The Relationship between Motivation and the Role of the Night of the Museums Event: Case Study in Oradea Municipality, Romania

Abstract

Cultural events are relevant attractions in contemporary cities that can be understood as celebrations, but also as agents of urban change. The primary objective of this study is to identify emerging themes in the area of cultural events, especially the Night of Museums, and to highlight the relationships among museums as places of learning and aesthetic satisfaction. To reach this goal, an exploratory and descriptive study was conducted. Multinomial logistic regression (MLR) and Regression Analysis were further performed. The results show that physical environment and situational interactions play a decisive role in contextual perception of the importance of events. Curiosity, gratuity, the need to learn and spending free time in the most pleasant way are just a few reasons that lead citizens to direct their attention in a special way. Additionally, this study not only limited to cultural tourism, but also contributes to literature in the area of economy, sustainability and environment. Moreover, our findings have implications for policy makers to identify practices and education that can improve the understanding of cultural and social value.

1. Introduction

The Night of Museums is a major event with broad spatial, cultural, organizational and social implications, during which people visit museums, libraries, galleries and theaters, and attend performances, film screenings, musical events, educational programs for children, etc. [1,2,3]. All these actions are intended to contribute to considerations such as the following: the formation of cultural identity by exposing the public to local culture [4,5,6,7]; changing the attitude and perception among visitors towards museums [8,9,10]; improving the image of the city [11,12] and of the tourist destination [13,14,15,16,17]; developing the local economy [18,19]; etc.

In general, ‘events’ are human organizational manifestations generated on an occasion, cyclically or not, being characterized by a general objective, operational objectives, target group activities and results to be obtained as a result of the activities. Over time, numerous events have been organized for different purposes, including military (e.g., the Crusades between 1096 and 1270; the 100 Years’ War between 1337 and 1453; the War of the Roses between 1455 and 1485; etc.), sports (e.g., the ancient Olympic Games between 776 BC and 339; modern Olympic Games; football championships; etc.), religious (e.g., religious pilgrimages to Jerusalem; to Rome; to Santiago de Compostela; to Lourdes; to Mecca; to Iasi and the relics of Saint Pious Paraschiva; to Prislop Monastery; to the tomb of Father Arsenie Boca; etc.), recreational and entertainment (e.g., festivals celebrating film; music; art; classical or urban culture; etc.), cultural (e.g., theatre shows and art exhibitions), etc.

Currently, some of these manifestations of events are still in place, adapting to the times and needs, while others have been invented and staged against the background of new needs specific to today’s human communities. One such event is the Night of the Museums, which originated from two events organized in Berlin (1997) and Paris (1999). The concept of this free event first appeared in Berlin in 1997, under the name “Lange Nacht der Museen”—Long Night of Museums. The purpose of this event was, among other things, to popularize cultural tourism and museum complexes [1]. Due to the success of this event, it was later taken over by Paris in 1999 under the name “Le Printemps des Musées” (The Spring of the Museums), through which the French Ministry of Culture and Communication aimed to encourage people to rediscover the variety of museums and the wealth of their collections [20]. In 2001, the event was organized by all the signatory countries of the cultural sector at the Council of Europe convention and had, as its main target group, the people who live near the institutions involved in the initiative. Since 2005, the event has expanded throughout Europe, being promoted as the European Night of Museums, and proposing to attract young people and families to museums during the night [21]. It was organized and held on the Saturday closest to International Museum Day (18 May), which was established in 1977 by ICOM to encourage public awareness of the role of museums in the development of society [22].

Museums play an important role in the research, conservation, promotion and valorization of cultural heritage [23,24,25,26]. At the same time, museums can also represent key tools in urban development [27,28], as well as a serious option for spending free time [29,30], thus becoming strategic partners in the tourism industry [31] with wide implications for local development [32,33] and the tourist destination image [34,35]. So, museums perform important functions in the development of cultural tourism, by presenting national and local values to potential tourists [36,37].

Since 2011, the International Council of Museums has been joined by UNESCO and the Council of Europe in sponsoring this event [21,22]. Over time, more and more cities have joined and will continue to join the event, thus making it a large-scale aspect at the EU level [38].

