Comparison of diagnostic methods for
\r\n\t
\r\n\tThis book will intend to provide the reader all the necessary information on apheresis with a comprehensive overview including techniques for therapeutic apheresis, indications of apheresis in the light of guidelines, adverse events associated with apheresis, as well as the care of the apheresis patient.
The number of infected by
According to studies conducted, many of the pathogenic species of
The different methods used for diagnosis range from antigenic screening (Ag) to molecular techniques. Antigenic screening techniques have been associated with a high sensitivity of a detection limit of the
The bacterium is one of the most important findings for gastroenterologists, who for years sought answers to multiple intestinal problems. This is how the gastroenterologist Walery Jaworski in 1899 after analyzing samples of human gastric expirations isolated spiral elongated bacteria and called them
So, it took 79 years for the bacteria to be rediscovered by the Australian doctors Barry Marshall and Robin Warren, who managed to make the first isolation through a pure culture in 1979. This rediscovery allowed them to be Nobel Laureates in 2005 [15].
Taxonomically, we can describe
The multiple genotypic and phenotypic characteristics are different from those of
Although in general there is no difference between the sexes, in some developed countries, there is a higher prevalence of infection in men than in women [16].
The prevalence of
Epidemiological and microbiological evidences have several transmission routes that have been proposed in the studies carried out. The gastro-oral, oral-oral, and fecal-oral routes are the most important routes of transmission [12]. Other routes of importance are also breastfeeding and iatrogenic transmission which are also included as alternating forms for the transmission of the pathogen. The possibilities of spreading the pathogen are of three possible vectors that have been suggested to maintain the viable form of the bacteria: water, food, and animals.
The prevalence of
Through molecular methods,
In another study, developed by Moreno et al. [21]; Moreno and Ferrús, [22]
On the other hand,
Those foods that have a water activity (aw) >0.97 and a pH between 4.9 and 6.0 theoretically provide the ideal conditions for the survival and development of
Vegetables are one of the foods with the highest risk of fecal contamination, since they are in contact with soil and contaminated irrigation water, which would mean the spread of
On the other hand, the ability of
Milk could also act as a vehicle for
On the other hand, Meng et al. [36] analyzed 11 raw chickens and 18 samples of tuna meat ready for consumption (sushi).
Studies have also been conducted on the presence of
The presence of
The term gastritis should be reserved for the histologically demonstrated inflammation of the gastric mucosa. Gastritis is not the mucosal erythema seen during endoscopy, nor is it interchangeable with the term “dyspepsia” [13]. On the other hand, the different etiological factors that cause gastritis are multiple and heterogeneous; to gastritis it has been classified with a chronological base (acute or chronic), such as histological typologies, anatomical distribution or its pathogenic mechanism, clinical correlation, histological data, abdominal pain or dyspepsia, and endoscopic data in gastric mucosal investigation [13].
The pathogenesis of chronic gastritis by
Second stage of the inflammatory process of the gastric mucosa by
It is the uncontrolled growth of stomach cells. Malignant tumors can originate in each of the three layers: mucosa, muscle, and serosa. This is also known as gastric cancer that originates in the stomach [41]. The risk factor is considered any caused that increases the likelihood of having a disease such as cancer, even though several risk components do not mean that the person will have the disease; Some scientists connoted that the risks that take a person to be more prone to suffer stomach cancer are several such as:
Incidence according to sex: Stomach cancer is more common in men than in women [16].
Age: The rate of stomach cancer in people over 50 years increases sharply [42].
Ethnic origin: In the United States, stomach cancer is more common among Americans of Hispanic origin, black people, and Asians and islanders compared to white people who are not of Hispanic origin [41].
Geography: On a global scale, stomach cancer is more common in Japan, China, Eastern and Southern Europe, as well as Central and South America [41].
Several risk factors for gastric cancer have been described, which play a fundamental role in their genesis, some of them remain under discussion, and others, on the contrary, have been confirmed more and more clearly [43].
Within the genetic risk factors [41], we have:
Families of patients with gastric cancer: incidence 2–3 times higher
Blood group A.
