EIRP Proceedings, Vol 11 (2016)

Socio-Economic Factors’ Impact on the Offline Networking:

A Quantitative Analysis of Albanian Business

Besa Shahini¹

Abstract: It exists so many studies and research on networks, especially on business networks, but still there is a little research which explores the factors that influence a manager's’ willingness to participate in business networks. Some of the socio-economic factors that moderate this participation are explored through this paper and the focus is based on offline face-to-face networking activities between companies, especially in an Albanian context. The research findings shows how the socio-economic factors such as gender, age, position and education level, influence the participation of such a business network moderated by the personal networking behavior of an individual. The study is based on a quantitative analysis of Albanian business member of the business networks. The results suggest that these socio –economic effects are important, but they do not come first in the perception of the process. What is important, the analysis, furthermore, finds that personal behavior and attitude are to be considered as key issues when it comes to participation in offline business networks.

Keywords: Business networks; online/offline networking; socio-economic factors; quantitative analyze

JEL Classification: C8; M12; L21; O2

1. Introduction

It is said that “If you want to go somewhere, it is best to find someone who has already been there.” This quote goes perfectly with what is happening nowadays. Everywhere in the world, it is difficult for a business, with many projects ongoing, to find people who know everything. Most tasks and projects within companies have a broad scope and it is difficult to have the expert knowledge about all dimensions of a project. One major approach to circumvent these problems is business networks to exchange knowledge between professionals. We are living in a digital are and more of these experience-sharing exchanges are moved to online platforms or internet-based services.

This raises the questions:

• Do offline business networks have more benefits for the participants?

• Is it face-to-face meetings still appropriate?

• Which factors are influencing the willingness to be part of such an offline business network?

So this study is an attempt to draw a simple panorama of the way how online and offline business networks can cooperate with each other, and an attempt as well to use empirical quantitative tools, in order to evaluate which are the most important socio economic factors which influence the offline business networks for the managers of Albanian business, based on a simple survey.

2. Types of Business Networks

“Today it’s no longer an option not to have a network; it’s a necessity. Business has always been a game of trust and that’s even truer these days. We’re so bombarded with advertising we’re becoming desensitized to its messages so the power of trust and networking is growing – and why networks are so powerful. Also, entrepreneurs in start-up phases typically work alone or in a small team – it can be lonely.

It’s an incredible energy-booster and motivator to get out, meet other entrepreneurs who share the same values, the same visions, the same willingness to take a risk. And there is rarely any problem that no-one has had before! You get into a group of 10 or 200 people and there’s a high likelihood that someone there has had the same problem as you and knows how to solve it.”

Generally there are two types of business network:

1) Online network; 2) Offline (face to face) network.

Whatever the type of business be, the networking is all about relationships. It’s not just about followers, friends or business cards, it’s about connecting with one individual (manager of a company in our case of study) at a time and creating mutual benefit. This is why the online and offline networking should work together.

For so many years in the past, the networking has played a big part in serious business strategies. It makes a lot of sense to be visible and build supportive, credible relationships with key connections in the marketplace, especially when the marketplace was in the local community. But, during years, the markets grew and opportunities expanded, so it became difficult to do business profitably through the local business group alone. Networks merged with networks via key individuals and “global networks” were created (think Business Networks International and Chambers of Commerce). But besides this, the technology has advanced as well, the proliferation of communication tools has provided new opportunities to the forward thinking networker.

There are created and developed so many tools such as LinkedIn, Facebook, Google+ and Twitter, tools for the job – facilitating connections, accelerating relationship development and shortening business development cycles. So beside offline the online network is developed, they are compliment of each other, but no way do they substitute one another.

These three activities will enrich all of your relationships, both online and offline:

1. Foresight – before you meet up with a business connection, is there anything in their social media that indicates they are seeking help right now?

2. Insight – when you are with someone (whatever the environment) listens actively and keep thinking about how you may be able to help them.

