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Master Degree Program in Data-Driven Marketing
TITLE
Investigating the effects of warmth and competence of service providers on consumer’s Prosocial behaviours during Covid-19
Ayodele Omosele
Dissertation
presented as partial requirement for obtaining the Master Degree Program in Data-Driven Marketing
NOVA Information Management School
Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa
MDDM
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NOVA Information Management School
Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa
Investigating the effects of warmth and competence of service providers on consumer’s Prosocial behaviours during Covid-19
By/por
Ayodele Omosele
Master Thesis / Project Work presented as partial requirement for obtaining the Master’s degree in Data-Driven Marketing, with a specialization in marketing intelligence
Supervisor/Orientador(a): Professor Diego Costa Pinto Or Co-Supervisors/Co-Orientadores: Professor Rafael Wagner
November, 2022
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ABSTRACT
The COVID-19 pandemic represents a massive global crisis on multi-lateral levels, with the disruption and change in the habitual behaviour of individuals. It becomes imperative to
understand how consumer’s respond to behaviour and how these responses help cater to theproblems that has come with the pandemic. Therefore this study aims to investigate the
effects of the warmth and competence of service providers on consumer’s pro-socialbehaviour during COVID-19. Using primary data obtained through the distribution of a structured questionnaire that focuses on the study aim and objectives, the study analyses the data using statistical packages to determine whether to either accept or reject the hypothesis based on the outcome of the analysis. The outcome of the analysis will inform the recommendations and conclusions. This study concluded by discussing the implications for the propounded theory, managerial implications, the research implications, the research limitations, and future research suggestions.
KEYWORDS
Prosocial behavior; Service Provider; Consumers; Warmth; Competence.
Sustainable Development Goals (SGD):
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INDEX
1. Introduction ... 1
2. Literature review ... 4
3. Conceptual Framework ... 13
4. Methodology ... 18
5. Empirical Study ... 20
6. Results and discussion ... 52
7. Conclusions and future works ... 54
Bibliographical REFERENCES ... 58
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LIST OF FIGURES
Fig 1. The conceptual map of this research
Fig 2. A service provider’s warmth being regressed on a prosocial behaviour, attention
Fig 3. A service provider’s competence model being regressed on the prosocial behaviour - attention Fig 4. A service provider’s warmth being regressed on a prosocial behaviour, donation Fig 5. A service provider’s competence being regressed on the prosocial behaviour, donation Fig 6. A service provider’s warmth being regressed on the prosocial behaviour, volunteerism Fig 7. A service provider’s competence being regressed on the prosocial behaviour, volunteer Fig 8. A service provider’s warmth being regressed on the prosocial behaviour, cooperation Fig 9. A service provider’s competence being regressed on the prosocial behaviour, cooperation
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LIST OF TABLES
Table 1. Sample Data: Gender Table 2. Sample Data: Age Table 3. Sample Data: Education Table 4. Sample Data: Employment Table 5. Sample Data: Monthly Income Table 6. The survey questions
Table 7. The dependent variable and independent variables.
Table 8. Bivariate regression of each IV for warmth on the DV, attention
Table 9. A coefficients table: Multi-linear regression of warmth IVs on the DV, attention, to answer H1.
Table 10. An ANOVAtable: Multi-linear regression of the IVs of warmth on the DV, attention, to test H1.
Table 11. A summary of the warmth model regressed on the DV, attention.
Table 12. Bivariate regression of each IV of competence on the DV – attention
Table 13. A coefficients table: multi-linear regression of competence IVs on the DV, attention, to answer H2.
Table 14. An ANOVAtable: multi-linear regression of the IVs of competence on the DV, attention, to answer H1.
Table 15. A summary of the warmth model regressed on the DV, attention.
Table 16. Bivariate regression of each warmth IV on the DV - donate
Table 17. A coefficients table: Multi-linear regression of the IVs on the DV, donate, to answer H1.
Table 18. An ANOVAtable: Multi-linear regression of the IVs of warmth on the DV, donate, to answer H1.
Table 19. A summary of the warmth model regressed on the DV, donation.
Table 20. Bivariate regression of each competence IV on the DV, donate.
Table 21. A coefficients table: Multi-linear regression of the IVs of competence on the DV, donate, to answer H2.
Table 22. An ANOVAtable: Multi-linear regression of the IVs of competence on the DV, donate, to answer H2.
Table 23. A summary of the warmth model regressed on the DV, donation.
Table 24. Bivariate regression of each IV for warmth on the DV, volunteer
Table 25. A coefficients table: Multi-linear regression of the warmth IVs on the DV, volunteer, to answer H1.
Table 26. An ANOVAtable: Multi-linear regression of the IVs of warmth on the DV, attention, to test H1.
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Table 27. A summary of the warmth model regressed on the DV, volunteer Table 28. Bivariate regression of each IV of competence on the DV, volunteer
Table 29. A coefficients table: Multi-linear regression of the competence IVs on the DV, volunteer, to answer H2.
Table 30. An ANOVA table: Multi-linear regression of the IVs of competence on the DV, volunteer, to answer H2.
Table 31. A summary of the competence model regressed on the DV, volunteer.
Table 32. Bivariate regression of each IV for warmth on the DV, cooperation.
Table 33. A coefficients table: Multi-linear regression of the warmth IVs on the DV, cooperation, to answer H1.
Table 34. An ANOVA table: Multi-linear regression of the IVs of warmth on the DV, cooperation, to test H1.
Table 35. A summary of the competence model regressed on the DV, volunteer.
Table 36. Bivariate regression of each IV of competence on the DV, cooperation.
Table 37. A coefficients table: Multi-linear regression of the IVs on the DV, cooperation, to answer H2.
Table 38. An ANOVAtable: Multi-linear regression of the IVs of competence on the DV, cooperation, to answer H2.
Table 39. A summary of the competence model regressed on the DV, cooperation.
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LIST OF ABBREVIATIONS AND ACRONYMS
IV Independent Varable DV Dependent Variable
H1 Hypothsis 1
H2 Hypotesis 2
Q Question
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1. INTRODUCTION
The outbreak of the novel Coronavirus came with a great deal of uncertainty. As the virus spread rapidly across the globe, various countries adopted strict measures and actions to curb the spread.
