The target population of this study was identified through the online database of Proff Forvalt with its NACE-codification, and description of corporate and financial information about Norwegian enterprises categorized into sectors.
Within the sector of manufacturers, only two industries were appropriate, and satisfied the criteria of operating within the food processing industry.
Consequently, the following two were selected; 1) production of food and other enjoyable snacks, and 2) production of beverages.
Next, these two industries were further refined into several sub-industries by Proff Forvalt. Manufacturers behind food categories that are frequently purchased and consumed by Norwegians were chosen (Nielsen 2013), and imported into our database in Excel. This can be viewed as the first estimation of our population (NACE sub-industries, table 3.18). This approach can reduce the impact of
extraneous sampling variation (Malhotra 2010, 384) that could occur by including food categories less frequently purchased and used by Norwegians. More
importantly, it ensured that businesses in the population contained and shared common characteristics that encompassed the universe for the purpose of our study (Malhotra 2010, 370). Further, we merged the extracted sub-industries into a manageable number. Hence, the following sub-industries represented the population; Dairies, eggs, eatable oils and fats / Ice cream / Fish, other seafood and canning / Bread, fresh and preserved pastry, cakes, and biscuits / Sugar, confectionery, cocoa, and chocolate / Meat and poultry products / Potatoes / Juice
from fruits and vegetables / Mineral water, soft drinks and other beverages / Fruit and vegetables / Ready-made food / Wide range of food and snacks (table 3.23).
The initial population consisted of 2829 business units, registered with a unique number as for identification of legal entities in Norway (table 3.18). Due to the law and regulations in Norway, most business units will have two (or more) unique numbers attached to their business, such as departments, units, sub-divisions and so on (Brønnøysundregisteret 2014). Also, another distinct characteristic for many large manufacturers is that businesses or specifically production is conducted at separate and different geographical locations,
consequently registered with separate unit numbers (Brønnøysundregisteret 2014).
Hence, the initial number of business units in the population was reduced to remove duplicates, and to further refine the population to consist of enterprises responsible and in charge for running the overall strategic operations of the firm - mainly the headquarter. After removing 369 business units of these instances, we ended up with a total of 2460 enterprises in our database, and as our population.
However, as we were only interested in enterprises that relied upon products with labels and packaging visible to the end-consumer, these companies had to be verified in terms of these criteria. Therefore, another 1031 enterprises where deleted - including those companies that do not deliver goods in the Norwegian market. The final population consisted of 1429 enterprises.
Table 3.18
3.5.2 Online Questionnaire
To be able to store personal information, or any general information that could possibly be linked to an individual, a permission had to be provided from the NSD - the Data Protection Official for Research in Norway. The survey was distributed after we were granted a permission, after a couple of weeks of evaluation. The questionnaire software of Qualtrics was used due to its high functionality in constructing and customizing online questionnaires. For each main theme of the survey, we created blocks to keep the desired structure constant. However, within each block both variables and its respective items were randomized. This was done as it helps to reduce question order bias (Malhotra 2010). Additionally, we selected the option of force response after each question, consequently we experienced no missing values. An advantage by using online surveys is the ease of transfer, and storage of data that allow for statistical analyzes, and other investigation of responses at any time (Easterby-Smith, Thorpe, and Jackson 2012). Also, from Qualtrics the data was easily transported into SPSS, for
cleaning and codification of items. With regard to the open-ended questions in the survey, i.e. work title, firm age, and industry sector, these were categorized and codified in SPSS - where each category were provided with an individual value and label. The various work titles were categorized in such way that those titles with relatively similar responsibilities and level in the organizational hierarchy were assigned to the same group. The firm age was grouped with intervals, considered to be reasonable age spans. Based on the similarities between the products and categories of the industry sectors, they were merged and reduced into twelve categories. Eventually, before transferring the data to Stata, it had to be transported to Excel as the software requires this type of format.
Important to consider, is that this study investigates the relationship and impact of many constructs, consequently the questionnaire is considered to be fairly long.
This could have been a potential threat to participation, however, this was not a major concern because respondents had prior to the acceptance been informed about the possible time-length (i.e. 20 minutes) to complete the questionnaire.
Additionally, another possible barrier to the response-rate was that the respondents had the opportunity to continue the survey at a later stage if necessary. However, we experienced that the majority of the respondents
contributor to acceptance as it reassured the respondents of the opportunity to tailor completion to a convenient time.
3.5.3 Probability Sampling Technique
Probability sampling was used to eliminate selection bias. From a statistical point of view, probability sampling is preferred as it allows us to make statistical projections and inferences of the results to a target population (Malhotra 2010, 390). Also, it helps to ensure accuracy about the relationship between a sample, and the population from which it is drawn (Easterby-Smith, Thorpe, and Jackson 226, 2012). Probability sampling through the form of simple random sampling helps to secure a representative sample, and that each sample entity (company) has an equal opportunity to participate (Easterby-Smith, Thorpe, and Jackson 226, 2012). As our database had been transported to Excel, we used the randomization function to ensure this.
3.5.4 The Recruitment Process and Response Rate
The data collection phase lasted during the period 13.05 - 18.06. Telephone calls were the primary focus during the first three weeks. The first reminder was sent after one week if the questionnaire was not completed after acceptance (in total three reminders per person). After three weeks with reminders, a last phone-call was made, and sometimes an additional and final reminder was sent. The potential respondents yielded on average 2-3 callbacks as they were occupied in meetings.
The extraction of business information from Proff Forvalt, conveniently provides information about the name of the MD for each enterprise. Occasionally, and particularly for small firms (<10 employees), we experienced a few instances of where direct contact information was provided. Mainly, we searched the web for the direct number of the MD/CEO or visited the company’s website that
sometimes provided us with contact details. When it was impossible to locate contact information directly to the MD/CEO, switchboards or personal assistants provided us with information.
The first contact was always initiated by the phone, as it was important to speak directly with the person to verify whether the person was still in charge, and had the right profile. Also, we wanted to clarify the purpose of the study, ensure confidentiality, and gain oral acceptance. Participants were offered a final report, as a thank-you gesture for participation. After oral acceptance, we collected the email address, and shortly after sent an individual link to the survey, and a cover letter (see appendix 2).
In a few instances, the MD/CEO referred us to the Head of Marketing or Sales.
Also, if several unsuccessful attempts to contact the MD/CEO had been made, we decided to reach the person responsible for the marketing function. In total, we were able to contact 270 companies. Out of these, oral acceptances to participate were given from 208 individuals. The response rate was 47 %, hence out of the 270 companies that were contacted, 126 respondents fully completed the questionnaire.