Goal Setting, Personality Traits, and the Role of Insurers and Other Service Providers for Swiss Millennials and Generation Z

Abstract

Service providers are developing more sophisticated offerings, and it is important for them to understand the demographics and specific context by which individuals might procure their services. This allows companies to stay relevant to their customers. The target of this paper is to investigate the types of goals Millennials and Generation Z individuals are pursuing and what role different service providers may play in supporting these endeavors, with the aim of providing actionable insights for insurers. Furthermore, it is to investigate how personality traits may relate to differences in individuals’ preferences. The study is based on a survey of 854 Swiss university students. The results indicate that goals are concentrated in a few categories, and educational institutions and healthcare providers are well-positioned to support goal achievement. Insurers, on the other hand, rank low among the preferences, and their profile is largely undifferentiated. This result indicates that insurers need to further focus their efforts to gain relevance among younger customers. Supporting goals relating to self-fulfillment and ability for high-conscientious and/or low-honest/humble customers by focusing on risk education and risk management seems a particularly interesting strategy for insurers.

1. Introduction

Goals and motivations are a fundamental aspect of human behavior. They provide the driving force by which individuals structure their behavior to meet the requirements of their everyday life (Austin and Vancouver 1996). Service providers are likely to better serve their customers by having a deeper understanding of the motivations that drive individuals to seek their services, allowing them to tailor their products to better serve each costumer’s specific needs. Despite this, there is a lack of empirical studies investigating how individuals look at different service providers as a way to achieve success in the particular goals they are pursuing.

This study aims at exploring the goals that drive human behavior in a cross-sectional cohort of individuals spanning two generations: the Millennials (Gen Y) and Generation Z (Gen Z). It investigates how a goal’s intrinsic value and an individual’s personality might be linked to how different service providers are suited to provide support in attaining individuals’ goals across these two generations. Given the authors’ background, the study is particularly focused on extracting insights for insurance companies.

1.1. Insurance and Services

Insurance companies have been evolving their business model and market presence to accommodate or take advantage of changes in technology and customer expectations. A survey of the existing literature identifies three areas where technology will impact insurance: (a) new technologies change the way insurers and customers interact; (b) new technologies can be used to automize, standardize, and improve the effectiveness and efficiency of business processes; and (c) new technologies create opportunities to modify existing products and develop new ones (Ehling and Lehmann 2018). Shifting to improved service delivery and leveraging available customer data has been shown to have a profound impact in several industries and to reinforce each other (Opresnik and Taisch 2015). Service transformation requires fundamental changes to a firm’s structure, culture, and competencies and in the way of delivering value and dealing with customers and stakeholders (Adrodegari and Saccani 2017). This innovation creates value by enabling cooperation between a firm and its customers along four dimensions: cooperative value innovation, customer-centric value innovation, cooperative productivity improvements, and company-centric productivity improvements (Zolnowski et al. 2016). Swiss insurance customers see value in additional services and are generally open to receiving services from insurers and are willing to share with them the data necessary to provide these services (Pugnetti and Elmer 2020). However, they are also open to receiving services from other providers, indicating that there is the potential for significant changes in the insurance industry if other providers can enter this space and build customer relationships (Pugnetti and Seitz 2021). Swiss insurers do not show great affinity for, or understanding of, newer customer segments. The onboarding process for new customers, for example, shows room for significant improvements (Pugnetti and Bekaert 2018). Understanding the goals of younger customers and developing services to support these goals, therefore, is of great importance to increase the relevance of insurance in the coming years.

1.2. Generational Differences

Generations are shaped by experiences and events occurring in their formative years. Millennials (born between 1981 and 1996) and Generation Z (born between 1997 and 2012) show differences in behaviors, attitude, and lifestyles, although it is not yet clear how lasting these differences will be (Dimock 2019). Gen Z are characterized by mobility, seeking dialogue, and being realistic, whereas Millennials grew up in an era of economic stability and are more oriented towards themselves. By contrast, the previous generation, Gen X, is more materialistic and competitive (Francis and Hoefel 2018). On the other hand, Millennials and Gen Z share similar views on a wide range of sociological and political values such as climate change, race, diversity, gender, and family (Parker et al. 2019). They also share similar concerns about the security implications of new technologies and their potential negative impact on interpersonal relationships and employment (Priporas et al. 2017). Less is known about the personal goals and motivations of Millennials and Gen Z, how they differ between generations, and how service providers may assist in reaching these goals. This understanding is likely to benefit both companies and customers by allowing service providers to tailor and improve their services and offers.

1.3. Taxonomy of Human Goals

Human goals have been characterized as having a hierarchical structure (Talevich et al. 2017; Carver and Scheier 2001). Higher order goals represent abstract concepts that guide behavior towards a desired end state (Talevich et al. 2017) but do not necessarily have a hedonic value or a driving force. On the other hand, lower-order goals, often referred to as “motives” (Talevich et al. 2017) or “personal strivings” (Emmons 1986), are more concreate, can more easily be defined by a set or sequence of behaviors, and tend to have forcelike or energizing properties (Lewin 2013; Revelle 2008), i.e., they represent what people are “characteristically aiming to accomplish”. This driving force or “value” of low-order goals, henceforth referred to as motives, can be quantified in an individual by attempting to measure the limited resources that the individual allocates to the pursuit of that motive (Kruglanski et al. 2002), for example, by measuring its importance, confidence in success, difficulty, etc. (Emmons 1986).