New guidelines on engagement in public spaces were rolled out while cities ravaged by the virus spread were forced to embark on a total lock down. This resulted in an unexpected new way of life which brought about people living in isolation, rapid changes to lifestyle, job loss and financial hardship to affect the mental health and wellbeing of many individuals experiencing these changes.
As countries struggle to manage the spread of the Coronavirus by introducing policy measures such as public health restrictions and social distancing, the non-essential retail sector, in particular, has been severely impacted. The total sales volume in Great Britain has fallen by 1.9% compared with records from 2019, making it the most significant annual fall since records began (ONS,2021). Similarly, in the United States, retail and food services sales between February and April 2020 were down 7.7%
compared to the same period in 2019 (OECD, 2019).
Hence, it becomes imperative to "engage novel intervention to protect mental wellbeing, including those based on positive, mechanistically based components" (Holmes et al., 2020). Here, we assess the efficacy of a service provider's warmth and competence on consumers' prosocial behaviour during the pandemic; that is, how consumers are influenced to benefit others by donating, cooperating and volunteering during COVID 19. The outbreak has created the most vulnerable situations for people as expected in times of great uncertainty. When people are uncertain, they become more susceptible and open to external factors that can influence how they behave or react during such periods.
Furthermore, a key concept in studying natural hazards and catastrophic events is vulnerability, exposed by the propensity to suffer some degree of loss from a hazardous event (Etkin et al., 2004).
However, vulnerability refers to how exposed the system is to natural hazards or catastrophic events, not how resilient it is. In this case, the consumers affected by the Coronavirus experience the threat
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(Turner et al., 2003). Thus, it is imperative to study how varying factors influence behaviour during periods of uncertainty, such as during COVID 19. Prosocial behaviour is, however, one of the most prominent human behaviours during times of disaster and trauma, like the recent pandemic.
Having elucidated on the recent pandemic and its relationship with prosocial behaviour, how do we define prosocial behaviour and how is it different from helping behaviour? Helping behaviour, prosocial behaviour, and altruism are frequently used interchangeably (Beirhoof, 2002). Nonetheless, there are also distinct definitions for each. Piliavin et. al defines helping behaviour as “an action that has the consequence of providing some benefit to or improving the well-being of another person”
(Dovido, Pilivan, Schroeder, & Penner, 2006, p.22). This is why Bierhoff (2002) opines that the definition of prosocial behaviour is narrower because “helping” is not considered a prosocial behaviour if the act is motivated by professional obligations. A nurse caring for a patient is not considered a prosocial behaviour as it is part of his/her job. Nonetheless, Pilivan, Dovidio, Gaertner, and Clark (1981) infers that the definition of prosocial behaviour depends largely on culture. They, therefore, suggest prosocial behaviour is “defined by society as behaviour generally beneficial to other people and to an ongoing social system.” With this definition, it is safe to claim that professional obligations are also examples of prosocial behaviour (even a nurse might help harshly which is not prosocial behaviour).
Hence, this research agrees strongly with Piliavan’s latter definition, and hence will use “helping behaviour” and “prosocial behaviour” synonymously.
Humans are considered to be a highly prosocial species (Burkart et al., 2014). Individuals generally give up a part of their time, money, skills, blood, and organs to others in very critical circumstances (Aknin
& Whillans, 2020). Insightful research has concluded that prosocial behaviour has been shown to persist and sometimes flourish marginally in times of emergency (Lowe & Fothergill, 2003) and even most recently (Zaki, 2020). Various studies have extensively covered the areas of a service provider's influence on consumer buying behaviour. All service providers must effectively prospect, manage
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pipelines and uncover pain while also capturing benefits. They must connect on a tangible level emotionally to earn the trust of their consumer. The delivery of service occurs when the service provider and the consumer (the service encounter) interact. Consumers most often compare their perceived quality of service provided by the service provider and compare them with their estimated ones.
Prosocial behaviour contributes to preventing the spread of an epidemic and improving people's mental health. According to Eisenberg and Mussen (1989), prosocial behaviour refers to voluntary actions intended to help or benefit another individual or group of individuals. It is essential to explore the relationship between service providers and consumers' prosocial tendencies during this pandemic.
This work focuses on how the competence and warmth of a service provider spur consumers to actively cooperate, volunteer and donate during the pandemic. Also, the study measures whether the competence or warmth of service providers generate prosocial behaviours including wearing masks, social distance, living indoors, and limiting movement at events/social gatherings. In doing so, however, the study seeks to determine whether customers' prosocial behaviour is influenced either by the warmth or competence of service providers.
This study is, therefore, organized as follows: the next section contains the theoretical background on consumers’ prosocial behaviour where the recent articles and journals on competence and warmth are reviewed, and different scholars’ postulations on the effects of helping behaviour are discussed;
Section 3 presents the conceptual framework which scrupulously identifies the variables used in Section 4 to conduct the research and how we have gathered data; Section 4 embodies the research results; Section 5 discusses the final results as I analyse significant findings and limitations reached during the study; and finally, in the last part of this section we present a brief discussion on future research areas to help advance the scope.
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2. LITERATURE REVIEW
This study’s literature review has a thematic organisation. This study starts by reviewing literatures that have addressed the theme of warmth and competence. Secondly, it reviewed other researcher’s evaluation of warmth and competence. Also reviewed is the topic of people’s social perception of warmth and competence. Furthermore, it reviewed other scholarly journals that highlighted the differences between consumers’ relevance of warmth and competence. Penultimately, research works which have investigated the idea of prosocial behaviour solely, without referencing any other concept, are also reviewed. Ultimately, influenced by the reviewed literatures, this study ends its literature review by designing its conceptual framework. The conceptual framework depicts graphically and also qualitatively the relationship between the independent and dependent variables. The conceptual framework has two aims.
The first aim of the conceptual framework is to provide a graphical format of this study’s variables, which help answer the research questions. These questions are as follows:
Question 1: Does the competence or warmth of a service provider influence a consumer to draw his/her attention to the current COVID-19 cases?