Numerous studies have highlighted the increase in the number of tourists following the organization of the Night of Museums event in Eastern European countries, including Croatia [39], Serbia [1], Romania [40] and Poland [41]. However, considering the spatial extent of the Night of Museums event (i.e., throughout all of Europe), it is quite difficult to highlight its role in attracting tourists to a specific tourist destination.

In this context, the purpose of this study is to establish the statistical relationship between the motivation and the extent to which people agree that museums are important as places of learning and aesthetic satisfaction, during the Night of Museums event in the Municipality of Oradea, Bihor County, Romania.

2. Context and the Study Area

In Romania, the Night of Museums event was introduced in 2005 as a cultural event without a clear tourism-oriented goal, instead being suggested to the public as an enjoyable activity [40].

At the geographic level, the 18th edition of the Night of Museums event in Romania (210 organized events) took place on 14 May 2022, and covered 87 localities in 40 counties, with an average of 2.1 localities per county. The most localities that participated in the Night of Museums 2022 were in the counties of Argeș (6 localities), Brașov (6 localities), Cluj (5 localities), Vrancea (5 localities) and Prahova (4 localities), while at the opposite pole was Harghita County where no cultural event of this type was organized (Figure 1). In terms of organization, with each edition over time, there has been a growing involvement of cities, cultural institutions and more. Thus, in Romania, the number of cities participating in this event exhibited an upward evolution in the last three years, from 29 cities in 2020 to 74 cities in 2021, while in 2022, 87 cities participated (Figure 1).

In the Municipality of Oradea, during the organization of the Night of the Museums event on 14 May 2022 (held under the auspices of the Oradea Museums Together! 7th Edition), numerous institutions were involved as hosts (including the Museum of Țării Crișurilor with related branches—the “Iosif Vulcan”, “Aurel Lazăr” and “Ady Endre” memorial museums, and the Oradea City Museum; the Museum of the Romanian Orthodox Diocese of Oradea; the Roman Catholic Episcopal Palace; the Roman Catholic Cathedral; the Rodia Pharmacy Museum; the St. Nicholas Cathedral museum point; the Greek Catholic Diocese of Oradea; the “King Ferdinand” National Military Museum, Oradea Branch; the museum objectives of the Oradea City Hall—Oradea City Hall Tower; Sion Neolog Synagogue; Darvas La Roche House; Aachvas Rein Synagogue; the Freemasonry Temple; and the Great Orthodox Synagogue), while other organizations served as volunteers (e.g., the University of Oradea, Oradea “Aurel Lazăr” Theoretical High School, Oradea Arts High School and “Iosif Vulcan” National College) or as partners providing specialized technical assistance (e.g., Bihor County Council, Oradea City Hall, the “Friends of the Țăriții Crișurilor” Association, Oradea Transport Local S.A., Oradea Gendarmerie, “Crișana” Emergency Situations Inspectorate of Bihor County, and Bihor County Ambulance Service).

The analysis of the number of participants attending the Night of the Museums (24.730 participants), according to the criteria of the organizing institution, highlighted the major role of the Țării Crisurilor Museum (40%), followed by the “King Ferdinand” Military Museum (25%), Oradea City Hall (15%), Roman Catholic Church—Diocese of Oradea (12%), Romanian Orthodox Diocese (5%) and Greek Catholic Diocese of Oradea (4%) (Figure 2).

Each of these organizing institutions got involved in the event by opening some cultural objectives (18 participating institutions). The share of visitors at different institutions ranged from 712 people (the “Ady Endre” Memorial Museum) to 10,243 people (the Museum of Țării Crișurilor, together with the “King Ferdinand”National Military Museum). The analysis of the number of participants highlighted the existence of five typological categories of participating institutions: no declared data (2 units); 712 to 1000 participants (4 units); 1001 to 2000 participants (6 units); 2001 to 3000 participants (4 units) and over 3000 participants (2 units) (Figure 2).

Based on an analysis of the specialized literature related to the Night of the Museums event, it can be easily observed that, despite the magnitude of such research in recent years, in nearly all respects (i.e., participants, organizing institutions, localities involved, participating states, financial implications etc.), the topic is not sufficiently well-studied from an economic, social, political or cultural point of view. Thus, although in the specialized literature there are numerous studies on motivation, namely the motivation of tourists, the approach used in the present study complements the previously existing research by introducing novel considerations, including the research vision, the indicators studied, the people consulted and the area studied (namely, tourist destinations in Oradea, Romania).