Among the environmental risk factors [41], we have:
Food (variable in each country): dried and salted fish, very spicy foods, and red meats, among others
Ingestion of alcohol, hot drinks, and sodium nitrate; chewed tobacco
Radiation.
Within the premalignant risk factors [41], we have:
Atrophic gastritis, intestinal metaplasm, and dysplasia.
Pernicious anemia (20 times more frequent than in normal subjects).
Gastric polyps: multiple hyperplasia, greater than 2 cm with some degree of dysplasia 0.4–4% of association with gastric cancer [41].
People who have suffered from a certain type of stomach lymphoma, known as lymphoma of lymphatic tissue associated with the mucosa (MALT), have an increased risk of developing adenocarcinoma of the stomach, probably due to infection with
The gastric infection produced by
Entrance and lodging of
The diagnostic methods of
The other indirect methods are based on the detection of certain characteristics of the bacteria, such as the ability to hydrolyze through urea, and based on the breath test or the response of the immune system through the measurement of specific antibodies. Its primary advantage is its noninvasive nature [44].
The presence of the germ can be recognized with the usual hematoxylin and eosin stain, although it is more easily demonstrated with other stains such as Giemsa. The histology not only demonstrates the presence of the microorganism but also informs about the morphological changes of the gastric mucosa [44].
Under optimal conditions
The selection and inoculation of the bacteria depend on the number and types of tests to be carried out as well as on the factors, type of bacteria, clinical importance of the isolation, availability of the strain, and reliable method of verification [46]. Plate culture has advantages ranging from typifying the organism to determining its sensitivity to antibacterial agents, so it is important to study it from the epidemiological point of view, because it allows knowing the pattern of resistance to different therapeutic regimens with a specificity of the 100% and a lower sensitivity than other diagnostic techniques [3]. This microorganism is also urease, oxidase, and catalase positive, characteristics that are frequently used in the identification of the microorganism, although its isolation is relatively complex [16].
It is usually considered a difficult and tedious technique. However, adopting a series of minimal precautions, most laboratories achieve the growth of the microorganism [44].
Serological techniques only indicate a previous exposure to the microorganism but do not discriminate between people with active infection and disease in healthy individuals with prior exposure to infection [44]. Rapid tests are methods for the detection of antigens and antibodies in serum, plasma, whole blood, and other fluids, which give results in a few minutes [47]. These serological techniques are widely used today for rapid diagnosis in laboratories.
The enzyme-linked immunosorbent assay (ELISA) is widely used to perform epidemiological studies on a considerable number of individuals [48].
In a work done by Siavoshi et al. [49], for the intracellular detection of
This is a chromatographic immunoassay for the qualitative detection of
Molecular methods are the names given to all the laboratory techniques used to isolate DNA or extract it in high purity, visualize it to see its state, cut it and paste it (Iglesias [51]), or amplify a region in a huge amount of molecules: fragment cloning in bacteria or other vectors such as viruses as well as polymerase chain reaction (PCR).
Infectious diseases have become the “spearhead” for the development of molecular diagnostic tests, with more than 50% of the techniques available today. The main explanation for this development is due to the difficulty of detecting a pathogen through classical microbiology [52] (Table 1).
