Motivated or Inhibited? - An Analysis of the Predisposition to Adopt Technological Implements in Personal Financial Planning

Silveira, Heber Pessoa da; Albergaria, Matheus; Farnezi, Paulo

doi:10.15728/bbr.2022.1317.en

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Article • BBR, Braz. Bus. Rev. 21 (4) • 2024 • https://doi.org/10.15728/bbr.2022.1317.en copy

Motivated or Inhibited? - An Analysis of the Predisposition to Adopt Technological Implements in Personal Financial Planning

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The adoption of technology in Personal Financial Planning (PFP) is permeated by motivating and inhibiting factors, with the predisposition to adoption being strongly influenced by personal traits. The present paper analyzed this predisposition among 366 higher education students in the city of São Paulo, Brazil. Using primary data and applying Logit and Probit regression models with Maximum Likelihood estimators, the study combined sociodemographic variables with constructs from a validated psychometric scale, assessing individuals in relation to their adoption of financial technology tools. Contrary to the notion that people avoid technology tools due to insecurity, the results showed that the inhibitors "Discomfort" and "Insecurity" did not play a relevant role in the intention to use technology in the PFP. The motivators "Optimism" and "Innovativeness" are considered statistically significant in the intention of use. Those who don't use it are quite disinterested and unconvinced of the benefits, therefore averse to technology, while those who use it highlight the benefits of technology in the PFP.

KEYWORDS
Personal Financial Planning; Financial education; Technology Readiness Index (TRI)

Gender	Case Number	Frequency
Male	169	46%
Female	197	54%
Total	366	100%
Marital Status
Single	264	72%
Married	82	22%
Common law marriage	10	3%
Separated/Divorced	10	3%
Total	366	100%
Schooling Level
Undergraduation	168	46%
Graduation	159	43%
Master’s	39	11%
Total	366	100%
Occupational Situation
Doesn’t work, only studies	43	12%
Out of work	14	4%
Freelancer or Entrepreneur	15	4%
Private company employee	276	75%
Public company employee	16	4%
Retired	2	1%
Total	366	100%
Income
No income	47	13%
Up to 1 minimum wage (up to R$ 998.00)	16	4%
From 1 to 3 minimum wages (R$ 998.01 to R$ 2,994.00)	122	33%
From 3 to 6 minimum wages (R$ 2,994.01 to R$ 5,988.00)	89	24%
From 6 to 9 minimum wages (R$ 5,988.01 to R$ 8,982.00)	45	12%
From 9 to 12 minimum wages (R$ 8,982.01 to R$ 11,976.00)	23	6%
From 12 to 15 minimum wages (R$ 11,976.01 to R$ 14,970.00)	2	1%
More than 15 minimum wages per month (above R$ 14,970.01)	22	6%
Total	366	100%

Constructs	Cronbach's alpha
Optimism and Innovativeness	0.68
Discomfort and Insecurity	0.62
Cronbach's α combined from the TRI scale	0.62

Descriptive statistics - TRI
	N	Interval	Minimum	Maximum	Average		Standard deviation
	N	Interval	Minimum	Maximum	Coeff.	Error	Standard deviation
OPT average	366	3.25	1.75	5.00	4.108	0.030	0.57006
INN average	366	3.50	1.50	5.00	3.258	0.040	0.77367
DIS average	366	4.00	1.00	5.00	2.699	0,.042	0.81063
INS average	366	3.25	1.75	5.00	3.573	0.033	0.63539
Average TRI	366	2.63	2.06	4.69	3.274	0.021	0.39981

	Sample	Gender		Schooling level		Planning		IT tools
	Sample	Female	Male	Undergraduation	Graduation	Plan	Do not plan	Use technology	Do not use technology
Optimism	4.11	4.07	4.15	4.10	4.11	4.13	4.05	4.15	3.97
Innovativeness	3.26	3.05	3.50	3.15	3.35	3.36	2.98	3.33	3.02
Discomfort	2.70	2.68	2.73	2.66	2.73	2.73	2.63	2.69	2.73
Insecurity	3.57	3.64	3.49	3.56	3.59	3.58	3.55	3.55	3.64
TRI	3.27	3.20	3.36	3.26	3.29	3.30	3.21	3.31	3.16

