Introduction

The objective of having frontline customers' service at the airport, according to earlier scholars, is to control traffic specifically at the peak or rush hour (Comas, 2020). This front line's further functions are to make sure that there is peace, stability, and orderliness at the airport. As such, there have been technological upgrades to ensure maximum security threats and potential crises are naturalizing before they occur or at least be contained (Ackerman, 2017). Despite the increased security measures put in place at the various international airport across the globe, in attempts to reduce unprecedented crises and enhance security, safety, and wellness of airport users and staffs, studies reveal that airport users were not really aware of safety rules and regulations in the airport pertaining to what is allowed and what is not allowed to be transported with (Ivanov et al., 2020). Thus, when asked to forfeit the unallowed items. Therefore, crises occur, and at most times, before the security agents' intervention, the staff got injured physically, or perhaps the airport customer utter vulgar languages that cause psychological distress to the staff (Fachandi, 2020). One of the factors responsible for this type of crisis is the lack of information on account of the passenger (Comas, 2020).

In a similar view, although there is no direct racism act in the airport business, however, there is profiling of people; specifically, those from third world countries have been perceived as potential threats (Cha, 2014). As such, the frontline managers after asking innocent citizen from third world country to step aside, they feel discriminated, and the result is unprecedented crises (Federici & O'Brien, 2019). As reported by (Cha, 2014), the profiling is among the security measures outlined by European Aviation Carter. On the contrary, evidence from pundits' analysis and policymakers argues the controversial documents employed by Transportation Security Administration (TSA) in profiling some certain race contributes to the crises faced at the airport instead of reducing potential crises, as such put the frontline personnel at risk of being assaulted (Humayun, 2020).

To support this claim, the study conducted by the American Civil Liberties Union (ACLU) after several years of profiling lawsuit reveals that there is no scientific backing for the claim of racial profiling (Franco, 2019). Based on these instances and issues identified, the researcher finds it important to empirically examine the significance of racial profiling as a factor among several factors that led to unprecedented crises between the airport customers and airport frontline staff (Juvan et al., 2020). In view of this, this research aims to identify the predicting factors that led to crisis escalations between airport users and the frontline security checks. Traveling is more enjoyable when airport operators meet passenger expectations in every aspect of service (Pius et al., 2018). The management needs to know how they could identify improvement opportunities within airport service areas to meet or exceed passenger expectations (Wiredja et al., 2019). As passengers are also airport stakeholders, their needs must be investigated to identify which aspects are important and how airports and/ or airlines respond to any inadequacy (Polater, 2020).

The highlighting that customers' first impressions of airport facilities may influence an airport's perceived value recommended that airports provide comfortable and convenient services (Loh et al., 2020). Offering an outstanding passenger experience has become a strategic priority for airports to increase their competitive advantage (Adeniran et al., 2020). In the increasingly competitive environment, service quality becomes an important factor, reflecting airport management's evolution from having a primary focus on facilities and operations to providing a passenger-driven service experience (Wiredja et al., 2019). Passenger experience is categorized into two broad categories: processing activities and discretionary activities (Zhang et al., 2020). Processing activities are those that should be completed by a passenger in sequence, consisting of check-in, security screening, immigration, and boarding, while discretionary activities are optional and unordered activities based on passengers' freedom of choice (El Najjar, 2018).

The aim of the study is to examine the impact of gender profiling, racial profiling, communication channel, and technology use on crisis escalations, besides organization culture as a moderation relationship to the crisis escalations in Dubai Airport.

Literature Review

Racial Profiling and Crisis Escalations

Racial or even ethnic profiling is the act of believing or even targeting a person on the basis of presumed features or even actions of a genetic or nationality, as opposed to individual uncertainty (Puppa, 2020; Fortunato et al., 2017). Moreover, genetic profiling, nonetheless, is actually certainly not limited merely to an individual's race or even ethnicity but can easily additionally be actually based upon the person's religious beliefs or national origin (Gigliotti et al., 2017; König et al., 2020). But in European nations, the term "ethnic profiling" is actually likewise made use of rather than racial profiling. According to some researchers such as (Gigliotti et al., 2017; König et al., 2020; Ruprecht, 2016; Shjarback et al., 2017) expected that doing racial or ethnic profiling could have a positive or negative impact on crises escalation, but most researchers in this field such as (Fortunato et al., 2017; König et al., 2020; Turvey & Mares, 2018; Vito et al., 2017) stated that the racial or ethnic profiling would of have a positive direct impact on crises escalations. However, based on that, the researcher expects racial profiling (RA) directly impacts crisis escalations at the Airport (CE). Finally, this hypothesis is also computable with some other hypothesis in other studies such as: (Carmen, 2016; Quek & Johnston, 2018; Turvey & Mares, 2018; Vito et al., 2017; Woodhams & Borrie, 2018).