3. Methodology

The data necessary to carry out the present study were obtained on 14 May 2022, in the six locations related to Oradea City Hall (Oradea City Hall Tower, Sion Neolog Synagogue, Darvas La Roche House, Aachvas Rein Synagogue, Freemasonry Temple, Great Orthodox Synagogue), through the survey method based on a sociological questionnaire [42,43]. All six locations were part of the Night of the Museums event, and the respondents were mostly selected from among the locals who participated in this event.

In this research we tried to analyze the factors affecting the motivation for museum visits, based on the responses of 123 people attending the event of The European Night of Museums 2022. We used the following denotation to facilitate analysis (

Table 1. Survey questions and motivations regarding museum visits.

Questions

Motivations

$Q_1—$I am interested in the activities of this annual festival because museum serves as a public educational center $Q_2—$Museums can provide inspiration about past glories $Q_3—$Museums can be the most prestigious cultural institutions because they allow people to view artistic works $Q_4—$Museums can be the most prestigious cultural institutions because they allow people to view artistic works $Q_5—$Museums serve as forum for dialogue and discussion $Q_6—$Museums can be interesting and fun places to visit for recreation $Q_7—$Museums can be managed as cultural tourism destinations $Q_8—$I found that the museum festival offered something unique $Q_9—$The museum’s cultural attractions fascinate me $Q_{10}—$The museums facilities are great for visitors $Q_{11}—$Museum festivals like this should be held annualy.

$M_1—$Gaining knowledge $M_2—$Entertainment $M_3—$Studying history $M_4—$Being curious $M_5—$Free of charge $M_6—$Being invited by a friend.

).

The questionnaire, which included 11 questions, was built to investigate the reasons participants gave for choosing to visit the museum.

Q₁–Q₂–Q₃–Q₄–Q₅–Q₆–Q₇–Q₈–Q₉–Q₁₀–Q₁₁–M₁–M₂–M₃–M₄–M₅–M₆—Each question (Q variables) included answers in the Likert scale format (1—Strongly disagree, 2—Disagree, 3—Neither agree nor disagree, 4—Agree, 5—Strongly agree). Meanwhile, motivation variables (M variables) were binary (0—No, 1—Yes). To provide focus, comparability and validity, questions were asked specifically about events that were staged during one calendar year (2022). This means that the effects of the Coronavirus crisis are not addressed in the research presented here.

The purpose of this study was to verify the following hypotheses:

• H₀—None of the independent variables (Q₁, Q₂, Q₃, Q₄, Q₅, Q₆, Q₇, Q₈, Q₉, Q₁₀, Q₁₁) significantly affect any of the following dependent variables: M₁, M₂, M₃, M₄, M₅, M₆.
• H₁—At least one of the independent variables (Q₁, Q₂, Q₃, Q₄, Q₅, Q₆, Q₇, Q₈, Q₉, Q₁₀, Q₁₁) significantly affect at least one of the following dependent variables: M₁, M₂, M₃, M₄, M₅, M₆.

We used multinomial logistic regression (MLR) to verify whether independent variables have significant statistical effects on the dependent variables. This method is comfortable with the unordered independent variables and nominal dependent variables. Moreover, multinomial logistic regression is considered agile because it does not assume normality, linearity or homoscedasticity. In our case, we also have asymmetrically distributed independent variables that are clustered close to the maximum value. The choice to conduct MLR analysis on six of the variables identified was adopted after carrying out various tests and ascertaining that these choices explained the highest level (percentage) of variance.