TRI average differences between groups	Gender		Schooling level		Financial planning		Use IT in planning?
TRI average differences between groups	Female	Male	Undergraduation	Lato-sensu/Master’s	Plan	Do not plan	Use	Do not use
Female	-	0.00024^***	0.11385	0.00513^***	0.00794^***	0.7876	0.00172	0.35958
Male	-	-	0.00929^***	0.30715	0.14198	0.00513^***	0.24346	0.00035^***
Undergraduation	-	-	-	0.02353^**	0.19909	0.36333	0.07748^*	0.04659^**
Graduation	-	-	-	-	0.07642^*	0.01050^**	0.10481	0.00223^***
Plan	-	-	-	-	-	0.07335^*	0.68718	0.00634^***
Do not plan	-	-	-	-	-	-	0.03350^**	0.31334
Do not use technology	-	-	-	-	-	-	-	0.00246^***
Use technology	-	-	-	-	-	-	-	-

Technological Segmentation Classification	No. of observations	Sample percentage	Averages					TRI 2014
Technological Segmentation Classification	No. of observations	Sample percentage	Optimism	Innovativeness	Discomfort	Insecurity	Total TRI
Explorers	95	26%	4.52	3.74	2.15	3.12	3.75		18%
Pioneers	81	22%	4.35	3.85	3.55	3.89	3.19		16%
Skepticals	125	34%	3.75	3.03	2.47	3.58	3.18	38%
Hesitant	53	14%	4.16	2.27	2.90	3.74	2.95	13%
Avoiders	12	3%	2.67	2.13	2.81	4.17	2.45	16%

Segmentation	Gender		Marital Status		Schooling Level		Occupation		Income
Segmentation	Male	Female	Single	Married	Undergraduation	Graduation	Work	Do not work	Up to 3 min. wages	More than 3 min. wages
Explorers	66%	34%	68%	32%	39%	61%	84%	16%	51%	49%
Pioneers	47%	53%	79%	21%	44%	56%	93%	7%	48%	52%
Skepticals	35%	65%	73%	27%	52%	48%	82%	18%	50%	50%
Hesitant	30%	70%	66%	34%	49%	51%	79%	21%	60%	40%
Avoiders	67%	33%	75%	25%	33%	67%	67%	33%	33%	67%

Correlation coefficients between TRI constructs
	Optimism	Innovativeness	Discomfort	Insecurity
Optimism	1;00	-	-	-
Innovativeness	0.37	1.00	-	-
Discomfort	0.08	0.12	1.00	-
Insecurity	-0.09	-0.05	0.35	1.00

Dependent Variable	Scale	Attributes
Planning	Nominal Qualitative	1 = Plan; 0 = Does not plan	first regression
Planning tool	Nominal Qualitative	1 = software tool, 0 = organizers, notepads, calendar, other	second regression
Explanatory Variables	Scale	Attributes
Gender	Nominal Qualitative	1 = Women; 0 =Men	-
Age	Discrete Quantitative	number of years since birth	+
Age²	Discrete Quantitative	square of the variable "age"	-
Marital Status	Nominal Qualitative	1 = Single; 0 = Married	-
Schooling Level	Nominal Qualitative	1 = Graduate and Masters; 0 = Undergraduate	+
Occupation	Nominal Qualitative	1 = Employed; 0 = Not employed	+
Income	Nominal Qualitative	1 = Above 3 minimum wages.; 0 = Up to 3 minimum wages	+
OPT	Continuous Quantitative	values between 1 and 5 - Optimism construct of the TRI scale (average)	+
INN	Continuous Quantitative	values between 1 and 5 - Innovation construct of the TRI scale (average)	+
DIS	Continuous Quantitative	values between 1 and 5 - Discomfort construct of the TRI scale (average)	-
INS	Continuous Quantitative	values between 1 and 5 - Insecurity construct of the TRI scale (average)	-