Several taxonomies of human motives have been previously proposed based on theoretical (Kenrick et al. 2010; Fiske et al. 2009) or empirical (Talevich et al. 2017; Chulef et al. 2001; Schwartz et al. 2012) approaches. This study focuses on understanding how the sentiment of support across service providers depends on the specific motives for the goals individuals are trying to achieve. This requires that (a) each goal can be classified to a low-order goal that correctly matches what the individual is referring to and (b) the taxonomy covers a wide range of possible goals at different levels of abstraction. To this end, the empirically determined taxonomy from Talevich et al. (2017) offers a good framework. Their taxonomy clusters human goals across 6 levels, from the 3 high-order (Z-level) abstract goals (meaning, communion, and agency) to the lowest order of 161 total motives that concretely describe individuals’ current strivings across a wide range of possibilities. The focus for this paper is on an intermediate level of abstraction (X-level), which describes a total of 14 goals, as shown in

Table 1. Goal types.

	Goal Clusters	Examples (V Cluster)
X1	Morals and values	Social values and personal morals
X2	Virtues	Social giving, interpersonal care, respected, and inspiring
X3	Religion and spirituality	Religion and spirituality
X4	Self-fulfill	Wisdom and serenity, self-knowledge, and happiness
X5	Openness to experience	Appreciating beauty, exploration, pursue ideals and passions, and enjoy life
X6	Self-protect	Avoid stress and anxiety, avoid harm, avoid rejections, and avoid conflict
X7	Avoid hassle	Avoid socializing and avoid effort
X8	Security and belonging	Interpersonally effective, social life and friendship, liked, sexual intimacy, emotional intimacy, fastidious, and stability and safety
X9	Power	Better than others, control of others, and leadership
X10	Health	Health
X11	Family	Good family life and close to parents’ family
X12	Ambition and ability	Mastery and perseverance, avoid failure, confidence and autonomy, self-regulated, smart and rational, and organized and efficient
X13	Intellectual competence	Analysis and technical know-how and intellectual growth
X14	Financial and occupational success	Money and wealth, financial freedom, and occupational success

. To provide better context, the table also lists examples at the V cluster level (Talevich et al. 2017).

1.4. Personality Traits

Personality traits have been shown to play a role in both the types of goals people tend to pursue, as well the values they attribute to them (measured through importance ratings) (Reisz et al. 2013; Roberts and Robins 2000; Bleidorn et al. 2010). However, to which extent personality plays a role in perceived support sentiment from service providers for the success of different goals has been less explored.

In this study, six factors of personality were measured. These include the five factors as defined by the Big-5 model of personality (openness/intellect, conscientiousness, neuroticism, extraversion, and agreeableness) (John et al. 2008; DeYoung 2015), as well as the honesty–humility factor as defined by the HEXACO model of personality (Lee and Ashton 2013). The framework of the Cybernetic model of personality was utilized for the definition of the Big-5 factors, defining personality traits in the context of goal-direct, adaptive systems (DeYoung 2015; Ones et al. 2015), as summarized in

Table 2. Personality traits.

Personality Traits	Definition (Cybernetic Function)
Openness/Intellect	Cognitive exploration and engagement with information.
Conscientiousness	Protection of nonimmediate or abstract goals and strategies from disruption.
Extraversion	Behavioral exploration and engagement with specific rewards (i.e., goals to approach).
Agreeableness	Altruism and cooperation; coordination of goals, interpretations, and strategies with those of others.
Neuroticism	Defensive responses to uncertainty, threat, and punishment.
Honesty–Humility	Assist in coordinating goal pursuit when deceit/guile is involved.

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1.5. Research Focus

The expansion into services to complement and complete risk transfer products is thus likely to be a driving force in insurance in the future, and supporting customer goals provides a promising foundation for this interaction between companies and customers. Generational differences have been shown to be significant and constitute an ongoing challenge for the traditional insurance business model. Personality plays a role in goal setting and could play a role in service provider preferences. The study therefore investigates the following research questions:

R1:

What role can service providers, especially insurers, play in supporting the pursuit of individual goals?

R2:

What are the differences by age cohort (Gen Y vs. Gen Z) and gender?

R3:

How does personality impact customer preferences for support on goal attainment?

R4:

What is the “sweet spot” for insurance companies to play a unique and sustainable role in supporting customers’ goals?

Especially the last question is of high interest to insurance practitioners, as a viable strategy needs to be built on a relatively stable value proposition and value capture and address a sufficiently large market segment. Because the goal types investigated are broad, different types of service providers can potentially be in competition with each other in order to establish themselves as preferred providers. Therefore, customer preferences across a wide range of industries are investigated in order to generate high-level insights.

2. Materials and Methods

2.1. Participants

Participants consisted of undergraduate and graduate students at the School of Management and Law of the Zurich University of Applied Sciences (ZHAW), surveyed between 11 May and 2 June 2021. All students were contacted through their university email to participate in an online survey to study the role of goals and motivations. Of them, 1881 started completing the survey and 854 (46%) fully completed it, and their responses were used in the analysis. The average age was 26 years (Std Dev = 4.85, range = 19–53). A total of 39% identified themselves as male, 59% as female, and 2% as other gender. Generation X (Gen X), Millennials (Gen Y), and Generation Z (Gen Z) individuals were defined as those born between 1965–1980, 1981–1996, and 1997–2012, respectively (DeYoung 2015). Students were incentivized to participate in the study by entering a contest to win an iPad (estimated value CHF 600), given to one randomly selected participant after the survey was completed.

