Personality is a unique set of attributes, characteristics, and behavioural patterns associated with an individual [22]. There have been studies on individual traits like openness to experience, acceptance of change, neuroticism, extraversion, conscientiousness, and their relation to job satisfaction [23, 24]. These traits are part of the famous Big Five Model which has found resounding consensus across the world from the 90s till date [17, 25]. Some studies found that extraversion was positively correlated to job satisfaction while neuroticism and psychoticism were negatively correlated to satisfaction. It was further established that agreeableness was more closely associated with individual-targeted citizenship behaviour while conscientiousness was more closely associated with organization-targeted citizenship behaviour.

Judge, et al. [26] conducted the traits from the five-factor model of personality that has a significant effect on employee satisfaction and performance in a job. Results showcased support for the validity that as a model taken together, the Big Five traits have a high relationship with job satisfaction. Agreeableness and extraversion were closely related to job satisfaction and employee well-being and they emerged as one of the most important factors compared to others in determining job satisfaction and subjective well-being. Based on the established conceptual and empirical linkage from the existing studies, this study took one step forward by incorporating the Big Five traits of extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience as part of the analysis to test employer satisfaction.

Wellman [27] tried to identify the employability qualities expected from fresh and early-career marketing graduates. The researchers verified the previous data available and identified 52 attributes and grouped them in 16 clusters. These included creativity as an important trait in addition to the Big 5 traits. Researchers also studied the construct of creativity around the Big 5 personality model which further brought out the strength of the Big 5 model [28].

Mehrabian [36] notified the importance of non-verbal communication where only 5% of the message was communicated verbally while 38% was transmitted by voice and 55% through body language. Kinesics is the understanding of bodily movements like expressions seen on the face and gesticulations. Kinesics encompasses expressions on the face, gesticulations, pose and manner, and noticeable bodily actions. It is formally termed non-verbal communication. Ray Birdwhistell, who was a ballet dancer turned anthropologist, first used the term in 1952. She was studying how people communicated using posture, gesture, stance, and movement. According to studies, kinesics can be explained as one of the many semiotic systems that individuals use in their everyday lives [37, 38]. It can be said that it is non-verbal communication where certain body movements and gestures serve as the medium of communication. Kinesics is a vital part of non-verbal conversation. Since gestures and moves of the body, or separate parts, have associated connotations, the interpretations of these meanings may be subjective. Since several gestures and actions are carried out subconsciously or at a low level of awareness, bodily actions or kinesics carry considerable risk. These can be easily misinterpreted within an intercultural conversational situation. Hans and Hans described the origin of kinesics from the word kinesis, which means “movement”. It can be explained as the study of hand, arm, body, posture, eye contact, and facial expressions.

Facial Scanning (Micro Expressions-MEs) can be defined as involuntary and rapid facial expressions that can reveal emotions that are not intended by the people to be shown [39, 40]. The study suggested that MEs are very valuable as identifying them could reveal a lot of information regarding the person's hidden behavioural traits. Observing the micro-expressions of the candidates for attributes such as anxiety, excitement, disappointment, surprise, and guilt during assessment and offering training on mitigation strategies for those not selected by the hiring managers would add value to the recruitment cycle. The study of non-verbal communication has become immensely important for HR of organizations to judge the professionalism of a potential candidate at the time of recruitment, especially body language [41]. Key kinesic aspects that were identified as hurdles to successful hiring were unprofessional body language, inappropriate dressing, and improper dinner etiquette.

Emotions have been seen as organized responses crossing boundaries of subsystems vis-à-vis physiological, motivational, experiential, and cognitive systems [45]. Emotions normally emerge in reaction to some event whether internal or external that have a positive or negative connotation for the individual. Further, they also coined emotional intelligence as the ability to understand one's feelings, and others' feelings and apply this knowledge to one's actions. Interest in emotional intelligence has greatly increased over the last 2 decades because of the growing importance of emotional intelligence in organizations and its significance for goal achievement. This is also the reason that many studies have been conducted on accurately measuring emotional intelligence [46-48]. These studies summarized and measured emotional intelligence from the prism of a set of abilities called the multifactor emotional intelligence scale.