Question 2: Does the competence or warmth of a service provider influence a consumer to donate towards COVID 19?
Question 3: Does the competence or warmth of a service provider influence a consumer to volunteer towards COVID 19?
Question 4: Does the competence or warmth of a service provider influence a consumer to cooperate with COVID-19 guidelines?
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The second aim of the conceptual framework is to help us derive a theory from this study’s hypotheses.
The hypotheses of this research are as follows:
Hypothesis 1: The warmth of a service provider has an influence on a consumer’s prosocial behaviour.
Hypothesis 2: The competence of a service provider has an influence on a consumer’s prosocial behaviour.
The subsequent subsections are reviews of literatures that are relevant to this research’s questions and hypotheses.
2.1 Warmth and Competence
Recent research in social cognition theory has discovered direct associations with warmth and prosocial behaviour. Fiske, Cuddy & Glick (2018) stipulate that warmth captures traits often related to perceived intent, including friendliness, helpfulness, morality, and sincerity. William & Bargh (2008) concluded that providing a physical appearance of warmth increases positive interpersonal feelings and promotes prosocial behaviour.
Across many proceedings on social relationships, warmth has always been a prominent aspect since it appears to be associated with a response in terms of emotional and behavioural reaction (Fiske, Cuddy,
& Glick, 2007). Many scholars have studied how customers evaluate services and their providers, and these studies are very profound in various service literature (Brady & Cronin, 2001).
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The results of these studies demonstrate that two fundamental components of social cognition, warmth and competence, affect service outcomes and consumers' behaviour (Scott, Mende & Bolton, 2013.) For service providers, warmth constitutes social and moral attributes, and the consumers can perceive these attributes. Similarly, competence refers to the service provider's capabilities (Kervyn, Fiske, & Malone, 2012). A survey by Kirmani et al. (2017) discovered that over 88% of Yelp reviews rely on warmth and competence to evaluate service providers. Frontline employees can be judged by the consumers on two dimensions: warmth and competence in their work. The warmth dimension can be conveyed during service through gestures and facial expressions (Grandey et al., 2005; Wang et al., 2017).
2.2 Evaluation of Warmth and Competence
Research studies on how customers evaluate service providers have a long-standing history. How customers evaluate service providers is a recurring topic in research works, and Brady and Cronin (2001) are just one among them.
However, this research demonstrates that the two fundamental dimensions of consumers’ social cognition, warmth and competence, can ultimately improve the outcome of services (Scott, Mende, and Bolton 2013).
The two dimensions can be sub-categorised into the warmth dimension, which refers to a service provider’s helping instincts. In contrast, the competence dimension can be referred to as a service provider's capabilities (Kervyn, Fiske, and Malone 2012). Simply, competence has similarities to hard skills; warmth correlates with soft skills. Marinova and Singh (2018) talked about how individuals/customers judge front-line service employees such as doctors, nurses, typically first responders on the basis of competence (hard skills) and warmth (soft skills).
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These dimensions have caused a remarkable level of interest among academics as prominent scholars have broadly engaged themselves in the service sector as in Bolton and Matilla (2015) and Kirmaniet al. (2017). Contemporary fields such as chatbots and service robots, as in Van Doorn et al. (2007), are also studying these dimensions (competence and warmth).
However, a key question across many of these studies that have mainly been unresolved is which dimension amongst warmth and competence is more important for evaluating customer-service provider relationships across all service sectors. Most literature has repeatedly assigned a dominant role on competence as being a more critical metric towards an evaluation of customer – service provider relationship as in Aaker, Garbinsky, and Vohs (2012) and other notable studies carried out by Grandey et al. 2005; Kirmani et al. 2017; Marinova, Singh and Singh 2018). In other studies, such as in Andrei et al. (2017), there was mixed evidence resulting in an unresolved conclusion. In Infanger and Sczesny (2015); Kolbl, Arslanagic-Kalajdzic; and Diamantopoulos (2019), a dominant role on warmth was recorded.
The inconsistencies across various studies on what dimension is a more dominant metric for evaluating customer-service provider relationship has resulted in managers having little orientation as regards initiatives around warmth and competence. As a result, the managers - aiming to attract new customers or attempting to build stronger customer relationships - are more likely to make wrong decisions when they focus on the wrong dimension.
2.3 Social Perception of Warmth and Competence
Another relevant aspect of review has been a growing interest in understanding the dimension by which individuals judge others, especially from a first impression point of view. The need for individuals to make spontaneous and accurate assessments of others to successfully navigate today's increasingly complex social world cannot be overemphasized.
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Although there have been many dimensions for the several factors that inform such social judgements, research suggests they can all be divided into two key dimensions: warmth and competence (Fiske et al., 2007).
In Rosenberg, Nelson and Vivekananthan (1968) the study attempted to identify several underlying dimensions of personality by asking participants to describe different people they know by selecting personality traits from a list of over 50 other characteristics. This study then measured the degree to which these traits co-occurred according to their descriptions of a particular person. The study found that the characteristics which appeared more frequently can be selectively categorized into those described intellectual qualities that were either good or bad (i.e., competence—e.g., qualities like determined and industrious vs irresponsible and unintelligent) and also social qualities that were good or bad (i.e., warmth—e.g., qualities like sincere and good-natured vs irritable and humourless). The two dimensions explored in this study were independent and accounted for most of the variance in how people perceived or judged others.
In another similar study as in Wojciszke (1994), participants were asked to generate descriptions of events that have, over time, helped them form strong impressions of other people or even themselves.
Of over 500 reports generated by these participants, approximately three-fourths depicted considerations of warmth or competence, as rated by independent judges. In yet another study, a pool of 200 diverse traits was placed on various dimensions, including the degree to which they captured the warmth and captured competence (Wojciszke B, Dowhyluk M, Jaworski M, 1998). These ratings of a trait's warmth and competence predicted all but 3% of the variance in ratings of trait favourability, which led to the conclusion in this study that these two ingredients (warmth and competence) are essential keys to describing both positive and negative qualities in the perception of an individual.
These studies and dozens of others using various methodologies suggest that warmth and competence are two key dimensions holding the most significant explanatory power for positive and negative evaluations of others.