Multinomial logistic regression is also known as softmax regression. As MLR is an extended version of logistic regression, it uses a generalized sigmoid function referred to as a softmax function. The softmax function can be expressed as follows:

$$P\left(y=j\right|z^{\left(i\right)})=\frac{e^{z^{\left(i\right)}}}{{\sum }_{j=0}^k{e}^{z_k^{\left(i\right)}}}$$

(1)

Here:

• $P\left(y=j\right|z^{\left(i\right)})$ is the probability of the corresponding class’s impact on the dependent variable.
• j is the class of the independent variable i, and can range from 0 to k, where k is the number of classes for the independent variable.
• The term ${\sum }_{j=0}^k{e}^{z_k^{\left(i\right)}}$ normalizes the distribution so that the sum of the probabilities of each class sum to one.
• $z$ is the net input vector, given as $z=w_1{x}_1+\cdots +w_m{x}_m+b={\sum }_{n=1}^m{w}_n{x}_n+b=w^{T}x+b$, where w is the weight vector, x is the feature vector of n training samples and b is the bias (Zhang et al., Dive into Deep Learning. Unpublished draft).

We also tried to determine the statistical relationship between the motivation for visiting the museum and the integral indicator of the role of museums in society. We determined the integral indicator of museums’ role as follows:

$${IM}_{integral}=\frac{{\sum }_{i=1}^{11}{Q}_i}{11}$$

(2)

Here ${IM}_{integral}$—the integral indicator of museums’ impact on society—is the mean of the Q variables.

We also try to verify the following hypotheses:

• $H_0^1—$none of the M variables significantly affect the ${IM}_{integral}$.
• $H_1^1—$at least one of the M variables significantly affects the ${IM}_{integral}$.

So, in this case, we will verify the following dummy regression:

$${IM}_{integral}=\alpha +{\beta }_1{M}_1+{\beta }_2{M}_2+{\beta }_3{M}_3+{\beta }_4{M}_4+{\beta }_5{M}_5+{\beta }_6{M}_6+ϵ$$

(3)

where, $\alpha —$intercept, ${\beta }_{\mathrm{1,2},\mathrm{3,4},5}—$corresponding coefficients, $ϵ—$error term, $M_{1,2,3,4,5,6}—$corresponding motivation dummy variables (1—Yes, 0—No).

4. Results and Discussion

The first step was to process the data collected, using a first phase of descriptive analysis to evaluate the main features, followed by the multinomial logistic regression (MLRC) analysis. Below, we present the descriptive statistics of the explanatory ordinal variables (

Table 2. Descriptive statistics.

Variables	N	Minimum	Maximum	Average
$Q_1—$I am interested in the activities of this annual festival because museum serves as a public educational center	123	1	5	4.37
$Q_2—$Museums can provide inspiration about past glories	123	1	5	4.38
$Q_3—$Museums can be the most prestigious cultural institutions because they allow people to view artistic works	123	1	5	4.32
$Q_4—$Museum serves as the home of a noble civilization	123	1	5	4.36
$Q_5—$Museums serve as forum for dialogue and discussion	123	1	5	4.37
$Q_6—$Museums can be interesting and fun places to visit for recreation	123	1	5	4.46
$Q_7—$Museums can be managed as cultural tourism destinations	123	1	5	4.55
$Q_8—$I found that the museum festival offered something unique	123	1	5	4.32
$Q_9—$The museum’s cultural attractions fascinate me	123	1	5	4.39
$Q_{10}$The museums facilities are great for visitors	123	1	5	4.39
$Q_{11}—$Museum festivals like this should be held annually	123	1	5	4.56
$M_1—$Gaining knowledge	123	0	1	0.28
$M_2—$Entertainment	123	0	1	0.28
$M_3—$Studying history	123	0	1	0.29
$M_4—$Being curious	123	0	1	0.30
$M_5—$Free of charge	123	0	1	0.13
$M_6—$Being invited by friend	123	0	1	0.15

).

As we can see from Table 2, in almost all Q variables the average responses were above 4—Agree, which means that most of the respondents agreed with the importance of museums as an important cultural destination, and an attractive place for aesthetic satisfaction with good facilities. We used IBM SPSS 22 software to conduct the multinomial logistic regression analysis. In the process of analysis, we found that the Q variables significantly affected only one out of six possible dependent variables, namely, M₂—Entertainment. Below we present the results of the analysis (

Table 3. Case Processing Summary.