3. Hindsight – follow up – the most overlooked activity in networking and the most powerful.

Networking is essential to business success, and its digital evolution means that it has never been easier for small businesses to make and develop important connections. The key, however, is to combine online networking with offline – so don’t always choose a Google hangout over a coffee catch-up.

Facebook is the main contact-point if you want to connect yourself with friends whereas LinkedIn is the equivalent but with focusing on professionals. On the other hand, the speakers invited to business networking events, themselves are part of other business networks with organizers. The better the background of the panel, the higher is the positive impression on the participants. Thereby, we can agree that online networks play with numbers; visuals and facilities (for ex. mobile applications) and offline networks stay alive and succeed by handshakes, in person interaction, trust and selective information. Networking is used as a highly effective instrument to raise the common understanding and bring new interesting issues “on the table” in between companies.

Heather Townsend (2011), differentiates types of business networking events by goods that they present on an individual or organizational level.

• The first type she describes is named “Events and networking that help to learn, find answers and tools to problems”. These activities ensure opportunities to learn and benefit valuable continuing professional development credits. It becomes more useful especially for high-profile industry meetings, which most of the time include target market with the innovation of many decision makers

• The second type mentioned is “Events and networking groups to help building community of like-minded people”. The latter is efficient when the manager lacks time for networking activities and considers these types generally as a starting point to find the next position. It is recommended to introduce yourself in as many circulations as possible. Meeting the professional goal needs you to be out of the comfort zone and these events are the appropriate ones.

• The third type, “Networking opportunities designed to help to increase the profile of the participants”, is seen as the group with a high degree of diversity among participants. This is the reason why it is preferred to always be prepared for the topic that the event is dealing with; you never know who you will meet and what question may they ask you.

To conclude, “networking groups designed to generate referrals” is the fourth type. Successful events in this category require consistency in meetings participation. Many of these groups are driven by specified rules and policies, which makes them very structured and to some extent centrally governed.

3. Literature Review

The nature of networking implies people interaction, which can be perceived as a series of events that build trust among the actors and will lead to gain knowledge (Ford, 1980). Therefore it can be essential to form business networks for a firm or individual person as they are considered to provide valuable assets. These assets can facilitate acquisition of resources and knowledge, which help the firm or individual to survive and grow. (Moran, 2005).

The nature of networking consists of people interaction, perceiving it as a series of events that build trust among the actors leading to gain knowledge (Ford, 1980). Business networks are considered as assets which facilitate acquisition of resources and knowledge, which help the firm or individual to survive and grow (Moran, 2005).

Networks enable firms to obtain and retain knowledge and resources more efficiently as if they would rely on themselves in the economic context; Granovetter (1985) argued that social activities are an important factor in any action. Social networks are expected to contain valuable resources and capabilities, which are related to the firm's success (Dyer & Singh, 1998). Social networks can affect the individual’s power and influence (Krackhardt, 1993), each individual's chances to obtain an appropriate job (Granovetter, 1973) and can increase advancement potential and speed (Burt, 1992a).

Nahapiet and Ghoshal (1998) divided social interaction or relationships into two dimensions:

• The relation embeddedness;

• The structural embeddedness.

The relational embeddedness is described as personal relationships that have developed through a history of interactions An important aspect of relational embeddedness is trust, which can reduce transactional uncertainty and diminish the risk of opportunistic behavior by partners in the network (Morales & Martinez-Fernandez, 2010) and leads to better access to resources and improves the behavior in case of unforeseen problems (Uzzi, 1996). Another important aspect of rational embeddedness is considered to be closeness. Ties can provide the potential for either party to access valuable resources, but the level of closeness of both parties decides whether they actually provide those resources (Moran, 2005).

In contrast to relational embeddedness, structural embeddedness is considered to be “the impersonal configuration of linkages between people or units”.

Structural embeddedness is percepted as the possibility of gathering information, from the firm being member of that network (Moran, 2005). There is an advantage of having a lot of diversified participants in the network, so that the diverse knowledge and pool of resources are valuable and scarce resources.

