MKTG 2010
Marketing Research
Major Project
Cathay Cinema
Evelyn
Stella
Rena
Emy
Lecturer: William Lee
Course Coordinator: Alison Dean
Table of Contents
Pages
.0 Introduction
5
.1 Case Background
5
.2 Competitor Analysis
5
2.0 Stage 1: Management Decision Problem Definition
6
2.1 Stage 2: Marketing Research Problem Definition
7
2.2 Stage 3: Marketing Research Objectives
7
2.3 Hypothesis
7
2.3.1 Research Model
7
3.0 Description of sampling technique used
8
3.1 Secondary Data
9
3.1.1 Australia
9
3.1.2 Hong Kong
0
4.0 Data Analysis
1
4.1 Hypothesis 1
1
4.1.1 Justification
5
4.2 Hypothesis 2
6
4.2.1 Justification
9
4.3 Hypothesis 3
21
4.3.1 Justification
23
4.4 Hypothesis 4
24
4.4.1 Justification
28
5.0 Conclusions
29
5.1 Limitations
29
5.2 Recommendations
30
6.0 Bibliography
33
Appendix A
34
Appendix B
34
Appendix C
35
Appendix D
36
Appendix E
37
Appendix F
37
Appendix G
38
Appendix H
38
Appendix I
39
Appendix J
39
Appendix K
40
Appendix L
42
Appendix M
43
Appendix N
45
Appendix O
46
Appendix P
46
Appendix Q
47
Appendix R
48
Appendix S
49
Appendix T
50
Appendix U
50
Appendix V
51
Appendix W
51
Appendix X
54
Appendix Y
55
Appendix Z
57
.0 Introduction
.1 Case Background
The Cathay organisation was started their business in Singapore on 1939. Their first cinema is at Dhoby Ghaut. They have the total of three Cathay cinemas. The Cathay Cinemas provide a waiting lounge for consumers, a larger size of capacity area in theaters; build in toilets in every theater room and the extra personalized services which u order of snacks to be delivery to your seating area when the movie starts (Cathay Oganisation Holdings, 2007). It offers 24 hours of show time only in orchard branch and fifty cents lower prices rate for movie tickets.
.2 Competitor Analysis
Golden Village
Golden Village was started in Singapore on 28 May 1992 with the opening of Yishun 10 cinema complex. It was established to develop and operate modern and luxurious multiplex cinemas. Today, Golden Village has become a cinema with comfort, convenience and has a reputation of offering more choice (Golden Village Multiplex, 2007). We can actually buy Golden Village movie tickets via an integrated telephone system, internet buying facilities and also at the AXS stations located island wide. . The ticketing system automatically selects the best seats available at any one time although patrons may choose their own seats via our Internet booking facility.
It offers various types of special theaters like; Gold Class and cinema Europa mainly with a better comfortable of seating area like sofa and personalized facilities of serving red wine and champagne in it and charges a higher rate of ticket prices with the range of $25 to $35 (Golden Village Multiplex, 2007). In the normal theatres, it had designed that the hand rest at the seating can be raised for the comfort for the couples (Golden Village Multiplex, 2007).
Eng Wah
Eng Wah was founded in the 1940s, it was the first cinema operator listed on the main board of the Stock Exchange of Singapore (SGX). The Group is a leading film exhibitor and distributor with 26 strategically located cinema screen halls with a capacity of over 5,300 seats (Eng Wah Organisation, 2006). In May 2004, Eng Wah was the first cinema operator in Singapore and in the world to launch the world's first full 2K (2000 lines resolution) Digital Cinema in its cineplexes, and is still the operator with the highest number of 2K digital screens in Singapore.
The Group's leisure and lifestyle entertainment businesses include cinema operations, film distribution and food concessionaire operations. The Group also owns and leases several commercial properties in Singapore which include the Jubilee Entertainment Complex at Ang Mo Kio and Toa Payoh Entertainment Centre at Toa Payoh (Eng Wah Organisation, 2006).
Shaw Organisation
The Shaw Organisation was founded in 1924. During the late 1980s, the organisation undertook their biggest project and the Shaw House project with 22 storey complex is located in the heart of the city in 1993. The largest hall, Lido One, has a capacity of over 900. It was the first hall in Singapore to bear certification as a THX hall as well as SDDS, DTS, SRD and Dolby SR. To achieve maximum capacity, the Lido projection booth has the ability to "interlock" one print in more than one hall, enabling a popular film to be seen in several halls (Shaw Organisation, 2007). Within the halls, ergonomic 'rocking' cinema chairs with flip-up arms and cup holders were also introduced. Today, Lido One is the largest digital hall on the island (Shaw Organisation, 2007).
2.0 Stage 1: Management Decision Problem Definition
Your client is the management team of a cinema complex, situated about two km from the CBD of a large city. The cinema is part of a large chain and has access to many resources but it is not cheap. Lately, there has been a serious decline in attendance at the cinema.
2.1 Stage 2: Marketing Research Problem Definition
What features of the cinema complex are most important in creating consumer patronage and increasing consumer loyalty?
2.2 Stage 3: Marketing Research Objectives
- To find out if the quality of facility is an important determinant of consumer attendance at the cinema
- To determine the age group of consumers who watch movies at the cinema
- To determine if price is the most important factor affecting consumer attendance at the cinema
- To determine whether time-pressed consumers go to the cinema
2.3 Hypothesis
Hypothesis 1: Consumers who are particular about the quality of facility will go for movies.
Hypothesis 2: Younger people (15-26) are more likely to go out for movies.
Hypothesis 3: Price-conscious consumers are less likely to go to a movie.
Hypothesis 4: Time-conscious consumers are less likely to go to a movie.