2.2. Measures

The survey was constructed using the Typeform platform and all items were of mandatory completion. Two versions of the survey were constructed, one in the English language and the other in German. Participants were allowed to choose which version they would like to fill. In total, 122 (14%) participants responded to the English version of the survey and 732 (86%) responded to the German version. The analysis of the results was conducted in Python using the statsmodels and scikit-learn packages (linear regression) and in MS Excel (descriptive statistics and ANOVA). The survey comprised four main parts: In the first part, participants were asked to write in free text two main personal everyday goals they are currently trying to achieve. The instruction was as follows: “The following questions ask you to consider your personal everyday goals. These are the goals that you are currently trying to achieve. These can be positively framed (e.g., “I typically try to get in better shape”) or negatively framed (e.g., “I typically try to eat less processed food”). Consider your everyday goals in terms of repeating, reoccurring goals that you are trying to achieve, regardless of whether you’re being successful or not”. Participants were then asked to rate a set of questions related to each of the goals they filled in, namely: “How important is this goal to you in your life?”, “How confident do you feel about achieving this goal?”, “How difficult/time consuming is it for you to succeed in this goal?”, “How emotionally challenging is it for you to achieve this goal?” and “In the recent past, how successful have you been in this goal?”. The answers to each question followed a 5-item Likert scale.

In the second part of the survey, participants were asked to rate how much they thought specific types of companies and service providers (Automotive, Banking, Education, Utilities and Telecommunication, Entertainment, Healthcare, Technology, Insurance, Retail, and Local services) could help in supporting their goals. The answers to each question similarly followed a 5-item Likert scale. Additionally, they were asked to rate different types of communication with a potential service provider (Contact in person, Chat, Phone, Videocall, E-mail, Mobile app, and Online portal) in order to achieve their goals. Of note, to reduce the length of the final survey and attempt to increase total participation, these questions were asked in reference to both goals the participants mentioned rather than any of them specifically.

In the third part, participants were asked to rate a set of 20 items in a 5-item Likert scale related to their personality according to the Big-5 model of personality (John et al. 2008; DeYoung 2015), as well as 4 items purposed to measure the honesty–humility factors as defined by the HEXACO model of personality (Lee and Ashton 2013). To score each participant on each factor, the answers to each of the 24 question items were scored 1 (low) to 5 (high) and averaged across each factor. The internal consistency for the six personality scores, as measured by Cronbach’s alpha, were: 0.53 for openness/intellect, 0.48 for conscientiousness, 0.62 for extraversion, 0.47 for agreeableness, 0.34 for neuroticism, and 0.47 for honesty–humility.

In the fourth part, participants were asked to rate on a 5-item Likers scale, how good was their last interaction with insurance, and how strong was their trust in insurance companies.

2.3. Goal Classification

Each goal written in free text was classified using the taxonomy described in Talevich et al. (2017), which clusters human motives into 6 hierarchically organized levels (Z, Y, X, W, V, and motive), ranging from the most abstract level Z (meaning, communion, and agency) to the most concrete motive level (161 total motives).

Two independent reviewers with high German language proficiency were tasked to classify each of the 1708 goals by first matching each goal to 1 of 14 possible clusters on the X level, and then matching with the most appropriate motive that descends from the identified X cluster. The content analysis reliabilities measured by Cohen’s kappa between the two independent assessors were 0.52 and 0.36 for X and motive-level classification, respectively. Due to these low reliability scores, a third independent reviewer was tasked to resolve goal classification disagreements by choosing one of the two classifications from the previous reviewers.

3. Results

The responses to the survey provide insights into the types of goals typically being pursued by Millennials and Gen Zers, how reaching these goals can be supported by different providers, the preferred channels for this interaction, and how different personality traits influence both the selection of the goals being pursued and individuals’ interest in being supported by different service providers.

3.1. Goal Setting

The percentages of responders for each goal are shown in Figure 1. The majority of goals being pursued are concentrated in a few categories, with health (32.1%) and ambition and ability (28.8%) accounting for more than half of the goals.

Together with self-fulfill (15.7%), they account for three quarters of the declared goals, with only another four goals, virtues (5%), intellectual competence (4.6%), openness to experience (4.4%), and security and belonging (3.1%) being mentioned more than three percent of the time.

The concentration of goals in the health and ambition and ability categories may be at least partly explained by the fact that most respondents were university students participating in undergraduate and graduate degree programs, and by the impact of the COVID-19 pandemic. Switzerland, like all other European countries, faced a wave of contagion in the winter of 2020–2021 with visible countermeasures limiting mobility and contacts among people. Indeed, 6% of responders mentioned “COVID” or “Coronavirus” as some of the obstacles preventing them from achieving their goals (these results are not part of this study). In a previous study on goals of university students, health-related goals only accounted for some 12% of the goals mentioned, while organization-related goals (comparable to our ambition and ability category), accounted for approximately 6% (Reisz et al. 2013). The results show no differences in the frequencies of goals individuals mention between Millennials and Gen Zers (X²(13, N = 1668) = 20.28, p = 0.08).

3.2. Goal Value

Goal value was determined by the ratings respondents gave their goals related to their Importance, confidence in achieving, previous success, difficult/time consuming, and emotionally draining to achieve. The rating details for each goal are tabulated in

Table 3. Goal mean ratings (std dev) by cluster (Scale 1–5; 5 more positive outcome).