As a conclusion: trust seems to be essential to have rich relationships, where close ties are built. On the economic perspectives firms need to consider themselves in a beneficial position in the network to be able to gain knowledge, which they assume to be critical for the firm or the individual. Considering Albanians as character in comparison with other nationalities, they pay a lot of attention to trust and it’s difficult for them to trust somebody. In terms of online and offline Networks one could assume that offline business networks have an advantage in relation to the prior theory because of the face-to-face contact which simplifies the process of building trust.

Research so far proposed as to be taken into consideration that some individuals are more likely to engage in networking activities than others. Forret and Dougherty (2001) found that gender, and other socioeconomic factors are related to networking behavior of managers and professionals.

Hambrick and Mason (1984), revealed that the age of the managers has a considered impact on the performance of the firm and moreover, they provide several reasons on why this effect is negative.

Thibaut & Kelley (1959) concluded that men see women (gender issue) as less attractive exchange partners, so they have traditionally more resources to offer others than women have had.

Managers (position level) who play important roles in the organization are more likely to be part of effective relationships (Kaplan, 1984). Managerial level positions are investigated by Michael and Yukl`s (1993), who explained that the higher the manager's level, the higher they engage in networking activities because they monitor the overall environment easier and handle across functional areas more than managers in lower levels.

Besides the ability and personality traits shaped by a person during time, the culture on how a country approaches education, determines productivity and satisfaction among generations. It is not only about finding a network, but also being accepted as an integral part of it and fulfilling successfully your duties. Furnham (2013) mentioned ability, education and experience as the needed attributes to find you in a team. Moreover, he considers education and regulations as most impacting attributes for behavior changes.

Individuals consolidate relationships through networking behavior with other individuals from external or internal parties, with whom they may have (or may not) prior common interests, but the upcoming results consist in the same degree of profitability (Barton, 2001; Fisher & Vilas, 1991; Kramer, 1998; RoAne, 1993; Wellington & Catalyst, 2001)

Forret and Doughery (2001) investigated if personal and job characteristics are associated to participation in networking behaviors of managerial employees. They concluded in a positive effect of socioeconomic features (age, gender, organizational level, education, etc.) and personal traits (self-confidence, extraversion and attitudes toward workplace politics) to the networking behavior.

4. Methodology

The main quantitative research question is focused on: ‘What impact do socioeconomic factors have on the offline business networks’ participation?

Considering the issue in this perspective, the participation in offline business networks is considered to be the dependent variable in this case. The four socio-economic factors, i.e. age, gender, position and education are chosen to be used as independent variables. Further, to get a better understanding of the ascendancies, the mean personal networking behavior is included in this study.

In Albania, the two most well-known business networks are considered to be: Chamber of Commerce and Industry (CCI, established since 1958) and Member of Albanian Business (MAB, established since 2010). The population of the study is closely linked to the database of these business networks. In order to generalize our results, the database of CCI and MAB was considered. This database consists of different contacts holding various relationships with CCI and MAB, which need to be considered: Members, former members, non-members, trials and partners.

In order to obtain a broad picture of factors influencing the participation in offline business networks, we selected self-administered questionnaires, which are to be filled out by the respondents independently and are distributed electronically through emails. To encourage respondents to complete the survey, the layout of the questionnaire should be professional-looking, thus the Google Forms was used as a service provider since this service is free of charge, easy and quick to administer and offers professional templates. From 220 email contacts, 180 opened the email and 142 responded, so the overall response rate is 64.5%.

The questionnaire was split-up into four blocks. The first section gives a brief and precise introduction to the research topic and why it is necessary to study this relationship. This part is of high importance in order to increase respondents’ interest and accordingly achieve a high response rate (Dillmann, 2007). The second block deals with the respondent's company-level data, namely location, size of the company and the nature of the company’s industry. The third block refers to the data of the respondents, e.g. gender, age, position and education, which directly links to the four mentioned socio-economic independent variables. This leads to the fourth block, the assessment of the respondent’s individual networking behavior when it comes to offline business networks, firstly on an individual level and secondly on a company level.