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Although these dimensions have sometimes been called by other names in earlier studies, regardless of the terminology, researchers have a remarkable consistency in the qualities commonly reflected by these two dimensions. There is a solid evolutionary argument for the primacy of warmth and competence in literature: the need to quickly determine if a person intends to, or is capable of, inflicting harm or helping an individual. In summary, warmth encapsulates answers to the question of
"Is this person's intentions toward me positive or negative?" and competence encapsulates answers to the question of "Does this person have the ability to enact those positive or negative intentions?"
(Fiske ST, Cuddy AJC, Glick P, 2007). To promote survival, a person must answer these critical questions whenever they encounter someone new.
2.4 The Differences Between Consumers’ Relevance of Warmth and Competence.
Warmth may be vital for high vs low individuals on anxiety in their brand relationship style. Indeed, in a hypothetical selling context, trust or warmth, respectively, were more critical for the "buyers" in conditions of higher perceived risk e.g., Van Swol (2003). In cases of product failure (Xu et al., 2013).
MacInnis (2012) showed the relevant principle that further demonstrated by showing that a specific subset of people responds with higher purchase intent to a warm rather than competent advertising campaign for the same high-involving product, in this case, a smart phone. The high-involving nature of the product should trigger higher purchase intent in the case of the competent campaign.
However, the heart of the product may be anxiety-provoking, and for them, the warm movement should result in higher purchase intent. Precisely, it is predicted that (a) the clever advert strategy will be less effective for individuals who have high smartphone anxiety levels than for those who are less anxious, and (b) individuals who have high smartphone anxiety levels will prefer the warm ad type strategy over the competent one. Such a pattern of findings would indicate that the "golden quadrant"
shifts according to personal differences in line with the relevance principle.
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A final demonstration of the relevance principle focuses on the unity of warmth and competence with the type of appeal (i.e., self- vs people-focused) employed in the same advertising campaigns. Whereas SCM proposes that assessment of others' warmth is evolutionarily more critical than assessing their competence (Fiske et al., 2007), as discussed earlier, this does not hold in a brand context. Fiske et al.
(2012, p. 206) suggested that "brands may have a more personal contact than many out-groups do, [and] so people might differ more systematically" in their perception of brands. This statement implies that the closeness between the brand and the consumer's self may determine the perceived relevance of warmth and competence to a brand. However, this is not unique to human-brand relationships.
According to Wojcicki's (2005) Double Interest Account (DIA), observers are interested in the actor's warmth in social situations. This is because observers, by their position, maybe the target of the actions of others. However, those actions have a more significant interest in competence because the movement needs to be carried out competently to benefit them.
Moreover, close others will be evaluated similarly to oneself. So, the typical primacy of warmth over competence reverses self and close friends (Wojcicki & Abele, 2008). Thus, the relevance principle implicitly underlies the DIA. By extension, we predict that when it comes to self- versus people-focused blood donation service appeals: (a) the competent ad strategy should perform better (than the warm one) in the self-focused appeal condition, and (b) the warm ad strategy should perform better in the people-focused appeal condition (as compared to the self-focused condition). Such a pattern of findings would demonstrate that the "golden quadrant" shifts as a function of appeal type in line with the relevance principle. According to Ybarra, Chan, and Park (2001), warmth and competence are also primary to other influences. Before judging the competence of others, consumers feel their warmth.
Second, Aaker, Vohs, and Mogilner (2010) emphasize that broad global concepts, such as warmth, are more likely to be cued than narrow and specific ones, such as competence. Thus, it is more likely that warmth is an appropriate, harmonious and vital signal of identity than is the case for competence.
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2.5 Prosocial Behaviour
Prosocial motivation is the desire to engage in activities that benefit other people, often conceptualized as a personality trait (Grant, 2008). Hoofman (1982) highlighted two key components required for the development of prosocial motivation: empathy (a positive affective state) and guilt (a negative affective state). Other scholarly studies in psychology prompt the importance of studying empathy and guilt to understand their influence on an individual's prosocial behaviour properly.
Considering a service provider's role of creating and nurturing relationships with consumers, the idea of empathy and guilt as two higher-order influencers of prosocial behaviour seems applicable within the context of a salesperson and consumer due to their interpersonal nature. Both empathy and guilt affect the relationship maintained between a service provider and a consumer, and they are considered personal traits integrated and expressed within social relationships (Jones and Kugler, 1993). Many scholars believe that service providers embody a critical component that affects their relationship with consumers (Dion et al., 1995). The capacity for guilt has been identified for its role in fostering interpersonal and exchange-based contexts (Clark et al., 1986). It may sometimes serve as a negative social disposition. It may possess "positive consequences" in the sense of helping to attain goals, induce relationships and enhance coping mechanisms, or allow people to better adapt to their changing or aversive environments" (Bagozzi, 2006).
Prosocial motivation theory suggests that prosocial behaviours are caused by intrinsic motivation. Still, it could be driven by "the capacity of humans to take the other's roles ", including the capacity for empathy and guilt (Hoffman, 1982). The theory of prosocial motivation becomes applicable to the context of a service provider and a consumer, where a service provider's job involves regular interactions with consumers. Many pieces of literature on sales have recognized consumer service behaviours as prosocial behaviours directed towards consumers (George and Bettenhausen, 1990). In our case model, we link two emotional traits critical to a service provider during the COVID-19 with the capacity to influence prosocial behaviours in consumers. Both of these metrics are behaviourally
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based and could be applied to the context of sales. Many scholars have stressed the need for a more behavioural-based metric in evaluating service providers. These metrics show the "how" behind achieving sales outcomes (Ahearne et al., 2007). These behaviours evaluate how service providers sustain their relationships with a consumer.