2.3.1 Research model
Dependent variable: Consumer patronage to the cinema
Independent variables: Price consciousness, time consciousness, quality of facility, age
3.0 Description of sampling technique used
Top of Form
Bottom of Form
The team has utilised convenience sampling in this marketing research report. Convenience sampling is a non-probability sampling technique that attempts to obtain a sample of convenient elements (Malhotra and Peterson, 2006). The selection of sampling units is left primarily to the researcher (Malhotra and Peterson, 2006). Respondents are selected because they happen to be in the right place at the right time. The table below shows the advantages and disadvantages of this sampling technique.
Advantages
Disadvantages
Least expensive and time consuming
Sampling units are accessible, easy to measure and co-operative
Selection bias
Not representative
Cannot generalise to a population
3.1 Secondary data
The secondary data below are obtained from various sources to help us gain more insight into the problem of declining cinema attendance in recent years. It depicts the demographics of movie-goers and the factors affecting their choice of particular cinemas.
3.1.1 Australia
According to secondary research, movie-going is still most common among young people in Australia. In 2002, nearly 90 per cent of Australia's 14 to 24-year-olds went to the cinema, an average of 10.2 times each (Australian Film Commission, 2007). Teenagers and 20-somethings are the core of the clientele because more of them go to the pictures, and they go more frequently (Australian Film Commission, 2007).
Frequency of cinema attendance by people aged 15 years and over, 1995, 1999, 2006
93 per cent of 15-17 year olds and 85 per cent of 18-24 year olds went to the movies at least once in the 12 months to June 2006. Attendance rates decline with age, dropping to 45 per cent for people aged 65-74 and 27 per cent for people aged 75 and over (Australian Film Commission, 2007) (See Appendix A).
Among people who had been to the cinema at least once in 2006, 43 per cent were aged under 35 years, 19 per cent between 35 and 44, 29 per cent between 45 and 64, and 9 per cent 65 years and over (Australian Film Commission, 2007) (See Appendix B).
Percentage of male and female cinema-goers in various age groups, 1988-2006
Men tend to outnumber women among younger cinema-goers, particularly 18-24 year olds, and this remained the case in 2006. In the 35-49 age group, the gender split has been even for the past two years, while women tend to outnumber men among cinema-goers over 50.
Combining all age groups, women have outnumbered men among cinema-goers since 1988, accounting for 53 per cent in 2006 (See Appendix C).
According to the 2006 U.S. Theatrical Market Statistics, the vast majority of movie-goers say their overall theatre experience is time and money well spent (Motion Picture Association, 2007) (See Appendix D).
About two-thirds of respondents still feel that the theatre offers the ultimate movie-watching experience. Of the segments, younger males are the most likely to prefer the theatre experience (Motion Picture Association, 2007) (See Appendix E).
Those moviegoers who own or subscribe to four or more home technologies were actually more avid moviegoers, seeing an average of three more movies per year than the moviegoer who owned or subscribed to fewer than four (Motion Picture Association, 2007) (See Appendix F).
3.1.2 Hong Kong
According to the findings of "Survey on Movie-going Habits in Hong Kong" in 2001, those who belonged to the age group of '20-29 years old' were most frequent movie-goers (10.4 times), followed by those belonged to '12-19 years old' (7.5 times). Among them, 66.7% and 58.9% of the respective groups were 'occasional' and 'frequent' movie-goers (See Appendix G).
The majority of those aged 30 years or above was 'infrequent' movie-goers and on average, they went to cinemas 3.9 times or less in 2000 (See Appendix H).
Over half of the respondents (55.0%) were concerned that convenient transport was the factor affecting the choice of particular cinemas. Around one-fifth of them agreed that the 'ticket prices' (22.0%) and the 'facilities nearby' (18.2%) were the factors affecting the choice of particular cinemas (Hong Kong Policy Research Institute Ltd, 2001) (See Appendix I).
4.0 Data Analysis
4.1 Hypothesis 1: Consumers who are particular about the quality of facility will go for movies.
I. Primary Data (UON)
From the data, total there are 478 valid responses with 7 people who cannot answer the question. 14.6% felt that quality of facility is the most important factors when going to a movie, 18.4% felt that quality of facility is the 2nd important factors, 18.8% felt that quality of facility is the 3rd important factors, 17.7% felt that quality of facility is the 4th important factors, 13% felt that quality of facility is 5th important factors and 9.7% & 6.4% respectively, 6th and least important factors that quality of facility when going to a movie (See Appendix J).
The relationship we want to establish will be how important of the quality of facility will affect movie-going. Thus, we will need to test the hypothesis results to see whether important of the quality of facility will affect the movie-going.
Some questions to include are:
"Going out for movies is high quality"
For the chi-square test, under relationship of the movie-going affects the quality of facility hypotheses is set as:
H0: There is no relationship between important of the quality of facility and movie-going.
H1: There is a relationship between important of the quality of facility and movie-going.
The total respondents surveyed showed that for those who watched movie a few times a week has 33.3% felt that the quality of facility is most important. Overall, 14.9% respondents felt that quality of facility is the most important when watching a movie. 18.4% respondents felt that quality of facility is the 2nd important when watching a movie and 6.5% respondents felt that quality of facility is least important. This shows that the most of respondents take the quality of facility as an important factor to go and watch a movie (See Appendix K).
At the 0.05 level of significance with 36 degrees of freedom. From the cross-tabulation given, the calculated chi-square had a value of 45.650 and the p-value is 0.13. Thus, when p>0.05, we will not reject the null hypothesis. To conclude, data do support the claim that there is no relationship between quality of facility when watching a movie (See Appendix K).
For the chi-square test, under relationship of the genders affects high quality of movie hypotheses are set as:
H0: There is no relationship between the high quality and genders.