5. Data Analysis

The questionnaire contains open and ended questions, and it is provided categorical and numerical data, translated in variables, for numerical data, the discrete data is considered.

The dichotomous data were considered the variables of: gender (M/F), participation in offline business network (Yes/No), the numerical data is: age, while the categorical ones are considered: the education and the position. The “position” is grouped in five categories: assistant manager, general manager, senior manager, board member, director). This is based on Lewis (2015) approach to divide into low-, middle- and top-management and develops a distinguished perspective between board members and CEOs. The “education level” was grouped into: High school diploma, Bachelor, Master, Doctor or Professor.

The “mean personal networking behavior” is included as another independent variable using the previously validated scale by Forret and Dougherty in 2001. It consists of four five-point Likert scale items which measure how actively respondents behave in different offline business networks in terms of attending social activities with professional relevance, engaging actively as a speaker in these kind of events, participating in community activities, and engaging in networking activities to increase internal visibility within the company.

The main analysis consists of a binary logistic regression model, as the selected dependent variable is of a dichotomous nature consisting of two categories: The participation in offline networks (yes or no) according to our definition refers to the two-category outcome that should be predicted through the socio-demographic variables age, gender, position and education. By doing a sequential regression analysis it is further analyzed whether the mean personal networking behavior has an influence on the model. The analysis was performed using SPSS (Statistical programme for social science).

The mean values and correlations are displayed in Table 1. The average age of our sample is 41.9 and male and female respondents are equally distributed with a mean value of .43 (56.4% male; 45.4 % female). The majority of respondents, e.g. 68,2%, is holding a Master's degree and 25.4 % are holding a Bachelor degree. Moreover 47.4 % of the survey participants deviate from the high-level management, 43.2% from the medium-level management and 9.4% from a low-level management.

A first correlation analysis indicates an association between age and the Supervision (r=.211; p<.05) and between age and years (Duration position) within the position (Duration position) (r=.361; p<.05). This is supporting the general assumption that the older the age, the higher the position held and the more the years within that position, so it can be said that the level of the position and years within a position are increased by age.

Table 1. Basic descriptive statistics

Source: Author’s calcuation

A binary logistic regression is established to predict the effect of the socioeconomic factors of the respondents on the participation in offline business networks. So we can say that neither gender (b=-.38; p>.1), nor education (p>.1), nor age (b=-.014; p>.1), had a significant influence. On the opposite we find out that position has a weak significant association with the affiliation in offline business networks (p<.1). In particular position 2 and 3, e.g. general manager and senior manager, are more likely to participate (position2: b=1,818; p<.05; position3: b=1,894; p<.05). Than respondents holding an assistant or executive position. So, there is a correlation between job position and participation in business offline networks (POBN) but it is not of positive nature as assumed. Instead, the correlation is u-shaped which may be due to other influencing factors. Thus, we can conclude that compared to assistant managers, general managers have 6.2 times higher odds and senior managers have 6.67 times higher odds to be part of an offline business network. On the other hand, directors and board members have lower odds (1.97 and 1.88 respectively).

Table 2. Logistic Regression analysis

Variables	Participation in offline Business Networks ( POBN)
	M1		M2
	b	Exp.b	b	Exp b
Indipendant Variables
Age ( A)	-0.014	0.991	-0.014	0.991
Gender (G)	0.38	1.462	0.36	1.42
Position (P)
Position (P1)	1.818**	6.23	1.763**	5.793
Position (P2)	1.894**	6.67	1.692*	5.87
Position (P3)	0.68	1.976	1.365	1.387
Position (P4)	0.611	1.883	0.356	1.451
Education (E)
Education (E1)	0.567	1.823	0.374	1.453
Education (E2)	0.682	0.398	-0.72	0.54
Education (E3)	-1.134	0.285	-1.578	0.234
Mean personal network behavior (MPNB)			0.628**	1.892
Nagelkerke R2 0.27			0.295
Sample Size 142 * p<.1 ** p<.05