	Goal Cluster *	N	Importance in Your Life	Confidence in Achieving	Previous Success	Difficult/Time Consuming	Emotionally Draining
	Mean (std dev)	1708	4.14 (0.80)	3.50 (0.86)	3.48 (0.95)	2.85 (0.96)	3.07 (1.06)
	p-value		<0.001	<0.001	<0.001	<0.001	<0.001
X1	Morals and values	34	4.44 (0.79)	3.88 (0.69)	3.76 (0.61)	2.97 (0.94)	3.03 (1.19)
X2	Virtues	87	4.25 (0.74)	3.82 (0.67)	3.80 (0.74)	3.55 (0.84)	3.18 (0.95)
X3	Religion and spirituality	5	-	-	-	-	-
X4	Self-fulfill	262	4.47 (0.67)	3.60 (0.79)	3.42 (0.96)	2.92 (0.92)	2.90 (1.03)
X5	Openness to experience	74	4.36 (0.68)	3.61 (0.75)	3.61 (0.85)	2.80 (0.85)	3.16 (0.90)
X6	Self-protect	29	4.10 (0.83)	3.28 (0.97)	3.48 (0.99)	2.66 (1.02)	2.66 (1.06)
X7	Avoid hassle	1	-	-	-	-	-
X8	Security and belonging	53	4.32 (0.84)	3.66 (1.00)	3.43 (0.92)	3.10 (0.96)	3.19 (1.15)
X9	Power	2	-	-	-	-	-
X10	Health	548	3.98 (0.84)	3.48 (0.88)	3.49 (0.98)	2.98 (0.94)	3.40 (1.05)
X11	Family	10	5.00 (0.00)	4.10 (0.71)	3.90 (0.67)	3.40 (1.09)	3.20 (1.05)
X12	Ambition and ability	491	4.01 (0.79)	3.32 (0.86)	3.39 (0.95)	2.60 (0.95)	2.81 (1.00)
X13	Intellectual competence	80	4.33 (0.71)	3.51 (0.79)	3.59 (0.90)	2.50 (0.82)	2.83 (1.02)
X14	Financial and occupational success	32	4.16 (0.81)	3.63 (0.91)	3.41 (1.07)	2.50 (0.92)	2.81 (0.97)

(*) Clusters with fewer than 10 data points are omitted; clusters with fewer than 50 data points are shaded.

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Importance ratings were high (>4) for all goals, with family-related goals rating the highest in importance by respondents, followed by self-fulfill. Interestingly, despite its high frequency, health scored the lowest of all goal types in this category. Confidence in achieving the mentioned goals was overall moderate, with family ranking highest in confidence, followed by morals and values. Most goals were rate as difficult to achieve, with virtues ranking the least difficult and intellectual competence and financial and occupational success the most difficult. Emotionally draining ratings were positively correlated with difficulty, while previous success was positively correlated with confidence. However, the goals’ importance was not correlated with their difficulty or emotional load. This suggests that respondents are aware of their ability to reach goals and feel pressure to achieve their goals, but that the pressure does not come from the perceived importance.

To investigate if gender or generational differences among individuals might play a role in the value ratings of each goal, the ratings of each goal with gender and generation were modeled as dependent variables. The resulting coefficients and statistical significance are shown in Figure 2. Female respondents are less confident in achieving health-related goals than males (β = −0.18, p = 0.015) and find both health (β = 0.09, p = 0.002) and ambition and ability (β = −0.33, p < 0.001) goals more emotionally draining than their male counterparts. On the other hand, virtue-related goals are rated more important by females than males (β = 0.49, p = 0.001). In terms of generational differences, Gen Zers rate intellectual-competence-related goals as less emotionally draining (β = 0.68, p = 0.003) and have experienced more previous success (β = 0.48, p = 0.017) than Millennials.

3.3. The Role of Service Providers

Different service providers may be more or less qualified to support individuals to achieve their goals. To understand the views that individuals have concerning which service providers are more qualified to support individuals’ goals, each responder was asked to rate how much a particular provider would help them in attaining the goals they mentioned.

Educational establishments ranked highest overall, followed by healthcare providers and entertainment and technology companies, as shown in

Table 4. Potential support by service provider and impact of gender and generation.

Service Provider	Avg Score	Female	Male	p-Value	Gen Y	Gen Z	p-Value
Education	3.74	3.63	3.92	<0.001	3.73	3.73	0.97
Healthcare	3.27	3.28	3.24	0.63	3.27	3.25	0.83
Entertainment	3.27	3.28	3.24	0.65	3.22	3.33	0.23
Technology	3.10	2.93	3.40	<0.001	3.16	3.02	0.14
Retail	2.91	2.93	2.88	0.62	2.87	2.95	0.36
Utilities	2.43	2.33	2.61	<0.01	2.46	2.41	0.58
Local Services	2.38	2.33	2.47	0.11	2.37	2.37	0.98
Insurance	2.24	2.19	2.31	0.14	2.28	2.19	0.29
Banking	2.19	2.08	2.38	<0.001	2.21	2.18	0.75
Automotive	2.05	1.97	2.19	<0.01	2.05	2.08	0.76

p-values were calculated using ANOVA.