H1: There is a relationship between high quality and genders.
The total respondents surveyed showed that for those females who watched movie felt that it is high quality 22.7% are strongly agree and 46.5% are agree. Those men who watched movie felt that it is high quality 25.8% are strongly agree and 42.2% are agree. Overall, 24% respondents strongly agree that it is high quality when watching a movie. 44.7% respondents agree that it is high quality when watching a movie and 1% respondents strongly disagree that it is high quality when watching a movie. This shows that the most of respondents that are males associating watching movie as a high quality and overall both gender agree that watching movie is a high quality (See Appendix L).
At the 0.05 level of significance with 12 degrees of freedom. From the cross-tabulation given, the calculated chi-square had a value of 10.783 and the p-value is 0.548. Thus, when p>0.05, we will not reject the null hypothesis. To conclude, data do support the claim that there is no relationship between the high quality and gender (See Appendix L).
From the above analysis, we can conclude that there is no or little affect on the quality of facility to movie going and no or little affect on the gender to high quality.
II. Primary Data (Singapore)
From the data, total there are 33 valid responses. 9.1% felt that quality of facility is the most important factors when going to a movie, 18.2% felt that quality of facility is the 2nd important factors, 15.2% felt that quality of facility is the 3rd important factors, 15.2% felt that quality of facility is the 4th important factors, 21.2% ...
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From the above analysis, we can conclude that there is no or little affect on the quality of facility to movie going and no or little affect on the gender to high quality.
II. Primary Data (Singapore)
From the data, total there are 33 valid responses. 9.1% felt that quality of facility is the most important factors when going to a movie, 18.2% felt that quality of facility is the 2nd important factors, 15.2% felt that quality of facility is the 3rd important factors, 15.2% felt that quality of facility is the 4th important factors, 21.2% felt that quality of facility is 5th important factors and 12.1% & 9.1% respectively, 6th and least important factors that quality of facility when going to a movie (See Appendix M).
The relationship we want to establish will be how important of the quality of facility will affect movie-going. Thus, we will need to test the hypothesis results to see whether important of the quality of facility will affect the movie-going.
For the chi-square test, under relationship of the movie-going affects the quality of facility hypotheses is set as:
H0: There is no relationship between important of the quality of facility and movie-going.
H1: There is a relationship between important of the quality of facility and movie-going.
The total respondents surveyed showed that for those who watched movie once a week has 40% felt that the quality of facility is most important. Overall, 9.1% respondents felt that quality of facility is the most important when watching a movie. 18.2% respondents felt that quality of facility is the 2nd important when watching a movie and 9.1% respondents felt that quality of facility is least important. This shows that the most of respondents take the quality of facility as an essential factor when watching a movie. For instance, for the first 3 ranking, 42.5% take the quality of facility as an important factor (See Appendix M).
At the 0.05 level of significance with 18 degrees of freedom. From the cross-tabulation given, the calculated chi-square had a value of 18.694 and the p-value is 0.411. Thus, when p>0.05, we will not reject the null hypothesis. To conclude, data do support the claim that there is no relationship between the quality of facility and movie-going (See Appendix M).
.
For the chi-square test, under relationship of the genders affects high quality of movie hypotheses are set as:
H0: There is no relationship between the high quality and genders.
H1: There is a relationship between high quality and genders.
The total respondents surveyed showed that for those females who watched movie felt that it is high quality 0% are strongly agree and 42.1% are agree. Those men who watched movie felt that it is high quality 7.1% are strongly agree and 14.3% are agree. Overall, 24% respondents strongly agree that it is high quality when watching a movie. 44.7% respondents agree that it is high quality when watching a movie and 1% respondents strongly disagree that it is high quality when watching a movie. This shows that the most of respondents that are males associating watching movie as a high quality and overall both gender agree that watching movie is a high quality (See Appendix N).
At the 0.05 level of significance with 3 degrees of freedom. From the cross-tabulation given, the calculated chi-square had a value of 5.863 and the p-value is 0.118. Thus, when p>0.05, we will not reject the null hypothesis. To conclude, data do support the claim that there is no relationship between the high quality and gender (See Appendix N).
From the above analysis, we can conclude that there is no or little affect on the quality of facility to movie going and no or little affect on the gender to high quality.
This set of results is also consistent with the set of data provided by UON.
4.1.1 Justification
The data analysis is to investigate the reasons for those consumers who are particular about the quality of facility whether they will go for movies, thus, it will cost a decline in cinema attendance.
In the research done by Hong Kong, it stated that better facilities are still major reasons for going to cinema theater (Hong Kong Policy Research Institute Ltd, 2001). This can showed that better seating, good quality sound system and larger screens do can increase the attractiveness of going to cinema (Hong Kong Policy Research Institute Ltd, 2001).
People who are particular about the quality of facility may prefer watching a movie in cinema as they are able to feel a different atmosphere through the sound system and larger screens.
4.2 Hypothesis 2: Younger people (15-26) are more likely to go out for movies.
I. Primary Data (UoN)
We want to establish a relationship between age and going to movie. To test this hypothesis, we ask our respondents to tell us their age when filling in the survey.
The objective is to make the management being able to understand the problem of declining attendance in its cinema and address the problem effectively.
For the chi-square test, refer to the following hypotheses:
H0: There is no relationship between movie-going and age.
H1: There is a relationship between movie-going and age.
Out of the 500 respondents surveyed, we decide to divide it into two age groups. Group 1 is from age 12-42 years old. Group 2 is from 43-73 years old. 100% who go to movie daily are from group 1. 100% stated that they go to movie a few times a week are also from group 1. In addition, 94.8% who go to movie fortnightly are aged between 12-42 years old. Overall, 88.3% who go to movie are aged below 42 years old and 11.7% are aged above 42 years old. This shows that younger people (<42 years old) go to movie more frequently than older people (See Appendix O).