Source: Author’s calcuation

Adding our socioeconomic variables as predictors into the model, the Omnibus Test of model coefficients accounts for a Chi-Square of 17.44 on 9df, weak significance at 10% level, which stating that there is a slight difference to our null model. This results in partly rejecting our null-hypothesis, that socio-economic factors do partly significantly influence the affiliation to offline business networks. The pseudo R-squares, Cox & Snell R-square and Nagelkerke R-square, also give acceptable values (.155; .27 respectively), which means the proportion of variance explained by our predictors is 27%. Nevertheless it needs to be pointed out, that these R-square are not a true measure to assess the goodness-of-fit, but they may be useful for the evaluation of competing models (Hosmer, 2013). Additionally comparing the observed and predicted values by the model of the dependent variable does indicate a slight improvement by 3% from 64 to 67%.

Moreover we could have used the Hosmer and Lemeshow goodness-of-fit test, but we did not apply this test due to our small sample size, which would have biased our statistics. In this respect a sample size of more than 500 would be appropriate to give a more meaningful outcome (Hosmer et al., 1997).

From the conceptual framework, we included the mean personal networking behavior (MPNB). To analyze the effect on the model, the mean networking behavior is treated as an independent variable in the Model 2. The overall Cronbach-Alpha value to assess the reliability of the scale of .810 leads us to being able to use the MPNB as predictor variable. The outcome of the logistic regression gives us a medium significance of the MPNB (b=.628; p<.05) which the last hypothesis. As a result the odds of participating in POBN are 1,892 times higher, if your score increases by 1 unit.

The improved pattern quality of our model also represents the strong influence of the MPNB in participating in offline business networks. The Chi-square of MPNB the Omnibus test is 5.213 beyond a medium significance level of .05, adding up to a total Chi-square of 22.543 with 10df.

According to the goodness-of-fit test, this predictor increases the model quality (i.e. the value of -2 log likelihood is reduced comparing with model 1 and a higher Nagelkerke R-square (.27 and .295 respectively).

In order to verify the findings from the first sequential logistic regression, there are created dummy variables. The age was split into two dummies, e.g. below 40 and above 40, and grouped them into categories (20-29, 30-39 etc.) but did not achieve a significant influence.

Further, the years within the position is included as a dummy variable to confirm the influence of position on the participation in offline business networks. The responses are spread into below 4 years and above 4 years (being the mean years within a position of our sample) and got a (b=1.763; p<.5) for being longer than 4 years in a single position. Besides, dummies are tested to confirm the findings that position has an influence on the POBN. For this, the positions were grouped into lower- middle- and top-management (lower being assistant managers, middle being general and senior management, and top being directors and board members). The results we get support the findings of the model 2 analysis with lower management of a b-value=-2.542 and being highly significant (p-value<.05) and results for the top management of a b-value=-1.754 and p-value<.05. On the other hand middle management and thereby general and senior managers results in a b-value=1.412 being highly significant (p-value<.05).

6. Conclusion

Networking is about interacting with people and engaging them for mutual benefit. t can help you establish a new business or grow an existing one. You can also use networking as a tool for finding investors, customers, staff, suppliers and business partners with minimal cost to your business. You can network face-to-face at social events, conferences and through industry associations. You can also network online, through sites such as LinkedIn and Facebook. The more ways you can network, the more your business will benefit.

Still Albanian business, is fanatic to tradition offline (face to face) networking, even though a lot of progress has been done in the online one. Being a Mediterranean country, face to face relations are still very traditional and respected so this study is an attempt to evaluate the impact of socio economic factors affecting the offline business networking.

As shown through this study conducted with an internationally spread survey and the logistic regression analysis of the data, socio-economic factors such as age, gender and education do not have a strong influence on an individual’s participation in offline business networks. As the first model is analyzing the impact of the socio-economic factors, ones individual’s job position is the only factor having a significant influence on the dependent variable namely participation in offline business networks. When adding the personal perspective to the regression, the model improves itself, giving significance to the MPNB on the participation in offline business networks. Thus, personal networking behavior such as actively attending social activities with professional background and taking speaking engagement are more important than factors such as age and gender.