Research suggests that a service provider's capacity to manage a consumer at the point of service properly is essential to their respective firms and potential consumers in the long run. These behaviours, such as (warmth and competence) are considered to be managerially crucial as service providers often view them as drivers of sales productivity (MacKenzie et al., 1999). Also, cooperative behaviours such as volunteering and supporting have been linked to higher customer performance, as they often enhance in-role behaviours and perceptions of service quality by the consumer (Piercy et al., 2006; Yoon and Suh, 2003). Research also focuses on the impact of other service providers behaviours that are targeted toward consumers. These behaviours could be assessed by examining the service provider's success in satisfying the consumer and building relationships. Several recent studies have also considered consumer-related outcomes, such as satisfaction and service quality, to measure a service provider's performance (Hunter and Perreault, 2006; Rapp et al., 2006).
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3. CONCEPTUAL FRAMEWORK
In an attempt to investigate the effect of warmth and competence of a service provider on consumer prosocial behaviour during the COVID 19, The conceptual model will help us determine and understand the composition of concepts used in this study by simulating the subject of the study.
We considered two constructs in assessing influence on consumer prosocial behaviour:
i. The service provider’s warmth ii. The service provider’s competence
Fig. 1 The conceptual map of this research
According to Forgas et al (2008) the correlation between emotions and prosocial behaviour is complex as studies have primarily explored the relationship that both negative and positive emotions have with prosocial behaviour. According to Knoblich and Sebanz (2006), perception informs action as social perception informs social interaction. They both described social perception as falling along two primary dimensions which are warmth and competence, when these dimensions are perceived in light of their stereotyped social groups, perceived warmth elicits positive active behaviour such as caring and protecting (Cuddy, Fiske, and Glick., 2007), while perceived competence elicits behaviours such as cooperation (Cuddy et al., 2007). These behaviours can be said to be prosocial behaviour that are influenced by a service provider through his warmth and competence at the point of service.
The service provider’s competence
H2
Prosocial behaviour The service provider’s
warmth H1
COVID 19 Pandemic
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Building on the theoretical background of this study, the conceptual model takes context from our hypothesis. Thus, we hypothesize:
Hypothesis 1: The warmth of a service provider influences the consumer’s prosocial
behaviour
There has been an increasing debate along how warmth has been used as a fundamental dimension in the evaluation of service providers. Most of these debates have been on when and how warmth takes precedence for different outcomes in a customer-service provider relationship. The display of warmth by service providers establishes strong emotional bonds and drive both customer retention and outcome orientation. According to Lemmink and Mattson (2002), service provider’s behaviour has both a short term (i.e., likeability and perceived quality) and also the long term (i.e., trust and loyalty) effects on consumer perceptions.
In research carried out by Fiske et al. (1998), the researchers concluded that warmth carries more weight in affective and behavioural reactions. That is, the perception of warmth has a greater than average influence in eliciting certain positive behaviours across individuals. In this context, we can conclude that a service provider’s show of warmth at the point of service to a consumer has the potential to influence their prosocial behaviours particularly during periods of vulnerable state such as during the COVID19.
Hypothesis 2: The competence of a service provider influences the consumer’s prosocial behaviour.
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According to Kirmani and Campbell (2004), consumers are more likely to have stronger relational concerns if they have strong process-oriented goals. For these types of consumers, several intangible aspects of service delivery such as social functioning and valuable service personnel interaction are very important in determining their response and reaction to such services.
Customers with a relatively higher process orientation are more likely to pay more attention to the service provider’s attribute that portrays competence in the line of delivery, evaluating whether a provider offers a satisfactory process or not (DeRuyter and Wetzels, 1998). The indication of this is that customers are influenced by the way in which service provider’s carry out their jobs, and several indicators serve as an evaluative metric to determining how well a service is provided to a consumer.
During COVID 19, competence becomes a valuable element in providing service to a consumer who are in a more vulnerable state. The capability concerns during such periods shifts the focus to a service provider’s competence in their ability to deliver high impact or quality service. In Lovelock and Young (1979) they argued that in services directed at people, customers are the singular subject of the service and are inherently more actively integrated in the service process. Organ (1988) and Smith et al. (1983) arrived at a conclusion in their study that a service providers job satisfaction is a factor that enhances consumers behaviour.
These preceding paragraphs ultimately suggests the influential powers of the competence of a service provider and more so, during periods of vulnerable state of the consumer as having the capacity to initiate prosocial behaviours such as volunteering, donating and cooperating in consumers. We suggest that the diagnostic of a service provider’s competence influences consumers prosocial behaviour during the COVID 19.
The variables used in this study are factors responsible for influencing humans’ prosocial behaviour in general. To be more specific, this study attempts to pinpoint the one element (mediating variable) that will help to explain the relationship between influencing factors that affect consumer prosocial
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behaviour (independent variable) and the customer's decision to help or not (dependent variable).
Therefore, the survey plans to determine which is the correct mediating variable: the warmth or the competence of a service provider. The variables used in this study are listed as follows:
A. Independent Variable: Service Providers Warmth and Competence B. Dependent Variable: The Customer Displays of Prosocial Behaviour C. Mediating Variable: The COVID-19 Pandemic
D. Control Variable: Customer’s Satisfaction
Independent Variable: Attributes of Warmth and Competence
These variables are physical factors/elements of that can influence consumers’ prosocial behaviour which include elements of warmth such as friendliness etc. and elements of competence such as capabilities etc. Notwithstanding, since this research’s methodological approach is a survey.
Dependent Variable: The Customer Display of Prosocial Behaviour (effect)
The dependent variable is the case of the customer deciding to help or not. It is the effect caused by the independent variable. This variable depends on the rate of physical factors influencing prosocial behaviour. The higher the rate of physical factors exposed to the customer; the higher the chance of the customer rendering help.
Mediating Variable: The COVID 19 Pandemic.
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We expand our framework by adding the mediating variable. This mediator, the rate at which the pandemic, alters the effect that our independent variable has on the dependent variable. In our conceptual map, we expect that the higher the number of service provider’s attribute the customer is exposed to, the higher the chance of the customer engaging in prosocial activities. However, this is not totally the case. Adding the mediator variable, we recognise the fact that a customer’s decision to engage in prosocial activities also depends largely on his or her present emotions. There are three major emotional factors that influence prosocial behaviour: mood, empathy and internal attributions all emotional states that can be triggered by the current pandemic.