Since p value of 0.715 is greater than the level of significance of 0.05. Hence, the null hypothesis is not rejected. In this case, we say that there is insufficient evidence from the results to conclude there is a relationship between going to a movie (dependent variable) and the age of the movie-goers (independent variable) (See Appendix O).
Correlation
Correlation coefficient indicates the direction of relationship. Since the correlations coefficient is negative, which is -0.169, the relationship is opposite: as one variable increases, the other variable decreases. In this case, it means that the smaller the age, the more often they go to movie and the larger the age, the less often they go to movie (See Appendix P).
From the result analysis above, we can assume that younger people are more likely to go to movie. Secondary research showed that young people like to go to movie as it is the only indoor recreation for them. They go to be entertained and they choose the types of movie. Moreover, they go to movie to widen their social network friends (Cuban, 2006). Older people are less likely go to movie as they are lack of time, cost and caregiver responsibilities (Cinema, 2007). Furthermore, based on research done by Pew Research Center (2006), older people are too busy, a lack of good movies, and cost. They often feel inconvenience of show times, there are hassles of driving and parking, and it is easier for them ro wait for DVD and watch at home. It also stated that movie goers are more likely to be young, college educated, and more affluent.
According to IAMAI, 30% of movie goers are aged between 18-25 and 56% of those are aged between 26-35. Only 6% of movie goers who are aged between 46-60.
On the other hand, based on the research by Hong Kong Policy Research Institute Ltd (2001), 66.7% who are aged between 20-29 years old go to movie frequently. However, it was found that the majority of those aged 3- or above were infrequent movie-goers.
II. Primary Data (Singapore)
Our group did the same questionnaire on the first week of November and received a total of 33 responses. The results generated are treated as primary data.
According to the survey, the majority of respondents (60.6%) go to a movie once a month or less. 21.2% go to a movie fortnightly. This trend is similar as that observed in the survey results obtained from UoN. However, we can see that an even lower proportion of respondents (3.0%) go to a movie a few times a week and none daily and never (See Appendix Q).
The age of our respondents are varying from 15 to 26 years old. The highest proportion, which is 30.3% are aged 20 years old.
By having these data, we want to analyse the relationship between going movie habits and age of the movie goers.
Out of the 33 responses that we got, 30% and 20% who go to movie once a month or less are 20 years old and 18-19 years old respectively. Furthermore, 42.9% who go to movie fortnightly are 20 years old, and 28.8% of those are 22 years old. People who are 18 years old and 21 years old are 80% who go to movie once a week. Overall, highest proportion (30.3%) of movie goers are aged 20 years old, followed by 18.2% who are 18 years old, and 18.2% who are 19 years old. Due to the lack of age variety of respondents, we are still unable to come to a conclusion from this set of results (See Appendix Q).
For the chi-square test, refer to the following hypotheses:
H0: There is no relationship between movie-going and age.
H1: There is a relationship between movie-going and age.
At the 0.05 level of significance with 27 degree of freedom, the critical value of the chi-square statistic is 40.1133. From the cross-tabulation given, the calculated chi-square had a value of 30.329. Since this is less than the critical value of 40.1133, the null hypothesis of no association cannot be rejected, indicating that the association is not statistically significant at 5% significance level. In this case, we say that there is insufficient evidence from the results to conclude there is a relationship between going to a movie (dependent variable) and the age (independent variable) (See Appendix Q).
In other word, since p value of 0.300 is greater than the level of significance of 0.05 (p=0.300>0.05). Hence, we cannot reject the null hypothesis. In this case, we say that there is insufficient evidence from the results to conclude there is a relationship between the two variables. This set of results is consistent to the one conducted by UoN.
Correlation
Correlation coefficient indicates the direction of relationship. Since the correlations coefficient is positive, which is 0.012, the relationship is increasing: larger quantities of one variables are associated with larger quantities of another variable. In this case, it means that the larger the age, the more often they go to movie and the smaller the age, the less often they go to movie. This set of results is opposed to the one conducted by UoN. This could be due to the difference in demographics and psychographics of the respective samples. Moreover, the data obtained did not include the older age (>30 years old).
4.2.1 Justification
Our objective of doing these analyses is to find the reasons for older people not going to cinema which leading to decline in cinema attendance.
First of all, older people (above 30 years old), always go to cinema with their family members. They stated that they seldom go to cinema because each family members has different taste, and not many movies were suitable for the whole family except the ones produced for family viewers during the holiday seasons. Moreover, there is very few movies were suitable for small children or old people (Hong Kong Policy Research Institute Ltd, 2001).
Furthermore, going for movie with the whole family is rather expensive, because they need to buy many tickets. Therefore, they choose to watch movie at home using DVD or Videodiscs. It is more satisfactory and more convenient for them.
Moreover, we also want to know the reason of declining in cinema attendance, although it has been found that younger people are more likely to go to cinema.
Basically, from years ago, young people go for movie is to have fun with their friends. Since they still need to socialize, they use 'movie going habits' as their tools to broaden their network. Besides, they need to keep updating to maintain their relationship with their peers. Movie going also became their favorite entertainment to release their stress and hang out with friends.
However, this trend has declined which is showed there is a decline in cinema attendant. As the technology has been developed so well nowadays, people can watch anywhere and anytime. Most of young people are aware of this development and they do not need to go to cinema to watch movie or just to keep updating their knowledge of movie trend. Since their income level is not so high, they are more likely to buy DVD and watch at home, which is more affordable. Furthermore, by using Internet, there is free movie available, thus young people just need to download it. They also feel that the viewing time is inflexible as most of this group (<30 years old) are still studying or working (Hong Kong Policy Research Institute Ltd, 2001).