Finding out, that middle-level management positions tend to be more willing to participate compared to low and high-level managers lead to the assumptions, that several factors may influence the impact of the job position. This could be due to constricts in support and available time, which is connected to the job position. As these factors are very sensitive, a more appropriate way to dive deeper into this topic would be by considering this fact within a qualitative study.

The quantitative study detected that there is no correlation between the socioeconomic factors age, gender and education on the participation in offline business networks. To further dive into the reason, why these factors did not give a significant correlation and which other variables might play a role, this study can be an initiative to undertake a further qualitative analysis on this spot.

7. References

Creswell, John W. (2003). Research design, Qualitative, quantitative and mixed methods approaches. SAGE publications, 2nd edition.

Crotty, Michael (1998). The Foundations of social research- Meaning and perspective in the research process. SAGE publications.

Dillman, D.A. (2007). Mail and Internet Surveys: The Tailored Design Method. 2nd Ed. Hoboken: Wiley.

Ericsson, K.A. & Simon, H.A. (1980). Verbal reports as data. Psychol. Rev. 87:215–51.

Ford, D. (1980). The development of buyer – seller relationships in industrial markets. European. Journal of Marketing, 14 (5/6), 339 – 54.

Foddy, W. (1994). Constructing Questions for Interviews and Questionnaires. Cambridge: Cambridge University Press.

Furnham, Adrian (2013). The Resilient Manager The Resilient Manager - Navigating the Challenges of Working Life. Palgrave Macmillan.

Greene, Jennifer C.; Caracelli, Valerie J. & Graham, Wendy F. (1989). Educational Evaluation and Policy Analysis. Vol. 11, No. 3 (Autumn), pp. 255-274.

Heather, T. (2011). How to Use the Power of Online and Offline Networking or Business Success. NJ: Prentice Hall.

Hess, J, & Singer, E. (1995). The role of respondent debriefing questions in questionnaire development. Presented at Annu. Meet. Am. Assoc. Public Opin. Res., Fort Lauderdale, FL.

Hosmer, David W.; Lemeshow, Stanley, Sturdivant & Rodney, X. (2013). Applied Logistic Regression. 3rd Edition. NY: John Wiley & Sons, Inc.

Krosnick, J.A. (1999). Survey Research. Annual Reviews Psychology, Vol. 50, pp. 537-567.

Lewis, Jared (2015). What Are the Three Levels of Hierarchy of Managers? http://smallbusiness.chron.com/three-levels-hierarchy-managers-31468.html (16.11.2015).

Moran, Peter (2005). Structural vs. Relational Embeddedness: Social Capital and Managerial. Performance Strategic Management Journal, Vol. 26, No. 12 (Dec.), pp. 1129-1151.

Saunders, M.; Lewis, P. & Thornhill, A. (2003). Research methods for business students (3rd Ed.). Harlow: Pearson Education Limited.

Saunders, M.; Lewis, P. & Thornhill, A. (2009). Research methods for business students (5th Ed.). Harlow: Pearson Education Limited.

Schaeffer, N.C. & Presser, S. (2003). The Science of Asking Questions. Vol. 29, pp. 65-88.

Visser, P.S.; Krosnick, J.A. Marquette, J. & Curtin, M. (1996). Mail surveys for election fore- casting? An evaluation of the Columbus Dispatch poll. Public Opin. Q. 60:181–227.

Molina-Morales Martínez-Fernández (2010). Social Networks: Effects of Social Capital on Firm Innovation. Journal of Small Business Management 2010 48(2), pp. 258–279.

Online Sources

http://www.acitad.org/en/who-are-we-and-activities-from-stat/.

http://www.biznesalbania.org.al/en/?page_id=2.

https://stat.duke.edu/~zo2/dropbox/goflogistic.pdf.

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