		N	Marginal Percentage
$M_2$ (Dependent variable)	0	88	71.5%
	1	35	28.5%
$Q_1$	1	1	0.8%
	2	4	3.3%
	3	9	7.3%
	4	43	35.0%
	5	66	53.7%
$Q_2$	1	1	0.8%
	2	2	1.6%
	3	9	7.3%
	4	48	39.0%
	5	63	51.2%
$Q_3$	1	1	0.8%
	2	2	1.6%
	3	8	6.5%
	4	58	47.2%
	5	54	43.9%
$Q_4$	1	1	0.8%
	2	3	2.4%
	3	8	6.5%
	4	50	40.7%
	5	61	49.6%
$Q_5$	1	1	0.8%
	2	3	2.4%
	3	13	10.6%
	4	38	30.9%
	5	68	55.3%
$Q_6$	1	1	0.8%
	2	2	1.6%
	3	10	8.1%
	4	36	29.3%
	5	74	60.2%
$Q_7$	1	1	0.8%
	3	7	5.7%
	4	37	30.1%
	5	78	63.4%
$Q_8$	1	3	2.4%
	2	4	3.3%
	3	13	10.6%
	4	34	27.6%
	5	69	56.1%
$Q_9$	1	1	0.8%
	2	4	3.3%
	3	8	6.5%
	4	43	35.0%
	5	67	54.5%
$Q_{10}$	1	1	0.8%
	2	5	4.1%
	3	6	4.9%
	4	44	35.8%
	5	67	54.5%
$Q_{11}$	1	1	0.8%
	3	6	4.9%
	4	38	30.9%
	5	78	63.4%
Valid		123	100.0%
Missing		0
Total		123
Subpopulation		86 a

^a The dependent variable has only one value observer in 84 (97.7%) subpopulations.

).

We can see the distribution of questionnaire responses for each category. It is evident that the values 4 and 5 account for most responses. The Model Fitting Information table gives us information about whether the addition of the given variables improves the model fit, compared to if the model only consisted of the intercept. It is clear that the inclusion of the Q variables significantly improves the model fit (p value is below 0.05) in

Table 4. Model Fitting Information.

Model	Model Fitting Criteria			Likelihood Ratio Tests
	AIC	BIC	−2 Log-Likelihood	Chi Square	df	Sig.
Intercept only	118,001	120,813	116,001
Final	131,212	226,827	63,212	52,789	33	0.016

and

Table 5. Goodness of Fit.

Test Types	Chi-Square	df	Sig.
Pearson	58,668	52	0.244
Deviance	57,919	52	0.266

.

The Goodness of Fit table shows that both deviance and Pearson chi-square tests are insignificant, indicating that the model fits the data well. The pseudo-R-square is also similar to the R-square measures, indicating the extent of variation explained by the independent variables.

If we consider the 95% confidence interval, it can be seen from

Table 6. Pseudo-R-Square.

Cox and Snell	0.349
Nagelkerke	0.501
McFadden	0.0359

and

Table 7. Likelihood Ratio Tests.

Effect	Model-Fitting Criteria			Likelihood Ratio Tests
	AIC Reduced Model	BIC Reduced Model	−2 Log-Likelihood Reduced Model	Chi-Square	df	Sig.
Intercept	131,212	226,827	63,212	0.000	0	.
${\mathit{Q}}_{\mathbf{1}}$	127,099	214,277	65,099	1.887	3	0.596
${\mathit{Q}}_{\mathbf{2}}$	134,036	221,213	72,036	8.823	3	0.032
${\mathit{Q}}_{\mathbf{3}}$	126,437	213,615	64,437	1.224	3	0.747
${\mathit{Q}}_{\mathbf{4}}$	126,498	213,675	64,498	1.285	3	0.733
${\mathit{Q}}_{\mathbf{5}}$	139,890	227,068	77,890	14.678	3	0.002
${\mathit{Q}}_{\mathbf{6}}$	126,963	214,141	64,963	1.751	3	0.626
${\mathit{Q}}_{\mathbf{7}}$	133,115	223,105	69,115	5.903	2	0.052
${\mathit{Q}}_{\mathbf{8}}$	134,495	218,860	74,495 b	11.282	4	0.024
${\mathit{Q}}_{\mathbf{9}}$	131,380	218,558	69,380	6.168	3	0.104
${\mathit{Q}}_{\mathbf{10}}$	141,668	228,846	79,668	16.456	3	0.001
${\mathit{Q}}_{\mathbf{11}}$	130,407	220,397	66,407	3.194	2	0.202

b. An overflow occurred in a floating point operation while calculating the specified statistics. The chi-square statistic is the difference in −2 log-likelihood tests between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.

that the Q₂, Q₅, Q₈ and Q₁₀ variables are significant predictors in the model (p values are less than 0.05), while Q₇ is near significant.