4.3 Hypothesis 3: Price-conscious consumers are less likely to go to a movie.
I. Primary Data (UON)
From the table, we can see that out of 500 respondents surveyed, 41.7% and 14.6% who agree and strongly agree respectively that they are never go to a movie because they are careful about money. The total result shows that 41.1% of respondents agreed that they are price-conscious customer followed by 15.5% strongly agree and there is only a small proportion of 4.5% strongly disagree. From the large differences of the percentage, we can conclude that most of the respondents have taken care of their money just for a movie. Because most of them are price-conscious, so there is 45.2% of them only go for movie once a month or less (See Appendix S).
For the chi-square test, refer to the following hypotheses:
H0: There is no relationship between movie-going and price.
H1: There is a relationship between movie-going and price.
Since p value is 0.001 is smaller than the level of significance of 0.05. Hence, the null hypothesis is rejected. In this case, we can conclude that there is a relationship between going to a movie and price of the movie ticket (See Appendix S).
Correlation
Correlation coefficient indicates the direction of relationship. Since the correlations coefficient is negative, which is -0.029, the relationship is opposite: as one variable increases, the other variable decreases. In this case, it means that the cheaper the movie ticket, the more often people going go to movie and the more expensive the movie ticket, the less often people going to the movie.
II. Primary Data (Singapore)
Our group did the same questionnaire on the first week of November and received a total of 33 responses. The results generated are treated as primary data.
According to the survey, the majority of respondents (60.6%) go to a movie once a month or less. 21.2% go to a movie fortnightly. This trend is similar as that observed in the survey results obtained from UoN. However, we can see that an even lower proportion of respondents (3.0%) go to a movie a few times a week and none daily and never.
From the table, out of 33 respondents we gather from the survey, 100% strongly agree that they are careful about money and they only go to a movie once a month or even less. However, there is a same number of 66.7% people are disagree and neutral about money when they go to a movie for once a month or less. This shows that there is a lack of number of people doing the survey and result in insufficient conclusion of the data, so we are still unable to come to a conclusion from this set of results.
For the chi-square test, refer to the following hypotheses:
H0: There is no relationship between movie-going and price.
H1: There is a relationship between movie-going and price.
Since p value of 0.839 is greater than the level of significance of 0.05 where p = 0.839 > 0.05 therefore, we cannot reject the null hypothesis. In this case, we can conclude that there is insufficient evidence to support the relationship between the two variables. This set of results is opposed to the one conducted by UoN.
Correlation
Correlation coefficient indicates the direction of relationship. Since the correlations coefficient is negative, which is -0.015, the relationship is opposite: as one variable increases, the other variable decreases. In this case, it means that the cheaper the movie ticket, the more often people going go to movie and the more expensive the movie ticket is, the less often people going to the movie. This set of results is consistent to the one conducted by UoN.
4.3.1 Justification
Our objective of doing these analyses is to find out the reason for price-conscious people not going to the cinema which lead to decline in the cinema attendance.
Price-conscious people are less likely to go for movie because they prefer to stay at home for movie with their family with just paying or rent for one price of the DVD and going for movie with the whole family is rather expensive, because they need to buy many tickets. Therefore, they choose to watch movie at home using DVD or Videodiscs. It is more satisfactory and more convenient for them.
And also due the price of the increasing movie ticket, more students are less likely to visit the cinema as they have limited pocket money to purchase movie ticket. Some of them will have the mind set of going to the cinema beside spending money for the movie ticket, they must also buy snacks which will cost them even more (David Waterman, 2007).
With the technology today, people can be easily download movie at home without paying a single cents and it is more comfortable to watch with whole group of friends and even families members.
4.4 Hypothesis 4: Time-conscious consumers are less likely to go to a movie.
I. Primary Data (UoN)
According to the data, the majority of respondents (61.8%) go to a movie once a month or less. 16.9% go to a movie fortnightly and 9.9% never go to a movie. An even lower proportion of respondents, 1.9% and 0.4% respectively, go to a movie a few times a week and daily (Appendix W).
We want to establish a relationship between movie-going habits and time-consciousness. Therefore, to test this hypothesis, we will take into account the responses to the following questions:
"How often do you go to the movies",
"Going out for movies requires too much time",
"I am always pressed for time"
By associating these two variables, the management is able to better understand the problem of declining attendance in its cinema. This may help them to address the problem effectively and come up with workable solutions.
For the chi-square test, refer to the following hypotheses:
H0: There is no relationship between movie-going and time-consciousness.
H1: There is a relationship between movie-going and time-consciousness.
Out of the 500 respondents surveyed, 29.2% and 25.0% who agreed and strongly agreed respectively that they are always pressed for time never go to the movie. Overall, the highest proportion (29.6%) of respondents agreed that they are always pressed for time followed by 18.2% strongly agreed, 18.8% disagreed and 4.8% (smallest proportion) strongly disagreed. This shows to a large extent that most of the respondents have a tight and hectic schedule and that they may be time-conscious. 32.4% of those who go to a movie once a month or less are neutral when questioned if they are always pressed for time. This group of respondents may find that time is not a crucial factor for them (See Appendix W).
At the 0.05 level of significance with 30 degrees of freedom, the critical value of the chi-square statistic is 43.773. From the cross-tabulation given, the calculated chi-square had a value of 46.760. Since this is more than the critical value of 43.773, the null hypothesis of no association can be rejected, indicating that the association is statistically significant at 5% significance level. In this case, we say that there is sufficient evidence from the results to conclude there is a relationship between going to a movie (dependent variable) and the extent to which 'I am always pressed for time' (independent variable).