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There are some differences by gender, with males showing a higher affinity for providers in education, technology, and banking, while there are no significant differences between Millennial and Gen Z respondents. The prominent role of education can again potentially be explained by the population sampled, while healthcare can be linked to the high trust enjoyed by these providers. More unusual is the high score for entertainment and technology providers. The latter have typically not enjoyed a high level of trust in Switzerland as compared with local providers (Pugnetti and Seitz 2021), but they nonetheless seem to be viewed as a reasonable partner to support achieving personal goals. Insurance does not appear to be a particularly desirable partner, as do many other established industries.

The respondents’ interest in receiving support by different service providers depends in general on the type of goal being pursued. This is the case for all but three of the service providers. While for healthcare providers the results are undifferentiated but high, for utilities and especially for insurance the results are undifferentiated and low, as shown in

Table 5. Preferred support by goal type.

Service		X1	X2	X4	X5	X6	X8	X10	X11	X12	X13	X14
Provider	p-Value	Morals and Values	Virtues	Self-Fulfill	Open to Experience	Self-Protect	Security/Belonging	Health	Family	Ambition/Ability	Intellectual Competence	Fin./Occup. Success
Education	<0.001	4.14	4.01	3.90	4.04	3.66	3.81	3.28	3.20	3.88	4.54	4.44
Healthcare	0.09	2.91	3.36	3.37	3.24	3.45	3.00	3.34	3.20	3.18	3.15	3.59
Entertainment	<0.001	3.44	3.54	3.83	4.05	3.24	3.85	2.76	3.50	3.31	3.11	3.22
Technology	<0.001	3.06	3.16	3.04	3.11	2.59	2.81	2.97	2.20	3.25	3.46	3.59
Retail	<0.001	3.12	2.63	2.76	2.99	2.72	2.50	3.18	2.20	2.79	3.05	2.68
Utilities	0.08	2.56	2.48	2.49	2.58	2.38	2.34	2.27	2.50	2.54	2.68	2.28
Local Services	<0.001	2.88	2.45	2.57	2.38	2.41	2.60	2.15	2.50	2.44	2.54	2.34
Insurance	0.31	2.26	2.20	2.31	2.16	1.96	2.06	2.18	2.00	2.28	2.47	2.47
Banking	<0.001	2.21	2.16	2.32	2.27	2.34	1.96	1.93	2.60	2.32	2.59	2.75
Automotive	<0.001	2.26	2.06	2.06	2.26	1.76	2.06	1.81	2.10	2.24	2.31	2.16

Goal clusters with fewer than 10 data points are omitted; clusters with fewer than 50 data points are greyed out; p-values were calculated using ANOVA.

. This could potentially indicate that these service providers have not established a clear profile in the eyes of Millennial and Gen Z customers. For insurance, this result is consistent with the existing literature (Pugnetti and Bekaert 2018).

Table 5 also displays a heatmap of the difference between the desirability of support from different service providers for each type of goal, with values above average shaded in blue and values below average in red. Darker colors indicate larger deviations from the overall mean value. The significance of the variation of the preference for the support by a particular service provider was calculated using ANOVA.

Overall, respondents are more open to support from education providers for goals pertaining to intellectual competence, financial and occupational success, and to some extent morals and values. They are less open to goals focusing on health and family and to some extent on self-protect. Entertainment is particularly linked to openness to experience but not to health goals, whereas technology is sought after to support financial and occupational success but not family. These differentiated results can help service providers sharpen their profile and offer solutions particularly targeted at classes of problems and/or communicate effectively to address these prevailing perceptions. As discussed earlier, insurance companies are singularly undifferentiated in individuals’ perceptions and not particularly desired as a partner across all goals. Looking at the data by goal cluster, rather than by provider, some clusters can be observed, e.g., financial and occupational success, intellectual competence, and morals and values, are linked to higher provider preferences. On the other hand, other goal clusters, e.g., family, health, self-protect, and security and belonging, are linked to providers being excluded from customer preferences.

To understand gender or generational differences in service provider support across goals, the service provider ratings for each of the goals individuals mentioned were modeled with gender and generation as dependent variables. The results can be seen in Figure 3. Where gender differences can be observed, female respondents overall are less open to support from service providers than males. This is more evident in health-related goals, where education, technology, utilities, and local services are rated as less supportive by females than males. Similarly, females also find ambition and ability goals as being less supported by technology, utilities, and banking and automotive services. These results suggest that overall males view service providers as a way to achieve their goals relative to females.

Generational differences can also be observed in some goal categories. For instance, in financial and occupational success goals, Millennials find more support from technology services, while Gen Zers value education more. Interestingly, Gen Zers also find more support from entertainment services for security and belonging goals than Millennials.

3.4. Preferred Type of Contact

Respondents also indicated their preferred type of contact with the supporting service providers. In general, respondents preferred an in-person conversation, followed by an email exchange or interaction through a mobile app, as shown in

Table 6. Preferred type of contact and impact of gender and generation.

Type of Contact	Avg Score	Female	Male	p-Value	Gen Y	Gen Z	p-Value
In Person	3.76	3.69	3.90	0.02	3.76	3.75	0.86
Email	3.17	3.24	3.06	0.06	3.16	3.17	0.93
Mobile App	3.15	3.18	3.13	0.63	3.15	3.15	0.99
Online Portal	3.06	3.15	2.95	0.04	3.01	3.11	0.32
Phone	2.95	2.91	3.04	0.17	3.00	2.88	0.20
Chat	2.83	2.82	2.85	0.72	2.81	2.86	0.60
Video Call	2.60	2.49	2.79	<0.01	2.65	2.52	0.18

p-values were calculated using ANOVA.