Methods | Characteristic | Advantage | Disadvantages |
---|---|---|---|
Histological techniques | Habitual staining of hematoxylin and eosin Giemsa stain | Demonstrates the presence of the microorganism and reports on changes in the mucosa | The technique requires samples obtained from a biopsy Proper selection of stain fixatives |
Culture | Cultivation of the microorganism in specific media under microaerobic conditions The optimum temperature of culture is from 35 to 37°C [53] | Isolate the microorganism to study its behavior (in vitro) | It is difficult to isolate, since Samples destined for culture remain viable for approximately 5 hours and when stored in saline at 4°C or for more than 24 hours if stored at 4°C in a transport medium specific for Another disadvantage is the high contamination of the environment with accompanying biota, which makes it difficult to isolate |
Serological techniques | Methods for the detection of antigens and antibodies (serum, plasma, and whole blood, among others) Enzyme-linked immunosorbent assay (ELISA) | Rapid laboratory tests | It can induce a false-negative result |
Antigenic screening | Chromatographic immunoassay for the detection of | Rapid laboratory tests | Possible false positives due to cross reactions with other organisms [56] |
Molecular Methods | DNA amplification of the pathogen | Great versatility as an analysis technique, sequences are amplified from minute amounts of target DNA, even from DNA contained in a single cell | Need to have information on the target DNA sequence Short size of the PCR products The ease with which DNA is amplified requires avoiding the danger of contamination inherent to the multiplier power of the reaction |
Comparison of diagnostic methods for
It has become increasingly common to see people interacting with their smartphones while performing other activities at any time and place. There are millions of applications that can be used to keep in contact with family and friends, order food, take a taxi, book a hotel, set up a blind date, or simply to be reachable at work. However, despite this wide variety of applications and content to be created and visited, it is not surprising that most of the time spent on the phone is destined to the use of social networks. According to [1], smartphone, along with its cousin, tablet and a fast-expanding family of wearables and other smart devices are transforming the way people live, work, play, connect, and interact. Martin [2] suggests that the time spent in the digital world on mobile devices such as smartphones and tablets is longer than the time spent on traditional devices such as computers and laptops, in some countries like Indonesia or India, where the use of smartphones and tablets accounts for 90% of the time spent on digital media. Undoubtedly, this is due to a greater access to these devices, with increasingly cheaper purchasing options and better features, to the extent that today these technologies can perfectly replace laptops for many people. Additionally, another factor that indeed played an important role in the increased use of mobile devices is mobile apps. In this sense, we can confirm that mobile devices definitely changed digital entertainment.
\nThe influence of mobile apps is evidenced by the fact that their use represents more than 90% of the time spent on smartphones and tablets, with Latin American countries on the top of the list: Argentina (94%), Mexico (92%), and Brazil (89%). In addition, mobile devices are used more than traditional devices like computers to access the digital world. This behavior is common in users of all ages, but it is more concentrated in women [2]. Regarding the reach of mobile apps, the app universe is dominated by a small group, with 96% of the time spent on no more than 10 apps [2], most of them corresponding to social networks such as Facebook, Instagram, and WhatsApp.
\nIn connection, according to Smith [3], Facebook has more than 2.234 billion active users per month. Sixty-six percent of these users use the app daily, out of which 51% visit it several times per day, which translates to more than 2 trillion posts and 1.13 trillion likes since the launch of the app.
\nAnother popular social network is Instagram. This app has 1 billion active users per month. Twenty-two percent of them use it daily, out of which 38% check the app multiple times during the day [3].
\nFor its part, WhatsApp, a free app that offers messaging and calls in a simple, safe, and reliable way to phones all over the world, has more than 1.8 billion active users. Seventy percent of them use the app daily, which translates into 65 billion messages sent, 100 million voice calls, and 55 million video calls per day [3].
\nThese data seem to indicate that the use of WhatsApp is more intensive than other popular social networks, even though it is not the most popular social network, and some people could categorize it as a messaging application instead of a social network. In view of this, the research question that arises is what makes users prefer this social network? Is an attractive proposal and design enough to capture a large quantity of people, or there is something else in play? Is it only on the users to decide what social networks will be used? This study seeks to answer these questions, specifically regarding the intention to use WhatsApp and using an adaptation of the technology acceptance model known as UTAUT2 to achieve this goal [4].
\nCreated as an extension to the world of the theory of reasoned action [5], the technology acceptance model [6] corresponds to one of the most renowned, analyzed, and studied models in the literature. This model seeks to understand how and why users accept and use a technology, using perceived ease of use and perceived usefulness as prediction variables of the intention to use. TAM2 was created after TAM and explains the intentions to use a specific technology in terms of social influence and cognitive processes [7]. To this end, the model incorporates constructs such as subjective norm, image, job relevance, output quality, result demonstrability, experience, and voluntariness. Three years later, the unified theory of acceptance and use of technology come to the fore [8]. This theory seeks to predict the intention to use through the variable performance expectancy, effort expectancy, and social influence, which are defined in a way very similar to perceived usefulness, perceived ease of use, and subjective norm, respectively. This set of variables adds up to facilitating conditions, which have a direct effect on usage behavior, and is defined as the extent to which the individual believes that certain organizational and technical infrastructures exist to support the use of a system [8]. Two new variables incorporated into the model correspond to gender and age, which are moderating variables, as well as experience and voluntariness. The UTAUT2 emerged more recently, as an extension of the UTAUT and to study the acceptance and use of technologies in a consumption context [4]. This model incorporates three new variables, namely, hedonic motivation, price value, and habit.