Or we can say, since p= 0.026<0.05, we reject the null hypothesis. Thus there is sufficient evidence to conclude that there is a relationship between these 2 variables.
Out of the 500 respondents surveyed, 15.3% and 4.3% who agreed and strongly agreed respectively that going to a movie requires too much time never go to the movie. Overall, the highest proportion (41.9%) of respondents disagreed that going to a movie requires too much time followed by 21.5% strongly disagreed, 11.3% agreed and 3.8% (smallest proportion) strongly agreed. This set of results tells a different story as that above. This could be attributable to the accessibility of cinemas. There are so many cinemas scattered in all parts of the city that one does not have to travel too far to go catch a movie. This is a good sign for the cinema management as it shows that although most people are time-conscious, they do not dismiss movie-going as a time-consuming and total-waste-of-time leisure activity (See Appendix X).
From the result analysis above, we can deduce to a certain extent that time-conscious or time-pressed people are less likely to go to a movie. Secondary research has indicated that the increase in prevalence of alternative forms of entertainment such as online and DVD viewing may have deterred time-conscious people from going to a movie at the cinema. They can enjoy the same movie at the luxury of their own homes and sometimes for a lower cost. Inevitably, the hectic lifestyles of consumers today have more or less led to a shift in movie-going habits.
According to an AP-AOL Poll, 7% of adults said that they prefer watching movies at home on DVD and VOD over going to the theatre. Just 22% said they would rather see films in a theatre, according to the poll conducted by Ipsos for The Associated Press and AOL News. One-fourth said they had not been to a movie theatre in the past year.
At the 0.05 level of significance with 24 degrees of freedom, the critical value of the chi-square statistic is 36.415. From the cross-tabulation given, the calculated chi-square had a value of 28.517. Since this is less than the critical value of 36.415, the null hypothesis of no association cannot be rejected, indicating that the association is not statistically significant at 5% significance level. In this case, we say that there is insufficient evidence from the results to conclude there is a relationship between going to a movie (dependent variable) and the extent to which 'the activity requires too much time' (independent variable).
Or we can say, since p= 0.239>0.05, we do not reject the null hypothesis. Thus there is insufficient evidence to conclude that there is a relationship between these 2 variables.
II. Primary data (Singapore)
Our group did the same questionnaire on the first week of November and received a total of 33 responses. The results generated are treated as primary data.
According to the survey, the majority of respondents (60.6%) go to a movie once a month or less. 21.2% go to a movie fortnightly. This trend is similar as that observed in the survey results obtained from UoN. However, we can see that an even lower proportion of respondents (3.0%) go to a movie a few times a week and none daily and never. Could this be due to the time factor? The following analysis attempts to show if time-conscious people are less likely to go to a movie (See Appendix Y).
Out of the 33 responses we obtained, 30.0% who go to a movie once a month or less agreed they are always pressed for time. Overall, the highest proportion (45.5%) of respondents are neutral about the question on time followed by 27.3% agreed, 9.1% strongly agreed, 18.2% disagreed and none strongly disagreed. Due to ambiguity (high proportion of sample chose 'neutral'), we are still unable to come to a conclusion from this set of results alone (See Appendix Y).
At the 0.05 level of significance with 9 degrees of freedom, the critical value of the chi-square statistic is 16.919. From the cross-tabulation given, the calculated chi-square had a value of 6.514. Since this is less than the critical value of 16.919, the null hypothesis of no association cannot be rejected, indicating that the association is not statistically significant at 5% significance level. In this case, we say that there is insufficient evidence from the results to conclude there is a relationship between going to a movie (dependent variable) and the extent to which 'I am always pressed for time' (independent variable). This set of results is antagonistic to the one conducted by UoN. This could be due to the difference in demographics and psychographics of the respective samples.
5.0% of respondents who go to a movie once a month agreed that it requires too much time. Similarly, the same proportion of respondents who go to a movie once a month strongly disagreed the activity requires too much time. Overall, the highest proportion (42.4%) of respondents disagreed that going to a movie requires too much time followed by 18.2% strongly disagreed, 9.1% agreed and 3.0% (smallest proportion) strongly agreed. This set of results is consistent with that of UoN as above (See Appendix Z).
At the 0.05 level of significance with 12 degrees of freedom, the critical value of the chi-square statistic is 21.026. From the cross-tabulation given, the calculated chi-square had a value of 10.605. Since this is less than the critical value of 21.026, the null hypothesis of no association cannot be rejected, indicating that the association is not statistically significant at 5% significance level. In this case, we say that there is insufficient evidence from the results to conclude there is a relationship between going to a movie (dependent variable) and the extent to which 'the activity requires too much time' (independent variable). This set of results is consistent to the one conducted by UoN (See Appendix Z).
4.4.1 Justification
We seek to investigate the reasons for time-conscious people not going to the cinema, thereby leading to a decline in cinema attendance.
First, it has to be noted that many cinemas have been converted from the single grand theatre style to multiple mini-theatre style (Hong Kong Policy Research Institute Ltd, 2001). This implies that the screen size of these mini-theatres is reduced considerably. On the other hand, the home theatre systems, speakers, huge plasma screens are becoming increasingly sophisticated and readily available (Hong Kong Policy Research Institute Ltd, 2001).
In addition, most movie titles are widely available in DVD soon after the movies have been screened in cinema. This is one of the major factors contributing to the decline in cinema attendance as time-conscious and time-pressed people tend to watch movies in DVD at home.