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Thus, even younger customers value personal advice and are open to email communications, vindicating to some extent traditional advice models, for example in insurance. The relatively high rank of a mobile app interaction seems to indicate that the new app-based advice models are being well-received by younger customers. Interaction by phone, on the other hand, scores in the bottom half of the rankings and is another indication of the continuing decline of the telephone as a communication channel among these age cohorts. Despite the widespread implementation of online teaching during COVID-19, or perhaps because of it, video calls are not an established channel and score lowest among all options.

The preferred type of contact also generally varies by the type of goal being pursued, as shown in

Table 7. Preferred type of contact by goal type.

Type of Contact		X1	X2	X4	X5	X6	X8	X10	X11	X12	X13	X14
	p-Value	Morals and Values	Virtues	Self-Fulfill	Open. to Experience	Self-Protect	Security /Belonging	Health	Family	Ambition/Ability	Intellectual Competence	Fin./Occup. Success
In Person	<0.001	4.09	3.97	4.13	4.07	3.90	4.06	3.41	4.20	3.76	3.94	4.09
Email	<0.01	3.09	3.17	3.19	3.25	3.46	2.69	3.02	3.10	3.28	3.38	3.78
Mobile App	<0.001	3.12	2.82	3.04	3.07	3.17	2.51	3.35	2.50	3.10	3.40	3.20
Online Portal	<0.01	3.09	2.67	2.95	3.04	2.97	2.50	3.25	2.30	3.05	3.14	3.25
Phone	<0.001	3.12	3.30	3.15	3.06	3.21	3.06	2.61	3.90	3.03	3.24	3.38
Chat	0.49	2.68	2.95	2.91	3.03	2.72	2.75	2.72	2.50	2.84	2.96	2.97
Video Call	<0.001	2.74	2.89	2.87	2.80	2.82	2.60	2.22	2.60	2.65	3.00	3.28

Goal clusters with fewer than 10 data points are omitted; clusters with fewer than 50 data points are greyed out; p-values were calculated using ANOVA.

. The table shows a heatmap to understand variability across the different goal types, similarly to Table 5 for shading. In-person contact is widely accepted, with the potential exception of health. Email is a welcome channel for financial and occupational success but not for security and belonging. Mobile apps are less preferred for security and belonging and family, as are online portals in this latter category. Phone conversations are linked with family but not with health. Video calls also do not work well with health but are a welcome communication channel for financial and occupational success.

Table 7 can also be read vertically to provide insights for the affinity of different goals categories for the different communication channels. For security and belonging and health the selection is negative, with several channels below the average acceptance for that channel, while financial and occupational success has a higher preference for several channels. For family, the picture is more differentiated, with several channels below their respective average, with the exception of a stronger preference for phone conversations.

The preference for each communication channel is positively correlated with the preference for the service provider involved. However, here there are also nuances, as shown in

Table 8. Regression coefficients for type of contact based on service provider preference.

Service Provider	In Person	Email	Mobile App	Online Portal	Phone	Chat	Video Call
Education	0.27 ***	0.21 ***	0.12 **	0.10 **	0.24 ***	0.16 ***	0.27 ***
Healthcare	0.14 ***	0.15 ***	0.17 ***	0.15 ***	0.15 ***	0.12 ***	0.12 ***
Entertainment	0.20 ***	0.08 **	0.05	−0.01	0.19 ***	0.19 ***	0.18 ***
Technology	0.08 *	0.18 ***	0.24 ***	0.18 ***	0.16 ***	0.24 ***	0.21 ***
Retail	0.05	0.14 ***	0.20 ***	0.16 ***	0.08 *	0.13 ***	0.05
Utilities	0.19 ***	0.14 ***	0.15 ***	0.08 *	0.23 ***	0.18 ***	0.24 ***
Local Services	0.24 ***	0.15 ***	0.11 **	0.03	0.24 ***	0.17 ***	0.21 ***
Insurance	0.15 ***	0.16 ***	0.11 **	0.09 *	0.24 ***	0.15 ***	0.25 ***
Banking	0.22 ***	0.16 ***	0.08 *	0.03	0.26 ***	0.20 ***	0.30 ***
Automotive	0.19 ***	0.13 **	0.05	0.00	0.27 ***	0.17 ***	0.26 ***

p-values: *** p < 0.001; ** p < 0.01; * p < 0.05.

. The table summarizes the linear regression coefficients for type of contact preference based on the preference for a particle type of service provider.

The acceptance of interaction through an online portal is only weakly correlated to the preference for service providers, indicating an overall acceptance for the channel. This is true to some extent for email, mobile app, and chat. In these three channels, it is interesting to notice the higher regression coefficient for technology service providers, indicating that they should potentially pay more attention to select communication channels when they are not the provider of choice. In-person, phone, and video call communication, on the other hand, are strongly linked to provider preference. A potential interpretation is that for synchronous communication modes, consumers would like to interact with preferred service providers if possible.

Looking at gender or generational differences in type of contact preferences across goals (Figure 4), female respondents are less inclined to prefer in-person contact for intellectual competence and morals and values-related goals than their male counterparts. On the other hand, they tend to prefer email and online portal contact for health goals relative to male respondents.