Authors suggested that emotional intelligence in the context of performance at work can be best studied with ability measures. This is in contrast with what Goleman [49] suggested that emotional intelligence is beyond just abilities and it had certain non-ability dispositions and traits and it could also include certain beliefs about oneself. Precisely that is why the ability measures were further segregated into self-reported measures and actual measures [50]. While an understanding of self is important, people are often biased on the beliefs that they hold and consider their beliefs to be true. Senyuva et al. [51] took the ability-based line and the five-dimensional approach suggested by Goleman [49] to measure emotional intelligence vis-à-vis self-awareness, self-management, self-motivation, empathy, and social awareness. Most of these 5 traits found acceptance in other studies as well vis-à-vis self-awareness, self-management, self-motivation, and empathy [52]. The present study took the same 5 dimensions to measure emotional intelligence among potential candidates.

This study adopted the non-probability convenience sampling technique. The major reason is that the exact population is unknown and covers a wide area of service delivery. Convenience sampling techniques are the most widely used method in the empirical literature to determine the participants of the studies [60, 61]. Clark and Watson [62] suggested the sample size to be greater than 250 to perform CFA. Further, for model testing, the sample size must satisfy both item-to-subject ratios, and for every item 10 responses were needed [63]. In total 580 respondents' opinions were taken into account for the analysis.

Primary data was collected from employers who are recruiting fresh graduates through campus placements. The study included secondary sources of information from literature, news articles, and reports along with the primary sources. The opinion was collected from employers in IT companies who visit various public and private technical institutions for campus recruitment drives and hire young engineers. The companies included both Indian as well as MNCs indulging in software development, R&D, KPOs, BPOs, etc. These companies were located across India. The respondents included middle management and top management executives working in companies who are a part of the recruitment process. A total of 120 companies like Volkwvagen, Ashok Leyland, Mahindra Defense, Persistent Tech Ltd, Bajaj Auto, etc. were approached.

A survey approach was used to collect and process the data. A structured questionnaire including Administrative questions, classification questions, and target questions was designed where both administrative and classification questions were categorical in nature and target questions were with summated scale denoting (5) as strongly agree and (1) as strongly disagree was designed to register the opinion of the respondents. Accordingly, scale items were taken from different sources. Personality traits and their sub-constructs were extracted from the previous study [64]. Measurement items suggested by Bellou and Gkorezis [64] and Jung and Yoon [65] were used to measure Kinesics. Further, the scale suggested by Senyuva et al. [51] was used to measure Emotional Intelligence. Similarly, the scale items of employer satisfaction were extracted from an important study [66, 67].

Central tendency and Principal Component Analysis (PCA) were used for item reduction and uni-dimensionality [68]. Confirmatory factor analysis was used to validate and confirm the factor structure followed by path analysis to estimate the relationships among the variables [69]. Statistical software tools like, SPSS and AMOS Version 21 software were used for all tests.

Widely recommended measures for assessing the construct reliability namely Cronbach’s Alpha value, and composite reliability (CR) were taken into account [70]. According to Hair et al. [71] a value above 0.7 is acceptable and above 0.9 is excellent in proving internal consistency.

From Table 1, efforts were made to report the results of the reliability and validity of the proposed theoretical model. Items whose value was less than 0.5 were discarded. Constructs' item loadings were significant as they were above 0.5 and ranged from 0.70 to 0.91 for personality trait constructs such as Emotional stability, Agreeableness, Conscientiousness, Extraversion, and openness to experience. Moreover, the CR values range between 0.78 to 0.92. The AVE values were above 0.5 (ranging from 0.55 to 0.76) showing that all the measurement items were vital to explain the concept of their associated construct which claimed the convergent validity of the model [72].