Also, the prevalence of web movies is another factor for the decline in cinema attendance. It has become so common and easy to access that one does not even have to be IT-savvy to learn how to watch movies online. Although the technical performance of the web movies could not impress the audience, it could nevertheless affect the movie industry when it gradually became popular with the improvement in technology in the foreseeable future (Hong Kong Policy Research Institute Ltd, 2001).
Time-conscious people may prefer the flexible viewing time and the free environment at home. Given their hectic lifestyles, watching the movie at home is more than satisfactory and definitely much more convenient than having to travel to the nearest cinema to catch the same movie.
5.0 Conclusions
5.1 Limitations
The sample may not be representative of the population of interest. This is because the second set of primary data obtained from our group by conducting the same survey locally was very small-scale with only 33 respondents and the results reflected this lack of sample size. A larger survey may well establish a relationship which cannot be deduced from the small survey conducted.
Additionally, this case background is set in a local context. However, the survey results provided to us is in the Australian context. Hence, the results may not accurately reflect the situation in Singapore. The sample may not be representative of the population of interest. This is because the second set of primary data, which is obtained by conducting the same survey locally was in a small scale of 33 respondents. These results reflected the lack of sample size. Furthermore, our second set of primary data is lack of variety age of respondents (young people only), which limit us to draw the conclusion effectively.
A wider spread survey may be conducted to get more variety respondents thus the relationship may better established.
The results are biased since there is difference in culture and personalities between Australian and Singaporean. There may be difference in hobby, habits, and attitudes.
Hence, in terms of accuracy of results, primary data 1 is more accurate than primary data 2 due to a larger sample size. However, in terms of usefulness and validity, the latter is more useful and valid in this case as the set of results obtained is more reflective of our society.
Many sources of selection bias are present.
5.2 Recommendations
The overall aim is to increase customer patronage to the cinema by making cinema-going an important part of people's lives. Below are some of the recommendations.
Pricing
- To offer special discount for group tickets in order to attract a group of friends or family members to watch movies in cinema.
- To offer free food and drink coupons, such as popcorn or soft drink for those who purchase more than 4 tickets.
- To offer seniors cheaper tickets during the day, for example, so as to keep them away from busy evening sessions.
Cinema infrastructure
- To improve the design of seating (e.g. making it more spacious and comfortable).
- To consider providing separate rooms for groups of movie-watchers similar to the
karaoke lounges.
- To provide best sound system that give movie-watchers a never before sound entertainment
Customer service
- To be more polite to customers, especially to students, and
- To improve the queuing problems by having more counters and placing online booking booths near the cinema.
Old Generation
- Offer movie that has family themes, this can attract older generation customers to watch movie in cinema.
- Older generation will less likely to watch action movie that portray violation.
- Offer family discount ticket package that are intended for family show, such as: 2 + 1 packet (parent and 1 child), 2 + 2 packet (parent and 2 children), etc.
- Offers seasonal tickets during school holiday or public holiday, because most of the older generation would prefer to spend time with family rather than going alone.
- Provide better quality and more comfortable ambience.
- Provide easily understandable movie for old people and small kids.
Youth and young generation
- Offer special price for students, or specific card holders to get discount when they present it during purchasing ticket, such as Buy 2 get 1 free.
- Promote cinema club card, which can be used to get discount and other privilege in the cinema.
- Set a specific day of the week that the movie ticket can be purchased in a lower price than other week day (to boost sales during non-peak period).
- Offer movie ticket voucher for those who want to buy gift for their friends.
- Customers can get cheaper price when they purchase in a big quantity
- Organize event for movie card holder and provide prizes, such as free movie ticket or others discount when they watch movie in the cinema
- Organize event in the special occasion, such as Valentine day, Christmas, New Year, Lunar New Year, etc.
- Provide unique environment and ambience as they are experiencers (seek new experience)
Promotions
- To organise some film clubs to encourage movie-going habits.
- To mail film catalogues to those who sign up as cinema members once every month to update them on upcoming movies.
- To provide loyalty discounts and build stronger relationships with schools and community groups.
The environment
- Provide better quality and more comfortable seat place in the cinema
- Create a friendly environment so people are more likely to visit again without thinking of the money they spend
6.0 Bibliography
Cinema, 2007, Statistics, viewed 18 November 2007, <http://www.stats.govt.nz/NR/exeres/C49391D6-A990-4E1A-BF96-41F349E0422A.htm>
Cuban, M., 2006, The Movie Business Challenge, viewed 18 November 2007, <http://www.blogmaverick.com/2006/07/23/the-movie-business-challenge/>
David W, 2007, Hollywood's Road to Riches, viewed 20 November 2007, < http://books.google.com/books?id=66vKyRDH98sC&pg=PA59&lpg=PA59&dq=%22price+conscious%22+movie&source=web&ots=SMBl1zS2mX&sig=efud8SeU6KC3TM65FE6gqqDgAZk#PPA296,M1>.
IAMAI, 2006, Movie Tickets, viewed 18 November 2007, <http://www.iamai.in/IAMAI_mtickets.html>
Malhotra N, Peterson M, 2006, Basic Marketing Research: A Decision-Making Approach, 2nd edn, Pearson Prentice Hall, New Jersey
Pew Research Center, 2006, Increasingly, American Prefer Going to the Movies At Home, viewed 18 November 2007, <http://pewresearch.org/assets/social/pdf/Movies.pdf>
The Survey on Movie-going Habits in Hong Kong, 2001, Hong Kong Policy Research Institute Ltd, viewed 22/10/07, <http://www.tela.gov.hk/english/doc/forms/whatsnew/fullreport.pdf>
U.S. Theatrical Market Statistics, 2006, Motion Picture Association, viewed 23/10/07, <http://www.mpaa.org/2006-US-Theatrical-Market-Statistics-Report.pdf>
What Australians Are Watching: Cinema Industry, 2006, Australian Film Commission, viewed 19/10/07,
<http://www.afc.gov.au/GTP/wcessaytrends.html>
Appendix A
No. of cinema visits in past year
9951
9992
20063
No. of cinema-goers ('000)
Share of cinema-goers (%)
No. of cinema-goers ('000)
Share of cinema-goers (%)
No. of cinema-goers ('000)
Share of cinema-goers (%)
-5 times
4,737.6
54.2%
5,073.5
50.8%
5,657.0
54.2%
6-20 times
3,344.2
38.3%
4,049.5
40.5%
4,055.7
38.9%
21 times or more
651.9
7.5%
846.6
8.5%
718.6
6.9%
Total attending at least once
8,733.8
00%
9,987.6
00%
0,431.4
00%
Snapshot 2006
Appendix B
Source: Australian Bureau of Statistics (ABS), Attendance at Selected Cultural Venues and Events (cat no 4114.0).