• H1: There is a positive association between racial profiling and crisis escalations at Dubai international airport.

Gender Profiling and Crisis Escalations

Gender Profile (GP) is the result of a comprehensive gender analysis of women and men's situation in a country or company, which should be used to guide cooperation programming (Rangel et al., 2018; Rangel & Rosso, 2019). According to the researchers such as (Daelemans et al., 2019; Dias & Paraboni, 2019; Rangel et al., 2018; Schaetti & Savoy, 2018; Zhao et al., 2019) they found out that gender profile could of have a positive or negative impact on crises escalation. But, based on that, the researcher is expecting gender profiling (GP) has a direct impact on Crisis Escalations at the airport (CE). Moreover, this hypothesis is also computable with some other hypotheses in other studies such as: (Blanchet et al., 2017; Bordo & Meissner, 2016; Castiglionesi et al., 2019; Jordà et al., 2016; Petrosky-Nadeau & Zhang, 2020; Seabrooke & Tsingou, 2019).

• H2: There is a positive association between gender profiling and crisis escalations at Dubai international airport.

Technology Usage and Crisis Escalations

Technology is the sum of techniques, skills, methods, and processes used in the production of goods or services or in the accomplishment of objectives, such as scientific investigation (Carver, 2016; Gures et al., 2018). According to the usage of technology means the extent of use of technological means and tools in a certain situation (Fujimoto et al., 2016; Shubina & Kulakli, 2019), many researchers such as (Ahearne et al., 2016; Blut & Wang, 2020; Bray & Tangney, 2017; Carver, 2016; Fujimoto et al., 2016) emphasized that the usage of the technology would of help so much in case of Crises Escalations. But, based on that, the researcher is expecting Technology Usage (TU) has a direct impact on crisis escalations at the Airport (CE). Finally, this hypothesis is also computable with some other hypotheses in other studies: (Ataga et al., 2017; Berger & Bouwman, 2017; Bundy et al., 2017; Burnside et al., 2016; Guerrieri & Lorenzoni, 2017; Sosa-Padilla, 2018).

• H3: There is a positive association between technology usage and crisis escalations at Dubai international airport.

Confiscation Channel and Crisis Escalations

A communication channel refers either to a physical transmission medium such as a wire or to a logical connection over a multiplexed medium such as a radio channel in telecommunications and computer networking (Koponen et al., 2016; Miramirkhani & Uysal, 2017; Schleifer & Ozzie, 2016). According to the communication channel, the control and the process of following up in the crises faster and easier (Al-Kinani et al., 2018; Gyongyosi et al., 2018; Smith et al., 2018; Yan et al., 2019; Zhang et al., 2016). Moreover, some researchers were expecting that a better Communication Channel would of help so much and have an impact on Crises Escalations. Based on that, the researcher expects that the Communication Channel (CC) has a direct impact on crisis escalations at the Airport (CE). Finally, this hypothesis is also computable with some other hypotheses in other studies such as: (Boissay et al., 2016; Laeven & Valencia, 2018; Lorenzoni & Werning, 2019; Madras, 2018; Muir, 2017; Stockhammer et al., 2016; Warren, 2018).

• H4: There is a positive association between communication channels and crisis escalations at Dubai international airport.

Organization Culture as a Moderator in the Relationships to Crisis Escalations

Organizational culture encompasses values and behaviors that contribute to the unique social and psychological environment of a business (Kawiana et al., 2018; Willis et al., 2016). A better and good Organization Culture (OC) would help to make a better performance (Chatman & O'Reilly, 2016; Rosario & René, 2017; Paro & Gerolamo, 2017). Which could of affect the Relationship between Racial Profiling (RA) and Crises Escalations at the Airport (CE) positively, and based on that, the researcher is expecting moderating effect of Organization Culture (OC) on the relationship between racial profiling (RA) and Crises Escalations at the airport (CE). This hypothesis is compatible with other hypotheses in other studies: (Boin & Kuipers, 2018; Carson, 2016; Erickson & Liff, 2016; Karolewski & Cross, 2017; Ngai & Jin, 2016; Sechser et al., 2019; Talhouk et al., 2016). • H6: Organization culture moderation the relationship between gender profiling and crisis escalations among at Dubai international airport.

