Trust vs. E-commerce - The end of a myth?

There is still a big gap between the number of Internet users and online shoppers and although trust is considered by many as a key factor in e-commerce, the knowledge about it was little.

This was a quantitative research with a deductive approach in which statistical procedures were applied to validate the hypotheses of the theoretical frame of reference, and the research questions of this study were:


(i) What is the socio-demographic profile of university students’ online shoppers?
. Men have greater intention to buy online and recommend it to others than women.
. Working students have higher intention to purchase on the Internet than individuals who only study, but regarding their intent to recommend it to others the difference is not statistically significant.
. Older students have more intention to buy online and recommend it to others.
. Experienced online buyers have more intention to buy online and recommend it to others than individuals with less experience in online shopping.


(ii) What is the impact of trust and social influence in the intention to buy online?
. It was observed that the adjusted model regarding "Intention to Buy Online" in result of the weight of “Trust” and "Social Influence" explains only 1% of the variability of the "Intention to Buy Online", and although the "Social Influence" has more weight compared to “Trust", the relationships of these variables with the "Intention to Buy Online" are not statistically significant.


(iii) What is the impact of trust and social influence in the intention to buy online in individuals who never bought online before?
. It was observed that this model adjusted to the "Intention to Buy Online" regarding the weight of “Trust” and "Social Influence" explains only 4% of the variability of the "Intention to Buy Online" of those individuals, and despite that the "Social Influence" also has more weight than “Trust", the relationships of these variables with the "Intention to Buy Online "are also not statistically significant.


Conclusion:
This study confirmed the theoretical framework regarding the socio-demographic profile of online shoppers, namely that the profile is not homogeneous, even having differences between age groups as suggested by Reibstein (2002), with men having higher intention to buy online than women, as indicated by Greenfield Online in 1999 (quoted by Chih-Chung & Chang, 2005) and that the previous experience of Internet shopping of the individual is an important factor to consider, as stated by Perea y Monsuwe, Dellaert & de Ruyter (2004).

Regarding the central question of my research - "What is the Impact of Trust in the Intention to Buy Online?" - Turns out to be surprisingly that has been observed that Trust impact is in fact low, even in individuals who never bought online before.

However, some caution is required in interpreting these results, to avoid the mistake of generalization or abusive interpretation. Firstly, even considering that the sample was intentional, it is necessary to remember that the findings of this study apply only to the universe of students of ISEG (Technical University of Lisbon).

We also found that the impact of Social Influence in the "Intention to Buy Online" is very low too, although it was noted that this impact was greater than the Trust impact. So, it is important to remember that if consumers choose not to consult recommendations, consumers will rely on their knowledge or previous experience on the service to make their buying decision (Senecal, Kalczynski & Nantel, 2005).



Note: This is a summary of my Master Thesis. If you wish to read the complete study or the details of the mentioned bibliographic references, please follow the link below:

Antunes, A. (2011). Impacto da Confiança na Intenção de Compra Online. (Master Dissertation). Technical University of Lisbon Repository, Portugal.
https://www.repository.utl.pt/bitstream/10400.5/4459/1/DM-ANLGA-2011.pdf

The 2 Most Common Mistakes in Market Research

In management is common to rely on market research and statistics analysis to support the most important decisions in the company strategy.

Although the company top management don’t have to be experts in statistics, they must know how to identify the most common mistakes in Market Research studies.


1st Mistake: Correlation doesn't prove a cause-effect relation between the correlated variables

When a correlation is identified between 2 variables, you cannot automatically conclude that one reacts to another, establishing a cause-effect relationship between them.

Although it is possible that one of the variables causes a direct impact in the other variable, the correlation analysis is not enough to make that statement.

Example: Consider that there is a negative correlation between umbrella usage and beach towels usage in the summer.

Although there is a negative correlation you cannot conclude that if you force the population to use beach towels in a rainy day, they will automatically discard the umbrella usage in that same rainy day.

In this example the cause-effect relationship results from a 3rd variable not identified in the original correlation which is the weather in the summer. So, the weather in the summer is a 3rd variable that impacts directly both previous variables which were the umbrella usage and beach towel usage, creating a negative correlation between them.


2nd Mistake: “Confidence Interval” interpretation in a Market Research Study

When a market research report is presented, it’s common to rely only on the “Confidence Interval” to evaluate if the study conclusions are reliable or not.

That is a big mistake, because “Confidence Interval” is only relevant when presented also with the “Sampling Error”.

Why? It is easier to explain with examples:

Consider a study that has a 95% “Confidence Interval” and a 4% of “Sampling Error” usually is very reliable if the sampling method was appropriate an the interpretation of those statistical variables should be:
If I repeat the same study with other 100 samples of the same universe, probably I will get the same results only with 4% margin of error in 95 of those 100 samples.

On the other hand, if the study has the same 95% “Confidence Interval” but has 20% of “Sampling Error” usually that means that the results are not reliable, because the interpretation should be:
If I repeat the same study with other 100 samples of the same universe, probably I will get the same results with 20% margin of error in 95 of those 100 samples.

So, although you have the same “Confidence Interval”, the “Sampling Error” is critical to evaluate the quality of the Market Research study.