Control Variable: Physical/Mental Ability of the Customer
Additionally, to test the cause-and-effect relationship, we need account for other variables that we are not interested in measuring but might have an impact on whether a customer would engage in prosocial activities or not. The control variables include the satisfaction of the consumer with the delivery of service by the service provider. Is the customer satisfied with the service rendered? Is the customer happy with their interactions with their service provider? The answers to these questions are factors that are constant in our study. This means this research surveys only focuses on
participants who derived satisfaction from the service provider’s service delivery.
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4. METHODOLOGY
This study used a survey as the primary method to gather data. Particularly, 294 people from different countries answered the survey. This globality of the data was made possible because the survey was created online with the aid of Qualtrics Survey Software. Prolific, an online platform that connects researchers with participants, was used to select samples and share the survey.
It is in this same vein that IBM’ Statistical Package of the Social Sciences (SPSS) was used to analyse the data fetched from the survey. Because this research’s data is relatively small, IBM SPSS is the ideal application to use because of its academic ecosystem and its easy-to-use functionality.
Utilising a survey research strategy, this study investigated the hypotheses and, subsequently, answered the research questions posed in the literature review. In the analysis, we itemised the independent and dependent variables and then assessed the relationships between them.
Therefore, the strategy of this study follows the principles of statistics, where the causal-effect relationship of a phenomenon is investigated using inferential statistical methods.
This research follow a cross-sectional time horizon. This is because the data gathered during this study are within the time frame of the COVID-19 period: late 2019 to early 2021. Since this research wants to investigate the possibility of a consumer displaying prosocial behaviour during the pandemic period only, the relevant data that should be gathered are within the COVID-19 era.
This research used a survey to collect data for this research. The survey was distributed using a non- probability sampling. This means that the survey was deployed to random recipients who answered the questions. These recipients are from different nationalities, with most of them of Lisbon, Portugal.
Profilic, an online platform, was used to select the sample that answered the survey. Profilic is an online platform that helps recruit trustworthy participants for a research. To select samples in Profilic, a researcher would have to create requirements that the respondents must fulfil. The two major requirements from the respondents that shape this research’s sample is location and language. Once
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a person lives in Portugal (although other countries are also acceptable) and can speak English fluently, he or she is eligible to answer the survey.
Specifically, the variables in the survey were regressed on one another using the statistical method called multi-regression coefficient. Using this statistical model, we were able to measure the degree of influence the warmth or competence of a service provider has on a consumer’s prosocial behaviour.
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5. EMPIRICAL STUDY
Having elaborated on the methodology this study underwent to gather data, this chapter’s sole purpose is to analyse the primary data gotten from the survey. At the end of this chapter, the research questions (hypothesis) are answered.
The subsequent tables provide statistical descriptions of the selected sample based on gender, age, educational qualification, employment category and monthly income.
Gender
Gender Percentage Count
Male 63.83% 180
Female 32.98% 93
Non-binary 0.71% 2
Prefer not to say 2.48% 7
282 Table 1. Sample Data: Gender
Age
Age Percentage Choice Count
Under 18 1.42% 4
25 - 34 26.60% 75
35 - 44 15.25% 43
45 - 54 7.80% 22
55 - 64 2.48% 7
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65 - 74 0.71% 2
75 - 84 0.71% 2
85 or older 1.42% 4
282 Table 2. Sample Data: Age
Education
Education Percentage Choice Count
Less than high school 2.13% 6
High school graduate 13.48% 38
Some college 19.15% 54
2 year degree 7.80% 22
4 year degree 32.27% 91
Professional degree 21.28% 60
Doctorate 3.90% 11
282 Table 3. Sample Data: Education
Employment Category
Employment Category Percentage Choice Count
Employed full time 48.41% 137
Employed part time 14.13% 40
Unemployed looking for work 6.36% 18
Unemployed not looking for work 2.12% 6
22 Table 4. Sample Data: Employment Category
Monthly Income
Table 5. Sample Data: Monthly Income
Retired 2.12% 6
Student 26.50% 75
Disabled 0.35% 1
283
Monthly Income Percentage Choice Count
Less than $10,000 66.79% 187
$10,000 - $19,999 14.29% 40
$20,000 - $29,999 7.86% 22
$30,000 - $39,999 3.21% 9
$40,000 - $49,999 2.50% 7
$50,000 - $59,999 1.79% 5
$60,000 - $69,999 0.36% 1
$70,000 - $79,999 0.36% 1
$80,000 - $89,999 1.07% 3
$90,000 - $99,999 0.36% 1
$100,000 - $149,999 0.71% 2
More than $150,000 0.71% 2
280
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Below is a table showing the survey questions which were answered by 294 people, with existing outliers. There are twenty-seven questions in the survey.
Survey Questions
Question Number
Variable Name
Question
Q2 gender Gender
Q3 age Age
Q4 edu Educational Qualification
Q5 employ Employment Category
Q6 net Monthly Income
Q7 service Do you currently provide a service?
Q8 experience How many times did you experience a service from your service provider during the COVID 19?
Q9 change Have you experienced any change in service delivery with your provider during the COVID 19?
Q10 means What was the primary means of service delivery?
Q11 place Municipality
Q12 satisfaction How satisfied are you with service provider’s during COVID 19?
Q13 importance How important is warmth and competence to you during service delivery?
Q14 happy How happy was your service provider during the COVID 19?
Q15 helpful How helpful was your service provider during the COVID 19?
Q16 sincere How sincere was your service provider during the COVID 19?
Q17 skilful How skilful was your service provider during the COVID 19?
Q18 knowledge How knowledgeable would you rank your service provider during the COVID 19?
Q19 capable How capable would you rank your service provider during the COVID 19?
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Q20 attention Did the delivery of service by your service provider draw your attention to the current COVID 19 crisis?
Q21 corporate After your encounter with your service provider are you more likely to corporate with the COVID 19 guidelines?
Q22 donate After your encounter with your service provider are you more likely to donate towards the COVID 19 relief?
Q23 volunteer After your encounter with your service provider are you more likely to volunteer during the COVID 19?
Q24 friendly Would you consider the service provider’s friendliness to be the most influential attribute for your decision?