Some generational differences can also be observed, with Millennials preferring email contact for security and belonging goals and mobile apps for intellectual competence goals, while Gen Zers have a preference for online-portal-mediated contact for self-protect-related goals.

3.5. Personality Traits

Generational and gender personality scores are shown in

Table 9. Personality scores and impact of gender and generation.

Personality Traits	Avg Score	Female	Male	p-Value	Gen Y	Gen Z	p-Value
Openness/Intellect	2.86	2.78	2.97	<0.001	2.90	2.81	0.02
Conscientiousness	3.36	3.48	3.18	<0.001	3.35	3.37	0.66
Extraversion	3.45	3.45	3.72	0.69	3.45	3.45	0.92
Agreeableness	3.97	3.99	3.96	0.37	3.96	3.99	0.39
Neuroticism	2.83	2.91	2.72	<0.001	2.81	2.85	0.34
Honesty–Humility	2.43	2.32	2.60	<0.001	2.44	2.41	0.50

p-values were calculated using ANOVA.

. Gender influences several personality traits, with women being higher in conscientiousness and neuroticism, while men scored higher on openness/intellect and honesty–humility. On the other hand, personality traits do not vary significantly across generational cohorts in this sample.

3.6. Personality Traits and Service Providers

The study also investigates the link between personality traits and the interest in receiving support from different providers.

Table 10. Regression coefficients for provider preference based on strength of personality trait.

Service Provider	Openness/Intellect	Conscientiousness	Neuroticism	Extraversion	Agreeableness	Honesty–Humility
Education	0.13	0.02	−0.11	0.19 **	0.29 ***	0.04
Healthcare	0.02	0.07	0.23 ***	0.04	0.17 *	−0.07
Entertainment	0.26 **	−0.14	−0.08	0.24 ***	0.36 ***	−0.01
Technology	0.25 **	0.06	−0.10	0.08	0.11	−0.30 ***
Retail	−0.11	0.14	0.21 **	0.01	0.12	−0.20 *
Utilities	0.13	−0.02	0.02	0.25 ***	0.26 **	−0.24 **
Local Services	0.12	−0.02	−0.01	0.15 *	0.12	−0.10
Insurance	0.02	0.19 **	0.14 *	0.12 *	0.08	−0.23 ***
Banking	0.18 *	−0.03	−0.08	0.24 ***	0.20 *	−0.21 **
Automotive	0.12	−0.10	−0.06	0.23 ***	0.08	−0.26 ***

p-values: *** p < 0.001; ** p < 0.01; * p < 0.05.

summarizes the regression coefficients and the statistical significance of the linear fit for service provider preference based on the score for a particular personality trait. In general, higher scores for extraversion and agreeableness are linked with a higher interest in external support. On the other hand, the opposite is true for high honesty–humility scores. The connection is more nuanced for the remaining personality traits. However, the impact of each personality is statistically significant only in one direction for all service providers and it is positive in all cases except for honesty–humility.

Another interesting comparison is that between agreeableness and honesty–humility. Agreeableness drives acceptance of the high-scoring service providers at the top of the table, whereas low honesty–humility provides a boost to acceptance for lower-scoring providers towards the bottom of the table. This suggests a potential diversification strategy for these companies to target low honesty–humility customers. Expanding on this observation, technology and banking providers could potentially in addition focus on customers displaying high openness/intellect, whereas insurance players seem to have a unique leverage point in high-conscientiousness customers.

4. Discussion

The goals pursued by the students surveyed are heavily concentrated, with the top three categories accounting for more than three quarters of all goals stated. There do not seem to be significant generational differences and only minor differences by gender. Respondents also preferred support from educational, healthcare, and entertainment/cultural institutions, and were considerably less open to receiving support from commercial service providers. In-person exchanges were the preferred mode of communication, followed by email and mobile apps. For both these observations, there were also only minimal differences across genders and none across generations.

Investigating the link between type of goal and potential service providers or type of contact provides more differentiated insights. While the scoring remains fairly stable across the types of goals, sweet spots for different service providers and types of contact can be identified. For example, education providers are linked to intellectual competence, entertainment to openness to experience, and healthcare to health goals. Similarly, preference for in-person contact is consistently the highest across all goal types. However, this preference is the weakest for the largest goal category, health. In this category, mobile apps score a very close second, potentially indicting a maturing field of application.

Personality traits vary across genders, with females tending to be higher in neuroticism and conscientiousness and lower in openness/intellect and honesty–humility than their male counterparts. Gender differences in personality have been previously reported, confirming the observed differences in neuroticism scores, though these also report higher scores for agreeableness, and to a lower extent extraversion, in females (Weisberg et al. 2011; Schmitt et al. 2008; McCrae and Terracciano 2005). Gender differences in personality are moderated by both age and ethnicity (Weisberg et al. 2011; Schmitt et al. 2008), which may explain differences in the results found in this study with those of previous reports, as here our participant population is relatively young, and ethnicity was not taken in consideration.

Personality also seems to mediate the preference for different service providers to support goal attainment. This may open the door for differentiated market approaches, especially for the less-preferred providers, by leveraging the differential personality profile of costumers found by this study.

4.1. Addressing the Research Questions

The analysis conducted in Section 3 can therefore provide some answers to address the research questions formulated.

R1:

What role can service providers, especially insurers, play in supporting the pursuit of individual goals?