Similarly, for emotional intelligence constructs such as self-awareness, self-management, self-motivation, empathy, and relationship control the factor loadings ranged between 0.70 to 0.90. Moreover, the internal consistency and CR values range between 0.78 to 0.91 and 0.77 to 0.856 respectively. The AVE values are above 0.5 (ranging from 0.54 to 0.72) showing that all the measurement items are vital to explain the concept of their associated construct which claims the convergent validity of the model [72]. The factor loading of kinesics ranged from 0.710 to 0.798 and for employer satisfaction, it is 0.717 to 0.979. The AVE was 0.51 and 0.702 respectively. Both constructs satisfy the threshold of internal consistency and validity.

Researchers recommended the following threshold for the goodness-of-fit model: i) chi-square/degree of freedom (CMIN/DF) should be below 5; ii) goodness-of-fit (GFI) should be above 0.9; iii) normed-fit index (NFI) should be above 0.9; iv) comparative fit index (CFI) should be above 0.9; v) Tucker Lewis index (TLI) should be above 0.9; vi) root mean square error of approximation (RMSEA) should be less than 0.08; vii) PCLOSE should be above 7; and viii) items loading should be above 0.5 [72-74].

Fig. (1). Estimated path model between personality traits, kinesics, emotional intelligence, and employer satisfaction.

Before evaluating the strength of path-based relationships in the structural model, the model fit indices of the structural model were evaluated (Table 2). The model fit indices of all the constructs were CMIN/DF between 2.43 to 4.22, GFI between 0.90 to 0.95, NFI between 0.90 to 0.933, CFI between 0.920 to 0.951, TLI between 0.911 to 0.944, IFI between 0.920 to 0.962, RMSEA = 0.047 to 0.075, and PLOSE between 0.701 to 0.9987. Thus, the outcomes indicated a good structural model fit to the data.

The structural model was estimated for the significance and strength of the relationship between personality traits, Emotional Intelligence, and kinesics with Employer satisfaction. Fig. (1) visually reports the structural model and Table 3 reports the β value, the critical ratio (t-statistics), and the p-value associated. The results provided support for the acceptance of all three research hypotheses.

These findings from the CFA analysis of the measurement model confirmed that the higher order constructs such as the personality trait and emotional intelligence were strongly predicted by its lower order constructs respectively thereby ensuring the model fitness. In estimating the structural model three hypotheses were framed and tested for strength, direction, and significance.

The analysis provided support for the acceptance of research hypotheses associated with this research objective.

Numerous theoretical and practical implications stem from the study on the influence of personality traits, kinesics, and emotional intelligence on employer satisfaction while recruiting engineering graduates. Theoretically, the work added to our understanding and supported the results of earlier research. According to the study, personality attributes have a big impact on how satisfied an employer is with a candidate. In the hiring process, kinesics and emotional intelligence are equally crucial. Professionals and potential employees should pay close attention to the study's practical consequences. The findings can be used by HR managers to create a more effective hiring procedure that chooses the best applicant for the company. The results can help job candidates better prepare for the hiring process and raise their chances of success. The study's conclusions are generally helpful for academics and industry experts who work in the hiring process.

Although the current study offered insightful information about the effects of personality traits, kinesics, and emotional intelligence on employer satisfaction when hiring graduates, there were a few limitations that must be acknowledged. The survey questionnaire was utilised as the only method of data collection for facial expressions, which may not have adequately caught the complete spectrum of facial emotions and subtleties of body language. Including additional techniques, such as video capturing, could result in a more thorough investigation. Finally, employer satisfaction is a relative term that can evolve. A longer-term study that conducts follow-up interviews with hiring managers could provide more in-depth knowledge on the matter.

The current study also offered a wide range of potential scope for additional investigation. Future studies may examine the effects of additional elements like cognitive skills, job knowledge, and work experience on hiring employer satisfaction. Investigating the relationship between these variables and personality traits, kinesics, and emotional intelligence in predicting employer satisfaction and eventually job performance may be fascinating. Additionally, a cross-cultural investigation might be carried out to look into how differences in culture might impact the association between these characteristics and employer satisfaction during recruitment. Finally, longitudinal studies might be carried out to look into the long-term impacts of these variables on staff retention, job performance, and overall organizational success.