• H5: Organization culture moderation the relationship between racial profiling and crisis escalations among at Dubai international airport.

Company culture is the selection of values, assumptions, and techniques that direct and notifies all staff members' activities (Ali et al., 2017; Chatman & O'Reilly, 2016; Elsbach & Stigliani, 2018). Think about it as the assortment of qualities that create your company what it is (Nazarian et al., 2017; Shahzad et al., 2017). While Gender Profile (GP) is the end result of a thorough gender analysis of the condition of women and men in a nation or company, which should be utilized to help participation shows (Berger & Bouwman, 2017; Lorenzoni & Werning, 2019; Madras, 2018; Nazarian et al., 2017; Shahzad et al., 2017; Warren, 2018). And based on that, the researcher expects that a better Organizational culture that firms will have could of help so much in the relationship between gender profiling (GP) and crises escalations at the airport (CE) As this hypothesis is compatible with other hypotheses in other studies: (Banks, 2019; Dalton & Perkovich, 2016; Erickson & Liff, 2016; Long, 2016; Ngai & Jin, 2016; Pescaroli et al., 2018; Sechser et al., 2019).

• H6: Organization culture moderation the relationship between gender profiling and crisis escalations among at Dubai international airport.

Organizational culture is an association's/firm’s requirements, adventures, ideology, and market values that accommodate it with each other and is actually shared in its own self-image, internal workings, interactions with the outside world, and potential requirements (Chatman & O'Reilly, 2016; Kawiana et al., 2018; Shahzad et al., 2017). It is based on mutual attitudes, beliefs, personalized, and composed and unwritten rules that have been developed over time and are actually thought about valid (Rosario & René, 2017; Elsbach & Stigliani, 2018; Warrick, 2017) likewise called business society. On the other hand, technology may be very most broadly specified as the bodies, each product and immaterial, generated due to the application of psychological and physical effort to achieve some value (Driskill, 2018; Miramirkhani & Uysal, 2017; Smith et al., 2018). In this use, technology pertains to devices and equipment that might be actually utilized to handle real-world complications (Carver, 2016; Shubina & Kulakli, 2019). As we stated before, a better culture in the company would of results in better performance, and using the technology would improve the role of organizational culture as a moderator on the relationship with crisis escalations. And based on that, the researcher is expecting moderating effect of Organization Culture (OC) on the relationship between Communication Channel (CC) and crisis escalations at the airport (CE). As this hypothesis is compatible with other hypotheses in other studies: (Ataga et al., 2017; Bordo & Meissner, 2016; Jordà et al., 2016; Madras, 2018; Petrosky-Nadeau & Zhang, 2020; Seabrooke & Tsingou, 2019; Turvey & Mares, 2018).

• H7: Organization culture moderation the relationship between technology usage and crisis escalations among at Dubai international airport.

As has been described, Organizational culture involves values and habits that support the one-of-a-kind social and psychological atmosphere of a business (Driskill, 2018; Elsbach & Stigliani, 2018; Koponen et al., 2016; Schleifer & Ozzie, 2016). Communication networks are how folks in an organization communicate (Gyongyosi et al., 2018; Miramirkhani & Uysal, 2017). The idea must be provided what channels are used to accomplish various activities, because utilizing an improper channel for a task or even communication can easily trigger damaging outcomes (Smith et al., 2018; Zhang et al., 2016). Facility information needs richer communication networks that assist in communication to make sure clarity (Miramirkhani & Uysal, 2017; Schleifer & Ozzie, 2016; Yan et al., 2019). Furthermore, the researcher found out that some researcher such as (Ahearne et al., 2016; Al-Kinani et al., 2018; Jordà et al., 2016; Nam, 2019; Shubina & Kulakli, 2019) suggested that Organizational Culture (OC) always playing a role in improving the crises especially if a good

communication channel is used for a better outcome, based on that the researcher expects that the Organization Culture (OC) has a moderating impact on the relationship between Technology Usage (TU) and crises escalations at the airport (CE). This hypothesis is compatible with other hypotheses in other studies: (Dalton & Perkovich, 2016; Daminov, 2018; Carmen, 2016; Leys, 2018; Quek & Johnston, 2018; Woodhams & Borrie, 2018).

• H8: Organization culture moderation the relationship between communication channel and crisis escalations among at Dubai international airport.

Conceptual Framework

The research framework diagrammatically presents the summary of all the identified conceptual frameworks presented in this paper. The research framework is, therefore, the summary (in the diagram) of all the identified relationships between the identified exogenous variables, namely (racial profiling, gender profiling, and communication approach) used by the front line officials at the airport check-in counters which tend causing crisis escalations between them and the airport users or travelers (As seen in Figure 1).