Providers can play a significant and welcome role supporting goal attainment. Especially noncommercial actors (education and healthcare) seem to be widely accepted. There are, however, significant difference by goal type, suggesting an opportunity for insurers and other commercial providers to develop specialized models.

R2:

What are the differences by age cohort (Gen Y vs. Gen Z) and gender?

There do not seem to be significant differences between Gen Y and Gen Z respondents, and only relatively minor gender differences.

R3:

How does personality impact customer preferences for support on goal attainment?

Personality has a detectable impact on customer preferences. In some cases, such as agreeableness and extraversion, the effects are consistent across providers. In others, they favor or disfavor particular providers.

R4:

What is the “sweet spot” for insurance companies to play a unique and sustainable role in supporting customers’ goals?

While the overall picture is not fully supportive of commercial service providers, and insurers in particular, the results show several interesting possibilities, discussed in detail in Section 4.2 below.

4.2. Implications for Insurers

Preferred service providers, such as education and healthcare, enjoy relatively consistent high scores and a clearly identifiable sweet spot by goal type for interacting with Gen Y and Gen Z customers. On the other hand, the path forward for generally less-preferred providers is less visible and considerably more complicated. Insurance in particular faces two challenges: the first is that the scoring is low across goal types; the second is that there is little differentiation in the minds of potential customers where it can best play a supporting role (Table 5). The latter observation is both a challenge and an opportunity, as it can potentially allow enterprising insurers to proactively define their role. Insurers can leverage a differentiating factor as they embark in this journey: customers who score high on conscientiousness and/or low on honesty–humility seem to be particularly open to insurers (Table 10). This opens the possibility for targeted customer acquisitions and messaging.

The path forward may also differ by type of goal supported. To investigate this, the responses in Table 3 were analyzed relative to the mean values, as shown in Figure 5.

4.2.1. Focus on High Scores

The first potential strategy for insurers would be to concentrate on goals where they are relatively better positioned: intellectual competence and financial and occupational success. In both these areas they may choose to collaborate with established educational providers to enhance their acceptance. Here, they can work to develop their customers’ expertise on financial matters. While several insurance companies are supporting financial literacy efforts, it is the authors’ opinion that insurers should focus on the development of their customers’ understanding of risk and risk management tools. This would allow a differentiated profile and it would be a better fit for insurers than overall financial literacy. It would also be a topic uniquely suited to the conscientious and/or low honesty–humility customers. These goals, especially intellectual competence, are perceived to be difficult and emotionally draining. Thus, real support from insurers should be visible and appreciated.

A less evident but very interesting approach for insurers concentrating on relatively higher scores would be to focus on self-fulfill. This goal cluster contains motives such as having a mature understanding of life, accepting things as they are, and feeling satisfied with one’s life that can all be satisfied in part through understanding risks and managing it together with a trusted partner. For this goal cluster, the preference for other service providers is visible; on the other hand, there is no single provider dominating the scene as is the case for other goal types. These goals are very important and emotionally draining, but not perceived to be particularly difficult. However, respondents have been somewhat unsuccessful in achieving them in the past. Perhaps the most significant organizational challenge for insurers to support these goals is implied. These goals require personal development over a longer time horizon and require an alignment from insurers away from transactional, product-centric interactions to meaningful longer-term interactions, perhaps as part of a network of specialized providers. The additional complexity in this approach is not only to deliver value to customers but also to extract value for the insurers.

4.2.2. Focus on High Frequency

The second potential strategy for insurers is to focus on high-frequency goals, health, and ambition and ability. Supporting health goals may be challenging for insurers. The preference for supporting health goals seems to be quite diffused, and especially health insurers can create a credible argument for interacting with customers for these goals. The motivation, however, is less clear for P&C and life insurers. To further complicate the picture, in spite of the high frequency of health goals, they are perceived to be neither particularly important nor challenging or emotionally draining to achieve.

More promising seems to be an approach focusing on ambition and ability. This cluster includes motives such as avoiding or overcoming failure, being confident and in control, being efficient and getting things right, and being rational, among others. A number of these motives are close to and can benefit from technical expertise from insurers. Risk management, as a framework to understand and plan for uncertainty in one’s life can fulfill this customer need, again with a unique fit with the current profile of insurers.

5. Conclusions

The role of commercial service providers to support goals for Gen Y and Gen Z customers seems to be limited at this time. However, there seems to be an opportunity to concentrate on segments centered around goal type and personality type. These opportunities vary depending on the service provider. For insurers, this could mean concentrating on goals relating to understanding risk and making more rational decisions, especially for conscientious customers. For these types of goals, customers seem open to apps as an additional communication channel expanding on traditional in-person and email exchanges.

Several potential extensions to the study can help sharpen and expand its message. For starters, the analysis can be repeated outside Switzerland or to include other age cohorts to understand how those two factors may have impacted the results. Secondly, there seems to be an ongoing debate about the dimensions of the Cybernetic model for personality traits that was used as a foundation to the research methodology. As that debate evolves, it would be interesting to test alternative models of personality. Thirdly, goal taxonomy is an important component of the analysis, and allocating individual goals to categories post facto proved to be subjective. An interesting extension of the methodology would be to ask respondents to allocate their stated goals directly to a category. This would allow the investigation of potential support based on self-assessment of goal type, which is in any case the only relevant information available to customers as they choose providers.

While the opportunities for further expansion are significant, this investigation can provide a first roadmap for insurers and other service providers to expand and further tune their business models.