Table 8. Parameter estimates.

Entertainment a		B	Std. Error	Wald	Df	Sig.	Exp (B)	95% Confidence Interval for Exp (B)
								Lower Bound	Upper Bound
0	Intercept	1.317	0.397	11.003	1	0.001
	[$Q_1=$1]	−74.138	5590.986	0.000	1	0.989	6.342 × 10−33	0.000	. b
	[$Q_1=2$]	−7.395	7056.247	0.000	1	0.999	0.001	0.000	. b
	[$Q_1=$3]	−38.886	2977.778	0.000	1	0.990	1.294 × 10−17	0.000	. b
	[$Q_1=$4]	−0.953	0.701	1.846	1	0.174	0.386	0.097	1.525
	[$Q_1=$5]	0 c	.	.	0	.	.	.	.
	[$Q_2=$1]	0 c	.	.	0	.	.	.	.
	[$Q_2=$2]	−114.99	0.000	.	1	.	1.144 × 10−50	1.144 × 10−50	1.144 × 10−50
	[$Q_2=$3]	−27.913	2856.573	0.000	1	0.992	7.544 × 10−13	0.000	. b
	[$Q_2=$4]	0.441	0.869	0.258	1	0.612	1.554	0.283	8.539
	[$Q_2=$5]	0 c	.	.	0	.	.	.	.
	[$Q_3=1$]	0 c	.	.	0	.	.	.	.
	[$Q_3=2$]	−95.769	10,759.66	0.000	1	0.993	2.558 × 10−42	0.000	. b
	[$Q_3=3$]	13.273	836.938	0.000	1	0.987	581,365.377	0.000	. b
	[$Q_3=$4]	0.500	0.801	0.389	1	0.533	1.648	0.343	7.924
	[$Q_3=5]$	0 c	.	.	0	.	.	.	.
	[$Q_4=$1]	0 c	.	.	0	.	.	.	.
	[$Q_4=2$]	14.792	7083.199	0.000	1	0.998	2,655,250.564	0.000	. b
	[$Q_4=3$]	−14.660	2879.906	0.000	1	0.996	4.296 × 10−7	0.000	. b
	[$Q_4=$4]	1.021	0.926	1.216	1	0.270	2.776	0.452	17.042
	[$Q_4=5$]	0 c	.	.	0	.	.	.	.
	[$Q_5=1$]	0 c	.	.	0	.	.	.	.
	[$Q_5=2$]	−41.680	7286.849	0.000	1	0.995	7.920 × 10−19	0.000	. b
	[$Q_5=3$]	53.838	2960.589	0.000	1	0.985	2.4 × ${10}^{25}$	0.000	. b
	[$Q_5=$4]]	−0.901	0.807	1.245	1	0.264	0.406	0.083	1.977
	[$Q_5=5$]	0 c	.	.	0	.	.	.	.
	[$Q_6=1$]	0 c	.	.	0	.	.	.	.
	[$Q_6=2$]	125.38	13,656.14	0.000	1	0.993	2.854 × 1054	0.000	. b
	[$Q_6=3$]	1.776	2.127	0.697	1	0.404	5.906	0.091	381.61
	[$Q_6=$4]	−0.894	0.819	1.191	1	0.275	0.409	0.082	2.037
	[$Q_6=$5]	0 c	.	.	0	.	.	.	.
	[$Q_7=1$]	0 c	.	.	0	.	.	.	.
	[$Q_7=3$]	−1.082	1.698	0.406	1	0.524	0.339	0.012	9.461
	[$Q_7=$4]	2.570	1.254	4.202	1	0.040	13.070	1.119	152.585
	[$Q_7=5$]	0 c	.	.	0	.	.	.	.
	[$Q_8=1$]	56.114	3638.216	0.000	1	0.988	2.34 × ${10}^{25}$	0.000	. b
	[$Q_8=2$]	16.573	1482.213	0.000	1	0.991	15,753,343.130	0.000	. b
	[$Q_8=3$]	−16.407	828.047	0.000	1	0.984	7.489 × 10−8	0.000	. b
	[$Q_8=$4]	1.374	0.989	1.929	1	0.165	3.952	0.568	27.482
	[$Q_8=5$]	0 c	.	.	0	.	.	.	.
	[$Q_9=1$]	0 c	.	.	0	.	.	.	.
	[$Q_9=2$]	−52.204	6397.036	0.000	1	0.993	2.129 × 10−23	0.000	. b
	[$Q_9=3$]	−3.060	2.236	1.874	1	0.171	0.047	0.001	3.749
	[$Q_9=$4]	−2.951	1.333	4.900	1	0.027	0.052	0.004	0.713
	[$Q_9=5$]	0 c	.	.	0	0	.	.	.
	[$Q_{10}=1$]	0 c	.	.	0	0	.	.	.
	[$Q_{10}=2$]	61.901	2461.635	0.001	1	0.980	7.6 × ${10}^{25}$	0.000	. b
	[$Q_{10}=3$]	63.031	1972.613	0.001	1	0.975	2.36 × ${10}^{25}$	0.000	. b
	[$Q_{10}=$4]	−1.678	1.230	1.862	1	0.172	0.187	0.017	2.080
	[$Q_{10}=5$]	0 c	.	.	0	0	.	.	.
	[$Q_{11}=1$]	0 c	.	.	0	0	.	.	.
	[$Q_{11}=3$]	−1.823	2.496	0.533	1	0.465	0.162	0.001	21.532
	[$Q_{11}=$4]	1.454	0.970	2.249	1	0.134	4.282	0.640	28.645
	[$Q_{11}=5$]	0 c	.	.	0	.	.	.	.