Methodology

The study assumed that the crisis escalation, practices, and technology use could be measured in numbers, and prediction can be acquired from the analysis. Therefore, the study belongs to the positivism philosophy, deduction approach, quantitative methodology, empirical survey passed study, used cross-sectional data, and original data.

The target or study population chosen for this research work equates to the total number of airport customers flying through Dubai international airport who are willing to respond to the questionnaire and the check-in staff that will be on duty during the period of data collection. The actual sample size is 360 employees. The distributed survey is 446, which was distributed by using face-to-face data collection methods in a convenient sample selection technique in 2019.

The tool used for data collection is a well-structured survey that is adapted from previous. To measure crisis escalation, the researcher adopted the findings, arguments, and previous items from the studies of (Bocci 2018; Zhang et al., 2019). The items of racial profiling were adapted from studies such as (Pietro et al., 2017; Negri et al., 2019; Zhu et al., 2013). The items used in measuring passengers' perception towards gender profiling that might lead to crisis escalations are adapted from the findings and arguments of previous scholars (Faham & Ernst; 2016; Currah & Mulqueen; 2011; Sakano et al., 2016). To measure technological adoptions at the airport and passengers' perceptions towards its influence in the escalating crisis. Items are adapted from previous scholars' findings and arguments (Adey, 2003; Gallova et al., 2018; Freathy & O'connell, 2000). The items used in measuring communication issues that might lead to crisis escalations are adapted from previous scholars' findings and arguments (Boersma et al., 2012; Tyler, 2014; Uchroński, 2011).

The survey was organized to ask questions in Likert-5 format. Likert 5 questionnaire style has been used in social science studies for a long time and proved to be a suitable style for measuring human perceptions. Structural Equation Modeling (SEM) techniques are used for statistical data analysis via the SmartPLS software package, which is used in management and social science studies such as (Salem & Alanadoly, 2020; Salem & Salem, 2018).

Findings

In PLS-SEM, two-part of data analysis is essential in regression-based models, the reliability and validity tests (measurement model) and the relationships tests (structural model)

Validity and Reliability of Constructs

Several measures have been conducted, such as composite reliability, outer loading, convergent validity, and discriminant validity, to ensure the measurement model's reliability and validity (Hair, Hult, Ringle & Sarstedt, 2016; Sekaran & Bougie, 2016). As shown in Table 1, composite reliability is measured by Cronbach's Alpha, and all values are above the cut-off value of 0.70. Therefore, the reliability of the measurement model is achieved. The outer loading for all the items is above 0.708 with no cross-loading from the foreign item; therefore, indicator reliability is achieved. The average Variance Extracted (AVE) values are above 0.5; therefore, convergent validity is achieved. Finally, Table 2 shows the Fornell-Larcker criterion matrix, which indicates that, no discrimination validity issues are. Some items were eliminated based on the rule of thumb for outer loading and cross-loading; therefore, two items were deleted. Figure 1 shows the Research Framework of this study and Figure 2 shows the Structural Model Outer Loading Estimates.

Table 1 Constructs Reliability and Validity
Construct	Item	Loading	AVE	Cronbach's alpha
Communication Channel (CC)	CC_1	0.737	0.595	0.828
	CC_2	0.896
	CC_3	0.743
	CC_4	0.732
	CC_5	0.734
Crises Escalations (CE)	CE_1	0.919	0.757	0.918
	CE_2	0.91
	CE_3	0.932
	CE_4	0.861
	CE_5	0.711
Gender Profiling (GP)	GP_1	0.789	0.594	0.829
	GP_2	0.735
	GP_3	0.859
	GP_4	0.734
	GP_5	0.728
Organization Culture (OC)	OC_1	0.811	0.681	0.843
	OC_2	0.884
	OC_3	0.769
	OC_4	0.834
Racial Profiling (RP)	RA_1	0.823	0.689	0.887
	RA_2	0.836
	RA_3	0.89
	RA_4	0.869
	RA_5	0.723
	RA_6	0.823
Technology Usage (TU)	TU_1	0.818	0.626	0.804
	TU_2	0.834
	TU_3	0.718
	TU_4	0.79

Table 2 Discriminant Validity – Fornell-Larcker Criterion
	CC	CE	GP	OC	RP	TU
CC	0.771
CE	0.555	0.87
GP	0.292	0.466	0.771
OC	0.115	0.265	0.147	0.825
RP	0.224	0.401	0.289	0.121	0.83
TU	0.278	0.43	0.322	0.032	0.173	0.791