\nAs for social networks, several authors attempt to explain the use of online social networks (OSNs). According to Schneider et al. [9], users commonly spend more than half an hour interacting with OSNs, and the byte contributions per OSN session are relatively small. From this result, we could assume that most users are consumers and not content creators. In the case of Facebook, Ellison et al. [10] propose that this network might provide greater benefits for users experiencing low self-esteem and low life satisfaction. As for Twitter, Java et al. [11] suggest that people use microblogging to talk about their daily activities and to seek or share information. With respect to Instagram, motives were positively associated with both usage and self-presentation [12]. People use social networks such as Facebook, Twitter, and Instagram for the sole purpose of entertainment and maintaining contacts with their friends’ list [13]. As may be seen, the motivations to use social networks are varied. According to Brandtzæg and Heim [14], people use social networks to get in contact with new people, to keep in touch with their friends, and general socializing, and this could be closely related to the variable social influence proposed as a latent exogenous variable in the UTAUT2 [4]. Xu et al. [15] also suggested that user utilitarian gratifications of immediate access and coordination; hedonic gratifications of affection and leisure—which could be related to perceived usefulness and perceived ease of use, respectively; and website social presence were three positive predictors of social network site usage.
\nRegarding WhatsApp, there are specific motivators linked to cost, sense of community, and immediacy [16], as well as to unlocking new opportunities for intimate communication [17]; addictive behaviors have even been detected toward the application [18]. A number of studies about the use of this innovative technology have been conducted [19], which have detected a series of factors that positively or negatively influence the use of WhatsApp, such as the importance of family groups [20], the use of status within the application [21], interactions with the education field [22, 23], and concerns about privacy [24], among others. This study intends to analyze WhatsApp consumer behavior from the perspective of the variables that influence the intention to use this technology and to determine what these variables are and how they articulate to affect the intention to use WhatsApp, using the variables proposed in the UTAUT2 model [4].
\nThe proposed model considers the following latent exogenous variables that explain the intention to use WhatsApp. The model proposes that the following four variables positively influence the latent endogenous variable behavior.
\nH1: Hedonic motivation is defined as the pleasure individuals feel when they behave in a certain way or perform a specific activity [25].
\nH2: Performance expectancy is the extent to which using a technology will provide benefits to consumers in performing certain activities [4].
\nH3: Effort expectancy is the degree of ease associated with the consumers’ use of technology [4].
\nH4: Social influence is the extent to which consumers perceive that importantly others believe that they should use a particular technology [4]. This social influence or subjective norm is closely related to the intention to use a social network [26, 27, 28].
\nBehavioral intention is defined as the set of motivational factors that indicate how willing people are to try or how much effort people intend to put forth to develop a particular behavior [29].
\nThe structural model with the latent variables and their proposed relationships is shown in Figure 1.
\nProposed model.
In relation to the latent variables included in the structural model, Table 1 shows the observable variables measured in the questionnaire. It must be noted that these variables were measured through a Likert scale that ranged from 1 to 5, where 1 means “totally disagree” and 5 “totally agree.”
\nI find WhatsApp useful in my daily life | \n
Using WhatsApp increases my chances of achieving things that are important to me | \n
Using WhatsApp helps me accomplish things more quickly | \n
Using WhatsApp increases my productivity | \n
Learning how to use WhatsApp is easy for me | \n
My interaction with WhatsApp is clear and understandable | \n
I find WhatsApp easy to use | \n
It is easy for me to become skillful at using WhatsApp | \n
Using WhatsApp is fun | \n
Using WhatsApp is enjoyable | \n
Using WhatsApp is very entertaining | \n
People who are important to me think that I should use WhatsApp | \n
People who influence my behavior think that I should use WhatsApp | \n
People whose opinions that I value prefer that I use WhatsApp | \n
I intend to continue using WhatsApp in the future | \n
I will always try to use WhatsApp in my daily life | \n
I plan to continue to use WhatsApp frequently | \n
Observed variables.