Explanatory Variables	Expected Signal	MQO	Logit		Probit
Explanatory Variables	Expected Signal	Coefficients	Coefficients	Variation of Probability	Coefficients	Variation of Probability
Constant		1.119 ^***	*3.661 ^ [1.695]**	-	*2.434 ^ [1.909]**	-
Gender	-	-0.023	-0.081 [-0.0308]	-0.015	-0.069 [-0.447]	-0.021
Age	+	-0.054 ^***	-0.330 ^* [-3.023]**	-0.061	-0.204 ^* [-3.172]**	-0.065
Age²	-	0.0007 ^***	0.004 ^* [2.898]**	-	0.002 ^* [3.075]**	-
Marital Status	-	-0.120 ^**	*-0.706 ^ [-1.960]**	-0.119	-0.414 ^ [-2.000]**	-0.122
Schooling Level	+	0.128 ^*	*0.674 ^ [1.725]**	0.126	*0.417 ^ [1.774]**	0.133
Occupation	+	0.181 ^**	0.898 ^* [2.646]**	0.189	0.555 ^* [2.686]**	0.195
Income	+	0.001	0.012 [0.036]	0.002	0.011 [0.055]	0.003
OPT	+	-0.014	-0.086 [-0.369]	-0.016	-0.057 [-0.411]	-0.018
INN	+	0.109 ^***	0,637 ^* [3.230]**	0.118	0.367 ^* [3.240]**	0.117
DIS	-	0.017	0.102 [0.605]	0.019	0.045 [0.462]	0.014
INS	-	0.014	0.079 [0.373]	0.014	0.040 [0.323]	0.012
R² McFadden		0.096 (R^2)	0.085		0.085
Likelihood log		-203.942	-195.409		-195.313
Schwarz criterion		478.716	461.649		461.4580
Number of correctly predicted cases		-	263 (71.9%)		263 (71.9%)
No. of sample observations		366	366		366

Explanatory Variables	Expected Signal	MQO	Logit		Probit Model
Explanatory Variables	Expected Signal	Coefficients	Coefficients	Variation of probability	Coefficients	Variation of probability
Constant		0.046	-2.395 [-1.119]	-	-1.517 [-1.213]	-
Gender	-	0.010	0.091 [0.320]	0.014	0.047 [0.290]	0.013
Age	+	0.016	0.085 [0.728]	0.013	0.050 [0.741]	0.014
Age²	-	-0.0002	-0.001 [-0.982]	-	-0.0009 [-0.970]	-
Marital Status	-	-0.073	-0.687 [-1.374]	-0.097	-0.346 [-1.382]	-0.090
Schooling Level	+	0.120	*0.803 ^ [1.795]**	0.128	*0.449 ^ [1.774]**	0.128
Occupation	+	0.161	0.682 ^ [2.186]**	0.122	0.416 ^ [2.141]**	0.13
Income	+	0.038	0.341 [0.932]	0.053	0.174 [0.818]	0.049
OPT	+	0.070	*0.441 ^ [1.835]**	0.069	*0.251 ^ [1.752]**	0.071
INN	+	0.051	*0.331 ^ [1.725]**	0.052	*0.195 ^ [1.751]**	0.055
DIS	-	-0.024	-0.125 [-0.738]	-0.738	-0.075 [-0.751]	-0.021
INS	-	-0.018	-0.137 [-0.654]	-0.654	-0.056 [-0.453]	-0.016
R² McFadden		0.135 (R^2)	0.127		0.125
Likelihood log		-177.172	-173.061		-173.538
Schwarz criterion		425.176	416.953		417.9080
Number of correctly predicted cases		-	286 (78.1%)		286 (78.1%)
Sample observations		366	366		366