Q25 efficiency Would you consider the service provider’s efficiency to be the most influential attribute for your decision?
Q26 capability Would you consider the service provider’s capabilities to be the most influential attribute for your decision?
Q27 warmth Would you consider the service provider’s warmth to be the most influential attribute for your decision?
Q28 patronise Would you continue to patronize the services of your service provider (yes/no)?
Table 6. The survey questions
Independent Variable – 1 Independent Variable - 2 Dependent Variable The service provider’s
warmth
The service provider’s competence
The consumer’s prosocial behaviour
Q14 – happy Q17 – skilful Q-20 attention
Q15 – helpful Q18 – knowledge Q-21 corporate
Q16 – sincere Q19 – capable Q-22 donate
Q24 – friendly Q25 - efficiency Q-23 volunteer
Q27 – warmth Q26 – capability
Table 7. The dependent variable and independent variables.
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Each survey question in Table 6. can be grouped into this research’s independent variables (IVs), dependent variable (DV) as shown in Table 7. Even though the controlling and extraneous variables can affect the dependent variable, they will not be considered in this chapter because they do not address any of the research questions. Hence, only the IVs and DV will be used to test the hypotheses in the subsequent sections.
This study aims to decipher whether the warmth or competence of a service provider influences the prosocial behaviour of a consumer. Thus, what is to be measured is the effect of X (independent variables) on Y (dependent variable). In this study’s case, what is to be discovered is what happens to Y when X increases or decreases. In other words, what happens to the consumer’s prosocial behaviour when the customer’s display of competence or warmth increases or decreases? Does it increase? Does it decrease? Does it stay the same?
The regression coefficient will be used in the next sections to analyse the IVs and DV and consequently answer these research questions. Specifically, the multi-linear regression and the bivariate regression will used to analyse the variables. Since, our model has more than two predictor variables and one outcome variable, analysing the data using multi-linear regression is the best practice.
5.1 Attention
H1: The warmth of a service provider influences a consumer’s prosocial behaviour [to draw his/her attention to the current COVID-
19 crisis].
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Fig 2. A service provider’s warmth being regressed on a prosocial behaviour, attention
Figure 2. shows how the happiness, helpfulness, sincerity and warmth of a service provider predicts the consumer’s propensity to notice the vulnerability of people affected by the current pandemic.
Bivariate Regression
Hypothesis Regression Weights
Beta Coefficients
R2 F T-Value P-Value Hypothesis
Supported
H1 Q14 – Q20 -.080 .016 4.547 -2.132 .034b Yes
H1 Q15 – Q20 -101 .021 6.000 2.449 .015b Yes
H1 Q16 – Q20 -.009 .000 0.44 -.209 .835b No
H1 Q24 – Q20 -.054 .008 2.213 -1.488 .138b No
H1 Q27 – Q20 -.125 .044 12.325 -3.511 .001b Yes
Table 8. Bivariate regression of each IV for warmth on the DV, attention
Happy – Q14
Helpful – Q15
Sincere – Q16
Friendly – Q2 4
Attention – Q2 0
Warmth – Q27
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Table 8. depicts the degree of influence and significance of one IV for warmth on the DV, attention, using bivariate regression. By doing so, we can provide specificity on the significance and variance of change of each IV on the DV without influencing the other IVs. Nonetheless, we want to also analyse the variables as a single model; hence, we would also utilize multi-regression coefficient. Table 9. and Table 10. below provides data on the IVs (happy, helpful, sincere, friendly and warmth) of competence regressed on the DV (attention) as a single model.
Multi-Regression Coefficients
Model Unstandardised
Beta
Coefficients Std.
Error
Standardized Coefficient Beta
T-value Sig
(Constant) 2.401 .289 8.302 .000
How happy was your service provider during the COVID 19?
-.071 .041 -.109 -1.715 .087
How helpful was your service provider during the COVID 19?
.118 .046 .168 2.575 .011
How sincere was your service provider during the COVID 19?
-.062 .048 -.083 -1.288 .199
Would you consider the service provider’s friendliness to be the most influential attribute for your decision?
.040 .043 .068 .943 .346
Would you consider the service provider’s warmth to be the most influential attribute for your decision?
-.118 .043 -.198 -2.774 .006
Table 9. A coefficients table: Multi-linear regression of warmth IVs on the DV, attention, to answer H1.
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ANOVA
Model Sum of Squares Df Mean Square F Sig.
Regression 13.292 5 2.658 4.436 .001
Residual 158.208 264 .599
Total 171.500 269
Table 10. An ANOVAtable: Multi-linear regression of the IVs of warmth on the DV, attention, to test H1.
Using multi-linear regression, the dependent variable, attention (Q20), was regressed on the predicting variables, happy, helpful, sincere, friendly, and warmth, to test the hypothesis, H1. The predicting variables significantly predicted Q20 as F (5, 269) = 4.436 P = .001. This indicates that the predictors can play a significant role in influencing Q20(2.401, P = .001). Therefore, a service provider’s display of happiness, helpfulness, sincerity, friendliness and warmth can draw the attention of a consumer towards the COVID-19 crisis. Moreover, the adjusted R2 (selected because we have many IVs) is .060 as shown in the model summary below. This means the warmth model explains 6% change on the variance of the consumer’s prosocial behaviour to draw his attention towards the COVID-19 victims.
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .278a .078 .060 .774
Table 11. A summary of the warmth model regressed on the DV, attention.
H2: The competence of a service provider influences a consumer’s prosocial behaviour [to draw his/her attention to the current COVID-
19 crisis].
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Fig 3. A service provider’s competence model being regressed on the prosocial behaviour - attention
Figure 3. illustrates how the skill, knowledge, sincerity, (observed) capability and (displayed) capability of a service provider predicts a consumer’s likelihood to take cognisance of victims affected by the recent pandemic.