The first four variables refer to performance expectancy, while the four next questions refer to effort expectancy. Then, the following three variables refer to hedonic motivation and the next three to social influence. Finally, the last three questions refer to the behavioral intention of using WhatsApp. The questionnaire also contains variables to measure demographic information such as sex, age, and level of education completed, as well as questions to measure behavioral variables such as the number of hours per day spent using WhatsApp and the number of times per day that respondents use this application.
\nTo conduct this study, a questionnaire was applied that contained the observable variables described above, as well as the questions for demographic characterization. The instrument was applied to 579 people through SurveyMonkey. Sampling was non-probabilistic and by convenience and targeted young people who use the Internet and social networks. The survey was sent through these two channels.
\nOnce answers were collected, a univariate analysis was conducted to obtain the respondents’ profile. Afterward, the reliability and internal consistency of each proposed construct were assessed by a Cronbach’s alpha test. Finally, the structural analysis proposed in Figure 1 was carried out using the software IBM SPSS Amos, taking care to obtain adequate absolute, incremental, and parsimony adjustments. Once the structural equation model analysis was completed, a cluster segmentation analysis was executed to determine the different profiles of WhatsApp users based on the answers of the attitude variables proposed in the model.
\nFirst, with respect to the descriptive analysis of the questionnaire answers and as mentioned above, 579 questionnaires were filled out. Fifty-seven percent of respondents are women, 60% of the sample are university students, and 25% completed higher education. In terms of age, the sample is concentrated in an age range from 20 to 40 years, with a mean age of 25.6 years.
\nRegarding the total of respondents, as shown in Figure 2, 62.3% express using WhatsApp for leisure, followed by 23.4% that use it for informative purposes.
\nMain reason why WhatsApp is used.
When comparing the motives to use WhatsApp by sex, the trend remains constant, with 62.4% of men using WhatsApp for leisure, against 57.5% of women. No significant differences are observed by age and educational level.
\nRegarding frequency of use, most people report using WhatsApp several times per day (89%): 90.4% for women and 87.1% for men. In this same line, with respect to the observable variables of intention to use, 58.4% of men and 63.6% of women declare totally agreeing that they will continue to use WhatsApp in the future (I intend to continue using WhatsApp in the future), 36.4% of men and 40.0% of women totally agree that they use WhatsApp in their daily life (I will always try to use WhatsApp in my daily life), and 48.0% of men and 53.5% of women declare total agreement with using WhatsApp frequently (I plan to continue to use WhatsApp frequently). In sum, for the three observable variables of the factor intention to use, the percentage of women who totally agree is slightly higher than that of men, and therefore, we can assume that women are more likely to keep using WhatsApp in the future. As seen in the first section, women tend to use mobile devices to access online content more than men, and this trend evidently replicates itself in the use of a mobile app like WhatsApp.
\nContinuing with the analysis, a Cronbach’s alpha reliability test is conducted. Results for each of the four factors proposed in the model from Figure 1 as latent exogenous variables and the latent endogenous variable behavioral intention are shown in Table 2.
\nPerformance expectancy | \n0.736 | \n
Effort expectancy | \n0.801 | \n
Social influence | \n0.867 | \n
Hedonic motivation | \n0.725 | \n
Behavioral intention | \n0.812 | \n
Cronbach’s α reliability analysis.
All five structural variables yielded satisfactory results in terms of construct reliability, with results over 0.700 in all cases.
\nSubsequently, the structural model was analyzed using IBM SPSS Amos, obtaining adequate absolute, incremental, and parsimony adjustment. Figure 3 shows the structural equation modeling, in which all relations proposed are significant (p value < 0.001).
\nResult model.