Type	Variable (explanatory)	Specification
Sociodemographic	Enrollment	Student registration number
	Gender	Male
	Gender	Female
	Age	Student's age
	Marital status	Single
		Married
		Consensual union
		Separated
		Divorced
		Widow/widower
	Current education level	Undergraduate
		Graduate
		Academic Master’s Degree
		Professional Master’s Degree
	Current course	Business Administration
		Executive Assistant
		Auditing
		Accounting Sciences
		Economic Sciences
		Business Accounting with IFRS and USGAAP
		Controllership
		Online Controllership
		Economy Applied to Business
		Corporate Finances
		Energy Management
		Public Management and Controllership
		Strategic People Management
		Logistics Management of Supply Networks
		Online Public Management in Auditing
		Online Public Management in Controllership
		Online Public Management
		Tax Management
		Marketing
		MBA in Accounting
		MBA in Business Management
		MBA in Risk Management and Compliance
		Executive MBA in Finance
		Capital Market
		Academic Master's in Accounting
		Professional Master's in Business Administration - Finance
		International Business and Foreign Trade
		Forensics
		Advertising
		International Relations
		Public Relations
		Executive Secretariat
	Occupational situation	Does not work, only studies
		Out of work
		Freelancer
		Private company employee
		Private company employee
		Entrepreneur
		Retired
	Individual monthly income	No income
		Up to 1 minimum wage (up to R$ 998.00)
		From 1 to 3 minimum wages (R$ 998.01 to R$ 2,994.00)
		From 3 to 6 minimum wages (R$ 2,994.01 to R$ 5,988.00)
		From 6 to 9 minimum wages (R$ 5,988.01 to R$ 8,982.00)
		From 9 to 12 minimum wages (R$ 8,982.00 to R$ 11,976.00)
		From 12 to 15 minimum wages (R$ 11,976.01 to R$ 14,970.00)
		More than 15 minimum wages per month (above R$ 14,970.01)

Dimension of Constructs	Construct (explanatory)	Below, you will answer 16 questions related to your view of how technology influences your Personal Financial Planning activities. Note: Questions comprise Technology Readiness Index 2.0, copyright A. Parasunaman and Rockbridge Associates, Inc., 2014. This scale has been duplicated with permission of the authors
Motivators	Optimism	OPT1 - New technologies contribute to improving our quality of life
		OPT2 - Technology gives me greater freedom to come and go
		OPT3 - Technology gives people more control over their daily lives
		OPT4 - Technology increases my productivity in my personal life
	Innovativeness	INN1 - I am sought after for advice on new technologies
		INN2 - I'm usually among the first of my friends to buy new technology as soon as it's on the market.
		INN3 - I can usually understand new high-tech products and services without help from others.
		INN4 - I stay informed about the newest technological developments in my areas of interest
Inhibitors	Discomfort	DIS1 - When I need technical support from a product or service provider, I feel that those who know more are passing me by.
		DIS2 - Phone support doesn't work because they don't explain things in a way that I understand
		DIS3 - Sometimes I think that gadgets or systems with technology were not designed to be used by ordinary people.
		DIS4 - No state-of-the-art product or service manual is written in accessible language
	Insecurity	INS1 - People rely heavily on the work that technology does for them.
		INS2 - Too much technology distracts people to the point that it becomes harmful
		INS3 - Technology diminishes the quality of relationships by decreasing face-to-face interaction
		INS4 - I am not comfortable doing business with partners who only allow online contact

Type	Variable (explained)	Specification
Specific Questions Personal Financial Planning Habits	Do you prepare your own Personal Financial Planning, with definition of objectives, control of revenues, expenses and investment strategy?	Yes
		No
	Which instrument do you use in the preparation of your Personal Financial Planning?	Notepad (paper)
		Appointment book (paper)
		Calendar
		Spreadsheet (Excel or similar)
		Electronic application installed on the computer
		Web-based (personal computer) and cloud-based planning application
		Mobile app installed on smartphone
		Personal Financial Planning Service (outsourced)
		Online Banking application linked to the current account of the bank where you have an account
		Others
	Do you prepare your personal financial budget, with a forecast of income, expenses and investments for six months ahead?	Yes
		No

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