Relationships Examinations and Discussions

To assess the power of the model construct in predicting the outcome variables,See (Table 3) predictive power R² and predictive relevance were used (Hair et al., 2016). The main dependent variable, Crises Escalations (CE), illustrates a satisfactory predictive power and a large predictive relevance. As seen in the table, the related R square value is 0.494 (a power of 49.4%), and the related Q square is 0.369 (a relevance of 36.9%). Racial Profiling (RP), Gender Profiling (GP), Technology Usage (TU), Communication Channel (CC), and Organization Culture (OC) as can explain more than 49.4% of the Crisis Escalations (CE) variance.

Table 3 Predictive Power and Predictive Relevance of Proposed Model
Crises Escalations	Predictive Power		Predictive Relevance
	R Square	Status	Q Square	Status
	0.494	satisfactory	0.369	Large

Suppose the hypothesis argument is for testing the effect in both directions, negative and positive, after that. In that case, the estimation has to utilize the two-tailed levels. On the other hand, if the hypothesis debate is for testing the effect in one direction, negative or positive, the estimation must utilize the one-tailed levels. The research study relationships are in one direction as well as the suitable level of evaluation is one-tailed. Figure 3 shows the T statistics estimates of the research-designed model, and Table 4 shows the path coefficient assessment with the values of T Statistics and Beta values for the outcome variable Crises Escalations (CE). All variables antecedents have significant relation, in which the p-value scores are above 0.05, and the t statistics scores are above 1.65. The precedence for the relations based on the path coefficient value is CC (0.382), GP (0.224), RA (0.214), and TU (0.215).

Table 4 Path Coefficient Assessment of Crisis Escalations (CE)
	Path Coefficient	Standard Deviation	T Statistics	P Value (one tailed)	Status
CC -> CE	0.382	0.039	9.782	0	Significant
GP -> CE	0.224	0.038	5.82	0	Significant
RA -> CE	0.214	0.04	5.3	0	Significant
TU -> CE	0.215	0.038	5.617	0	Significant

Table 5 shows the path coefficient assessment with T Statistics and Beta values for the outcome variable Organization Culture (OC) as a moderator. Respectively, the Path Coefficient for this racial profiling (RP) variable is -0.078, the T- data score is 2.096, and also the P-value is 0.039 as significant, the Path Coefficient for this technology use (TU) variable is -0.073, T- statistics rack up is 2.024, and the P-value is 0.046 substantial, the Path Coefficient for this interaction channel (CC) variable is -0.038, T- stats score is 1.136, and also the P-value is 0.259 as significant, the Path Coefficient for this sex profiling (GP) variable is -0.023, T- statistics score is 0.456, and also the P-value is 0.649 non-significant.

Table 5 Moderation Assessment of Technology Usage (TU)
	Path Coefficient	Standard Deviation	T Statistics	P-Value (one-tailed)	Status
RP - CE	-0.078	0.037	2.096	0.039	Significant
GP - CE	-0.023	0.051	0.456	0.649	Non-Significant
TU - CE	-0.073	0.036	2.024	0.046	Significant
CC - CE	-0.038	0.033	1.136	0.259	Non-Significant

Overall, direct relationships for the four predictors of crisis escalations in the airport are significant; the precedence for the relations based on the path coefficient value is communication channel (0.382), gender profiling (0.224), racial profiling (0.214), and technology usage (0.215). For the moderating relationships of technology use, two interactions have a significant positive interaction; racial profiling (-0.078) and technology readiness (-0.073), but the communication channel and gender profiling have no significant change based on organization culture.

Contributions and Recommendations

The study contributes to the knowledge of crisis escalations, gender profiling, racial profiling, communication channel, and technology use, and organization culture. The proposed combination of variables, especially the emphasis on crisis escalations as a dependent variable and organization culture as a moderator, is another theoretical contribution, especially when it is applied in the potential daily crisis in the airports. The study also adds knowledge about crisis escalations and the causes in Dubai international airports.

This study is limited to the empirical examination of UAE airports; however, replicating the same design with the same research design but in different airports and different countries will support the model validity and generalization of the results. The interception of organizational culture in the relationship from the communication channel and racial profiling are found to be no significant; additional work is needed to reveal the reason and explain this rejection. The model can also explain up to 49.4% of the crisis escalations variance; scholars are welcome to investigate more antecedents to increase the model power and provide a strong explanation model.

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