As shown in Figure 3, the model reaches R2 = 0.52 to model behavioral intention. In this sense, the most influencing variable is hedonic motivation, with a standardized regression weight of 0.499. Consequently, people use WhatsApp mainly motivated by pleasure, entertainment, and leisure. This is also related to the 62.3% of the sample that stated using WhatsApp for leisure. The other variables that explain the behavioral intention to use this instant messaging application are social influence with a standardized estimate of 0.333, followed by performance expectancy (0.305) and effort expectancy (0.256).
\nRevising observable variables, it must be noted first that for the factor hedonic motivation, the variable “using WhatsApp is fun” obtains a very high mean of 4.37, while the mode is 5, that is, “totally agree.” Indeed, people find that using this app is fun (Table 3).
\n\n | Mean | \nStd. deviation | \n
---|---|---|
Using WhatsApp is fun | \n4.37 | \n0.84 | \n
Using WhatsApp is enjoyable | \n4.28 | \n0.89 | \n
Using WhatsApp is very entertaining | \n4.28 | \n0.83 | \n
Observable variables of hedonic motivation.
Additionally, it is interesting to observe social influence, in which the variable “people who are important to me think that I should use WhatsApp” obtains a mean of 3.57 and a mode of 3, which implies a certain degree of indifference to social influence. In fact, if the three observable variables of social influence are considered, the mean obtained is 3.75. This might be explained by the fact that individuals do not consider others’ opinions to be very relevant when using WhatsApp. This is supported by the standardized regression coefficient 0.333 for this latent exogenous variable in the structural equation model, which albeit statistically significantly does not represent a high impact. A second reading of these results shows that people who are important to the respondent might not approve of the use of WhatsApp by the respondent, shedding some light on a control problem related to this behavior or perhaps an addiction, as mentioned in the literature review. A third reading leads us to conclude that as WhatsApp is widely used, the opinion of people important to the individual is not absolutely important to him, and what would probably matter would be whether those people use the app. Regardless of the underlying reason, the result is relevant because WhatsApp is an application that allows people to communicate with family and friends, and therefore, a high valuation was expected (between 4 and 5) for the social influence factor (Table 4).
\n\n | Mean | \nStd. deviation | \n
---|---|---|
People who are important to me think that I should use WhatsApp | \n3.85 | \n1.07 | \n
People who influence my behavior think that I should use WhatsApp | \n3.57 | \n1.09 | \n
People whose opinions that I value prefer that I use WhatsApp | \n3.84 | \n1.08 | \n
Observable variables of social influence.
Regarding the observable variables of effort expectancy, Table 5 shows that all have a rather high mean, between 4 and 5, with a standard deviation lower than 0.80 in all cases. In fact, WhatsApp is perceived as easy to use, which, according to the structural equation model, positively contributes to the intention to use the application but to a lesser extent than the other latent variables proposed in the model.
\n\n | Mean | \nStd. deviation | \n
---|---|---|
Learning how to use WhatsApp is easy for me | \n4.66 | \n0.74 | \n
My interaction with WhatsApp is clear and understandable | \n4.54 | \n0.74 | \n
I find WhatsApp easy to use | \n4.68 | \n0.71 | \n
It is easy for me to become skillful at using WhatsApp | \n4.58 | \n0.80 | \n
Observable variables of effort expectancy.
In the case of performance expectancy, it is noteworthy that the observable variable “using WhatsApp increases my productivity” has a high dispersion, with a standard deviation of 1.366 and a mean of 3.07. This casts some doubts on the applicability of the variable to a technology of these characteristics. However, if this variable is removed from the study, the scale of performance expectancy maintains a Cronbach’s α of 0.783; therefore, it should not be eliminated. Considering the high dispersion of the answers represented in the standard deviation, we will delve into this point by means of a cluster analysis, since the existence of different segments could help understand this result (Table 6).
\n\n | Mean | \nStd. deviation | \n
---|---|---|
I find WhatsApp useful in my daily life | \n4.47 | \n0.86 | \n
Using WhatsApp increases my chances of achieving things that are important to me | \n3.72 | \n1.21 | \n
Using WhatsApp helps me accomplish things more quickly | \n4.02 | \n1.12 | \n
Using WhatsApp increases my productivity | \n3.07 | \n1.37 | \n
Observable variables of performance expectancy.