^a Reference category: 1. ^b An overflow occurred in a floating point operation while calculating the specified statistics. Therefore, its value will be set as a missing system value. ^c This parameter is set to zero because it is redundant.

shows the parameter estimate statistics. We can see that if a person’s agreeableness on Question 7 (Museums can be managed as cultural tourism destinations) increments by one unit, then the odds of him or her visiting a museum for entertainment is changed by a factor of 13 (in other words, the odds are increased by 13 times). In this analysis, the reference category is a respondent who visits museums for entertainment.

Additionally, if a respondent’s agreeableness on Question 9 (The museum’s cultural attractions fascinate me) grows by one unit, the odds of him or her visiting a museum for entertainment decreases by a factor of 0.05. The influence of the rest of the factors on the dependent variable remained insignificant at a 95% confidence interval. Below, in

Table 9. Classification.

Observed	Predicted
	0	1	Percent Correct
0	84	4	95.5%
1	17	18	51.4%
Overall Percentage	82.1%	17.9%	82.9%

information about the performance of the model’s predictability is given. Overall, the model’s predictions were correct 82.9% of the time. The model correctly predicted 95% of respondents who did not visit museums for entertainment, while 51.4% of the cases when respondents visited for entertainment were predicted correctly by the model.

So, in general, the model was effective in predicting the behavior of respondents whose motivation for visiting museums was entertainment.

Moreover, we decided to explore the inverse relationship of motivations (M variables) for visiting museums on the aggregate opinion about museums’ importance in social life. We took the mean of all Q variables to calculate the integral indicator of museums’ importance in social life. We used the method of simple linear regression with dummy independent variables. Below we present the results of regression analysis in SPSS software.

Table 10. Model Summary ^b.

Model	R	R-Squared	Adjusted R Squared	Standard Error of the Estimate
1	0.350 a	0.122	0.077	0.57349

^a Predictors: (Constant), Being invited by friend, Entertainment, Being curious, Free of charge, Gaining knowledge, Studying history. ^b Dependent variable: Integral indicator.

illustrates that the model explains only 7.4 percent of variance in the dependent variable, which is a relatively small value, while in

Table 11. ANOVA ^a.

