, smartphone experience and educational qualification. This is necessary because they are not binaries like gender. For creating a categorical variable, a threshold is needed that divides the scale into two meaningful groups.

				Group Variables
	Variables	Age M edian	AgeDN	Gender Education Smartphone Exp.
	Collection	z=-3.217** z=-3.288**	n.s.	n.s.	n.s.
	Errors	z=-6.644*** z=-5.852***	n.s.	z=2.766**	n.s.
	Un. Sec. Use	n.s.	z=-3.020** z=-4.019***	n.s.	n.s.
	Imp. Access z=-2.516** z=-3.870*** z=-2.589***	n.s.	n.s.
	Actual Use	n.s.	n.s.	n.s.	n.s.	t=-4.335***
	t statistic for t test and z statistics for Wilcoxon rank-sum test
	* p < 0.05, ** p < 0.01, *** p < 0.001
	frequency of playing Pokémon Go) differ with regard to age, gender, education and
	smartphone experience.

Table 4 :

 4 Regression analysis of privacy concern variables and use behavior (N=683)

		(1)	(2)	(3)	(4)	(5)
		Dependent variable: actual use behavior
	Collection	0.0518				-0.00691
		(1.00)				(-0.11)
	Errors	-0.0480			-0.170**
		(-0.91)			(-2.76)
	Un. Sec. Use			0.124*		0.00368
				(2.05)		(0.03)
	Imp. Access				0.149* 0.251*
					(2.53)	(2.19)
	cons	5.240*** 5.774*** 4.772*** 4.630*** 4.943***
		(18.70) (20.00) (12.85) (13.01) (12.78)
	t statistics in parentheses
	* p < 0.05, ** p < 0.01, *** p < 0.001

  46. Viswanath Venkatesh, James Thong, and Xin Xu. Consumer Acceptance and User of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1):157-178, 2012. 47. Samuel D. Warren and Louis D. Brandeis. The Right to Privacy.CollectionColl1. It usually bothers me when companies ask me for personal information. Coll2. When companies ask me for personal information, I sometimes think twice before providing it. Coll3. It bothers me to give personal information to so many companies. Coll4. I am concerned that companies are collecting too much personal information about me. Errors Err1. All the personal information in computer databases should be doublechecked for accuracy -no matter how much this costs.

	Harvard Law
	Review, IV(5):193-220, 1890.
	48. Justin Burton Weidner. How & Why Google Glass Failed, 2015.
	49. Howard Yu. What Pokémon Go's Success Means for the Future of Augmented
	Reality, 2016.
	All websites have been accessed last on July 20th, 2017.
	A Questionnaire I

Acknowledgments

The authors wish to thank the Faculty of Economics and Business Administration of the Goethe University Frankfurt am Main for supporting this work with a grant within the funding program "Forschungstopf". This research was also partly funded by the German Federal Ministry of Education and Research (BMBF) with grant number: 16KIS0371. In addition, we would also like to thank Harald Zwingelberg for his valuable feedback with regard to the privacy measures.