With the aim of elaborating on the results, a cluster analysis is conducted based on the observable variables of the proposed model. Two segments were found, which do not differ in sex or age but in the usefulness perception of WhatsApp. The values of the four observable variables of performance expectancy are presented in Table 7.
\n\n | Cluster 1 | \nCluster 2 | \n
---|---|---|
I find WhatsApp useful in my daily life | \n4.01 | \n4.83 | \n
Using WhatsApp increases my chances of achieving things that are important to me | \n2.8 | \n4.21 | \n
Using WhatsApp helps me accomplish things more quickly | \n3.22 | \n4.54 | \n
Using WhatsApp increases my productivity | \n2.3 | \n3.55 | \n
Cluster analysis and performance expectancy.
A very low valuation by users from Cluster 1 is observed for the variables “using WhatsApp increases my chances of achieving things that are important to me,” “using WhatsApp increases my productivity,” and “using WhatsApp helps me accomplish things more quickly.” This seems to point to the existence of a certain degree of indifference and even disagreement with the contribution of WhatsApp in terms of productivity and utility for the user. In other words, although people from Cluster 1 perceive WhatsApp as useful for their daily routine, this usefulness is not understood as a contribution to their productivity and performance in matters important to them but as a self-referential usefulness. This is completely in agreement with the results, which indicate that people use WhatsApp motivated mainly by leisure, as observed in the first part of the analysis. In addition, individuals from Cluster 1 declare that WhatsApp is a useful app, but it does not help them in their tasks. From this, the following question arises: what is it useful for? Probably this answer has to do with certain degree of causality over other variables, for example, WhatsApp is useful to reach friends or to entertain yourself. But, it is definitely useful in contexts linked to leisure and unrelated to the user’s productivity and duties.
\nIt must be also noted that the highest educational level completed by people from Cluster 1 is concentrated in university education and secondary education, while Cluster 2 users have university education and postgraduate studies. This can help explain the differences in performance expectancy, since Cluster 1, represented by university students, does not perceive WhatsApp as a support to its tasks and duties, while Cluster 2, represented by people already in the world of work, does see WhatsApp as a supporting tool for their activities and obligations. This leads us to believe that albeit not the focus of this study, WhatsApp could have a positioning associated with social network and leisure for Cluster 1 and one associated with a messaging app for Cluster 2.
\nWhen analyzing the main motive that each cluster has for using WhatsApp, the trend remains constant, with leisure as the dominant motive for Cluster 1 (72.5%) and Cluster 2 (56.4%), although the percentage is slightly lower in the latter, as shown in Table 8.
\n\n | Cluster 1 (%) | \nCluster 2 (%) | \n
---|---|---|
Work | \n7.0 | \n10.7 | \n
Leisure | \n72.5 | \n56.4 | \n
Academic | \n4.2 | \n11.4 | \n
Informative | \n16.2 | \n21.4 | \n
Cluster analysis and motivation.
Even when in both cases the major motivation is leisure, it must be noted that differences exist in terms of the distribution of the same. In the case of Cluster 2, the other motives become important, particularly the motive “informative” (21.4%). Moreover, if we had a dichotomy between leisure and non-leisure, in which non-leisure comprises work, study, and information, Cluster 2 would be totally balanced between people who use WhatsApp for leisure and people who use it for other reasons. This obviously helps understand the differences between both segments with respect to the latent exogenous variable “performance expectancy.”
\nThen, an analysis of structural equations is performed seeking to test the proposed model through the samples of the two separate clusters. In this regard, although model fit indices are not optimal, it is necessary to emphasize that the relative weight of each latent exogenous variable on the intention to use WhatsApp responds to the differences shown by both clusters in the previous descriptive analysis. Figures 4 and 5 show the results of each analysis of structural equations for each cluster.
\nCluster 1 result model.
Cluster 2 result model.
As shown in Figure 4, the most influencing variable is hedonic motivation, with a standardized regression weight of 0.448. Consequently, Cluster 1 uses WhatsApp mainly motivated by pleasure, entertainment, and leisure. This is also related to the 72.5% of the cluster that stated using WhatsApp for leisure. The other variables that explain the behavioral intention to use this instant messaging application are social influence with a standardized estimate of 0.311, followed by performance expectancy (0.386) and effort expectancy (0.261). As can be seen, Cluster 1 highlights the importance of hedonic motivation against the other variables with respect to the total sample.