Model		Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	5.309	6	0.885	2.690	0.018 b
	Residual	38.151	116	0.329
	Total	43.460	122

^a Dependent variable: Integral indicator. ^b Predictors: (Constant), Being invited by friend, Entertainment, Being curious, Free of charge, Gaining knowledge, Studying history.

we can see that the factors are selected correctly (p value is below 0.05). The main results of the regression model are given in

Table 12. Coefficients ^a.

Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.
		B	Standard Error	Beta
1	(Constant)	4.508	0.095		47.323	0.000
	${\mathit{M}}_{\mathbf{1}}$—Gaining knowledge	0.302	0.118	0.227	2.549	0.012
	${\mathit{M}}_{\mathbf{2}}$—Entertainment	−0.189	0.120	−0.143	−1.577	0.118
	${\mathit{M}}_{\mathbf{3}}$—Studying history	−0.323	0.120	−0.247	−2.698	0.008
	${\mathit{M}}_{\mathbf{4}}$—Being curious	−0.144	0.116	−0.111	−1.235	0.219
	${\mathit{M}}_{\mathbf{5}}$—Free of charge	−0.076	0.157	−0.043	−0.485	0.629
	${\mathit{M}}_{\mathbf{6}}$—Being invited by friend	0.113	0.148	0.067	0.762	0.448

^a Dependent variable: Integral indicator.

.

$${IM}_{integral}=4.5+0.3M_1-0.32M_3$$

(4)

here ${IM}_{integral}$ refers to the integral indicator of museums’ role in society.

It is clear that respondents whose motivation is gaining knowledge will increase the integral indicator by 0.3 points, while if a respondent visits museums for studying history the integral index will decrease by 0.32 points. This could tell us about the significant impact of museums on social life for people who visit museums to gain knowledge, while people whose motivation is to study history appear to give less value to the importance of museums in social life.

5. Conclusions

Citizens’ interest in the Night of Museums event is a defining constant in Europe generally [1,21,38,39,41], as well as in Romania specifically [40], a fact well-highlighted by the increasing scale of the event at both the geographic and organizational level. Curiosity, free cost, the need for culturalization and the desire to spend free time in the most pleasant way are just a few of the reasons that lead citizens to leave the house and direct their attention to this event once a year.

The present study has significant implications for governance pertaining to tourism and cultural services, as it has presented museums as a tourism resource based on the multidimensional roles museums can play, including (as discussed in the literature part of this paper), the promotion of local culture and sustainability, destination branding, economic growth and regional development, representing technological innovation, etc. This is more relevant as, in the framework of cultural tourism, the main motivation of the tourist is to discover and learn about the local culture through tangible and intangible cultural activities, and museums house most of the mobile cultural heritage of a nation. One aspect of this study that reflects the cultural importance of museum visits is how museum can be exhibited and presented as important cultural resources.

The overall results of the analyses of our survey showed that respondents supported the idea of managing museums as cultural tourism destinations, which suggests the transformation of museums into more entertaining destinations in the future. Meanwhile, it was found out that visitors whose motivation was to gain knowledge thought that museums played an important role in social life, whereas people with more specific motivations, such as studying history, gave less credit to museums. Among the limits of the study is the size of the sample, which does not allow generalizations at the national level of Romania, instead having a more local character, at the level of Oradea Municipality, Bihor County. This indicates that further quantitative and qualitative studies over larger areas are needed. Indeed, the questionnaire was given to only 123 people, which is not a large sample. Therefore, further research on museums’ impact on social life, and the behavior of visitors, should be carried out. This research serves as an empirical experiment on the exploration of museum visitors’ behavior and an attempt to estimate the museums’ impact on social life. The results of the present research can be used in the planning and organizing of future events of this type, as they complement other studies on tourism and tourism perception at both the local level [44,45] and internationally [46,47,48]. The element of novelty brought by the present study is given by the proposed theme (participants’ perceptions of the Night of Museums), the area where the study was carried out (in the six locations in the Municipality of Oradea, Bihor County, Romania) and the people consulted (a sample of 123 people from the population participating in the Night of Museums event on 14 May 2022, in Oradea, Romania). This paper is important for policy makers as well, as it highlights the theoretical evidence of cultural features being an important tourism resource for the development of the economy of a country, at the local, regional and even national level.