Bivariate Regression
Hypothesis Regression Weights
Beta Coefficients
R2 F T-Value P-Value Hypothesis
Supported
H2 Q17 – Q22 -.094 .019 5.252 -2.292 .023 Yes
H2 Q18 – Q22 -.120 .017 4.930 -2.220 .027 Yes
H2 Q19 – Q22 -.062 .010 2.738 -1.655 .099 Yes
H2 Q25 – Q22 .001 .000 .001 .036 .971 No
H2 Q26 – Q22 .023 .001 .399 .632 .528 No
Table 12. Bivariate regression of each IV of competence on the DV – attention
Skilful –
Q1 7
Knowledge – Q1 8
Capable o – Q 19
Efficiency – Q2 5
Attention – Q 22
Capable d- Q2 6
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As implemented in the first section, Table 12. depicts the degree of influence and significance of one IV of competence on the DV, attention, by means of bivariate regression. In this table, we report the significance and variance of change in each IV on the DV without considering the influence of other IVs. Nonetheless, analysing the variables as a single model requires the use of multi-regression coefficient. Table 13. and Table 14. below provides data on the IVs (skill, knowledge, (observed) capability, efficiency and (displayed) capability) of competence regressed on the DV (attention) as a single model.
Multi-Regression Coefficients
Model Unstandardised
Beta
Coefficients Std.
Error
Standardized Coefficient Beta
T-value Sig
(Constant) 2.181 .280 7.798 .000
How skilful was your service provider during the COVID 19?
-.082 .052 -.118 -1.586 .114
How knowledgeable would you rank your service provider during the COVID 19?
-.077 .065 -.085 -1.192 .234
How capable would you rank your service provider during the COVID 19?
-.012 .049 -.019 -.253 .800
Would you consider the service provider’s efficiency to be the most influential attribute for your decision?
.001 .043 .002 .028 .978
Would you consider the service provider’s capabilities to be the most influential attribute for your decision?
.064 .045 .110 1.418 .157
Table 13. A coefficients table: multi-linear regression of competence IVs on the DV, attention, to answer H2.
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ANOVA
Model Sum of Squares Df Mean Square F Sig.
Regression 5.538 5 1.108 1.771 .119
Residual 163.906 262 .626
Total 169.444 267
Table 14. An ANOVAtable: multi-linear regression of the IVs of competence on the DV, attention, to answer H1.
Using multi-linear regression, the dependent variable, attention (Q22), was regressed on the predicting variables (skill, knowledge, (observed) capability, efficiency and (displayed) capability) to test H2. The predicting variables insignificantly predicted Q22 as F (5, 267) = 1.771, P > .005. This indicates that the predictors play a less significant role in influencing Q22(2.181, P < .001). Therefore, a service provider’s display of skill, knowledge, capability, and efficiency have an infinitesimal effect on a consumer’s prosocial behaviour to be interested in victims of COVID 19. Likewise, the adjusted R2 is .014 as displayed in the model summary below. Accordingly, the competence model can explain only 1.4% of change in consumer prosocial behaviour towards the pandemic victims.
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .181a .033 .014 .791
Table 15. A summary of the warmth model regressed on the DV, attention.
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5.2 Donation
H1: The warmth of a service provider influences a consumer’s prosocial behaviour [to donate towards COVID 19].
Fig 4. A service provider’s warmth being regressed on a prosocial behaviour, donation
Figure 4. shows how the happiness, helpfulness, sincerity and warmth of a service provider predicts the consumer’s propensity to donate towards the COVID-19 relief.
Bivariate Regression
Hypothesis Regression Weights
Beta Coefficients
R2 F T-Value P-Value Hypothesis
Supported
H1 Q14 – Q21 .239 .072 21.900 4.680 .000b Yes
H1 Q15 – Q21 -.189 .037 10.895 -3.301 .001 Yes
H1 Q16 – Q21 -.067 .004 1.203 -1.097 .274 No
Happy – Q14
Helpful – Q15
Sincere – Q16
Friendly – Q2 4
Donation – Q2 2
Warmth – Q27
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H1 Q24 – Q21 .248 .090 26.751 5.172 .000 Yes
H1 Q27 – Q21 .262 .100 29.996 5.477 .000 Yes
Table 16. Bivariate regression of each warmth IV on the DV - donate
Using bivariate regression, Table 16 shows the influence and significance of one IV on the DV. In this way, the significance and variance of change of each IV on the DV can be determined independently of each other. However, since we also want to analyse the variables as a single model, we will also use multi-regression coefficients. Below are Tables 17 and 18 displaying the IVs regressed on the DV as one model.
Multi-Linear Coefficients
Model Unstandardised
Beta
Coefficients Std.
Error
Standardized Coefficient Beta
T-value Sig
(Constant) 1.236 .378 3.268 .001
How happy was your service provider during the COVID 19?
.189 .054 .209 3.498 .001
How helpful was your service provider during the COVID 19?
-.154 .060 -.157 -2.563 .011
How sincere was your service provider during the COVID 19?
.021 .063 .020 .332 .740
Would you consider the service provider’s friendliness to be the most influential attribute for your decision?
.101 .056 .122 1.805 .072
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Would you consider the service provider’s warmth to be the most influential attribute for your decision?
.140 .056 .168 2.508 .013
Table 17. A coefficients table: Multi-linear regression of the IVs on the DV, donate, to answer H1.
ANOVA
Model Sum of Squares Df Mean Square F Sig.
Regression 60.898 5 12.180 11.873 .000
Residual 272.867 266 1.026
Total 333.765 271
Table 18. An ANOVAtable: Multi-linear regression of the IVs of warmth on the DV, donate, to answer H1.
.
Using multi-linear regression, the dependent variable, donate (Q21), was regressed on the predicting variables, happy, helpful, sincere, friendly, and warmth, to test the hypothesis, H1. The predicting variables significantly predicted Q21 as F (5, 271) = 11.873, P < .001. This indicates that the predictors can play a significant role in influencing Q21(1.236, P <= .001). Therefore, a service provider’s display of happiness, helpfulness, sincerity, friendliness and warmth can trigger a consumer to donate to COVID-relief. Moreover, the adjusted R2 (selected because we have many IVs) is .167 as shown in the model summary below. This means that the warmth model explains 16.7% change on the variance of the consumer’s prosocial behaviour.
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .427a .182 .167 1.013
Table 19. A summary of the warmth model regressed on the DV, donation.