\nAs shown in Figure 5, the most influencing variable is hedonic motivation, with a standardized regression weight of 0.442. Consequently, Cluster 2 also uses WhatsApp mainly motivated by pleasure, entertainment, and leisure. This is also related to more than a half of the cluster stated using WhatsApp for leisure. The other variables that explain the behavioral intention to use this instant messaging application are social influence with a standardized estimate of 0.272, followed by performance expectancy (0.351) and effort expectancy (0.321). As can be seen, this cluster also highlights the importance of hedonic motivation against the other variables with respect to the total sample, but there is an increase in the relative weight of the effort expectancy.
\nTo close this point, the results obtained invite us to revise whether the observable variables proposed for performance expectancy can be applied to a technology that is perceived as pertaining to leisure and entertainment.
\nBased on the results of this study, we observe that the most influential variable for the intention to use WhatsApp is hedonic motivation, i.e., people use WhatsApp because it is fun, enjoyable, and very entertaining. This adds up to what is expressed by respondents, who report that their main motivation to use WhatsApp is leisure, which lead us to think that this application is seen more as an entertainment than a communication tool or, in other words, people use this app to communicate with people they are close to and seek entertainment in that interaction, as well as new opportunities for intimate communication [17].
\nAlthough all the variables are significant in the proposed model, the low impact of the variable social influence must be highlighted, as this was expected to be much more influential, considering that WhatsApp is an instant messaging application that does not work if people important to the user are not using it. This may indicate that social influence, in the case of an application already in use, translates into whether or not people important to the user’s decision-making process use the app and is not related to the opinion these people have regarding the user’s conduct.
\nFurthermore, the variability of the responses in the observable variables of the dimension performance expectancy seems to indicate that WhatsApp may have use drivers that vary among groups of people. To support this hypothesis, the cluster analysis yields two groups of users, whose main difference is related to the valuation of the observable variables of the dimension performance expectancy as well as the main motivation to use this application. As mentioned above, people in general use WhatsApp for leisure and entertainment; however, a great part of Cluster 2 declares that they use the app for motives other than leisure. Consequently, Cluster 1 presents valuations of indifference and disagreement regarding the usefulness of WhatsApp for their activities and duties. Consequently, there are at least two different segments of WhatsApp users motivated by different reasons, even though cross-cutting leisure and entertainment is the main motivation to continue using this technology.
\nFrom an AI/ML perspective, this study helps to guide the way in which the software behind WhatsApp should conduct their different learning processes about the user. Indeed, if we consider the results described above where hedonic motivation and social influence are the variables with the greatest influence on behavioral intention, WhatsApp should aim to develop its ML in the sense of generating updates that allow greater fun and entertainment to its users, with virtual reality features, camera filters, new emojis, photo effects and filters, and even games. In summary, WhatsApp could guide their ML to know in a better way how the user enjoys using WhatsApp, making it an increasingly entertaining mobile application.
\nLikewise, considering the social influence, WhatsApp should guide its AI toward a greater social role as a messaging application, similar to Facebook and other social networks, where the user can connect with new “recommended friends,” for example, according to their common interests and according to the number of contacts in common. In summary, the development of AI in WhatsApp could be oriented to a social network role over a simple instant messaging application.
\nFinally, the present chapter helps to understand what variables are involved in the behavior of users of these applications, information that can be used for the development of AI/ML ability of this application, making it adaptive to the needs of the user that can vary according to the context and the expected benefit, as our cluster analysis shows, ergo, to find ways to present the right service at the right time and with the right quality [30]. As stated before, this study also evidences the existence of consumer clusters in which users satisfy different needs with this mobile application, which indeed represents new opportunities for similar applications that aim to challenge the dominance of WhatsApp on the instant messaging field.
\nWhatsApp, Instagram, and Facebook are registered trademarks, and the author only uses them as a reference in his study due to the high level of use by the world population.
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