Notes:
. 12 months to March 1995.
2. 12 months to April 1999.
3. 12 months to June 2006.
4. Cinema-goers as a percentage of the Australian population aged 15 years and over.
5. In 1995 and 1999, the oldest age category was 65+. The number of persons and attendance rate in the 65-74 category for both years reflects all people aged over 65.
Appendix C
Proportion of cinema-goers (%)
4-17 years
8-24 years
25-34 years
35-49 years
50+ years
Women
Men
Women
Men
Women
Men
Women
Men
Women
Men
988
7%
5%
26%
30%
20%
25%
9%
8%
8%
2%
992
2%
4%
25%
31%
21%
22%
20%
9%
22%
3%
993
9%
1%
26%
28%
22%
25%
24%
21%
9%
5%
994
2%
3%
23%
24%
20%
22%
23%
22%
22%
9%
995
2%
2%
23%
26%
9%
24%
23%
21%
23%
6%
996
1%
2%
22%
22%
20%
26%
26%
23%
21%
7%
997
2%
4%
21%
22%
21%
25%
24%
22%
22%
7%
998
2%
2%
21%
22%
20%
25%
24%
22%
23%
9%
999
0%
3%
23%
24%
20%
24%
24%
21%
23%
8%
2000
1%
1%
22%
24%
20%
26%
23%
22%
24%
7%
2001
9%
0%
5%
7%
20%
22%
29%
28%
27%
23%
2002
9%
0%
20%
23%
20%
24%
24%
21%
27%
22%
2003
9%
0%
7%
9%
20%
23%
27%
26%
27%
22%
2004
9%
0%
4%
6%
9%
20%
28%
27%
31%
26%
2005
1%
1%
9%
22%
9%
21%
24%
24%
29%
22%
2006
9%
9%
4%
6%
9%
20%
27%
27%
31%
27%
Snapshot 2006:
Age profile of female cinema-goers compared to male cinema-goers, 2006
Source: Val Morgan & Co (Aust) Pty Ltd/Roy Morgan Research Centre, (c) Val Morgan & Co (Aust) Pty Ltd. Data may only be reproduced with permission.
Notes:
'Cinema-goers' means people who had been to the cinema at least once in the last 12 months.
Appendix D
Appendix E
Appendix F
Appendix G
Figure 1a: Average Number of Times of Movie-going in 2000 by Age Groups
Appendix H
Figure 1b: Frequency Types of Movie-going in 2000 by Age Groups
Appendix I
Figure 1c: Factors Affecting the Choice of Particular Cinemas
Appendix J
Rank Importance: Quality of facility
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
31
6.4
6.5
6.5
2
47
9.7
9.8
6.3
3
63
3.0
3.2
29.5
4
86
7.7
8.0
47.5
5
91
8.8
9.0
66.5
6
89
8.4
8.6
85.1
7
71
4.6
4.9
00.0
Total
478
98.6
00.0
Missing
99
7
.4
Total
485
00.0
Appendix K
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * Rank Importance: Quality of facility
477
98.4%
8
.6%
485
00.0%
Go to a movie * Rank Importance: Quality of facility Crosstabulation
Rank Importance: Quality of facility
Total
2
3
4
5
6
7
Go to a movie
Never
Count
2
5
7
3
4
3
2
46
Expected Count
3.0
4.5
6.1
8.3
8.8
8.5
6.8
46.0
% within Go to a movie
4.3%
0.9%
5.2%
28.3%
8.7%
28.3%
4.3%
00.0%
% of Total
.4%
.0%
.5%
2.7%
.8%
2.7%
.4%
9.6%
Once a month or less
Count
8
27
37
50
59
57
46
294
Expected Count
9.1
29.0
38.8
53.0
56.1
54.2
43.8
294.0
% within Go to a movie
6.1%
9.2%
2.6%
7.0%
20.1%
9.4%
5.6%
00.0%
% of Total
3.8%
5.7%
7.8%
0.5%
2.4%
1.9%
9.6%
61.6%
Fortnighly
Count
5
0
2
5
8
9
3
82
Expected Count
5.3
8.1
0.8
4.8
5.6
5.1
2.2
82.0
% within Go to a movie
6.1%
2.2%
4.6%
8.3%
22.0%
1.0%
5.9%
00.0%
% of Total
.0%
2.1%
2.5%
3.1%
3.8%
.9%
2.7%
7.2%
Once a week
Count
5
4
6
6
7
8
7
43
Expected Count
2.8
4.2
5.7
7.8
8.2
7.9
6.4
43.0
% within Go to a movie
1.6%
9.3%
4.0%
4.0%
6.3%
8.6%
6.3%
00.0%
% of Total
.0%
.8%
.3%
.3%
.5%
.7%
.5%
9.0%
A few times a week
Count
0
0
0
2
3
3
9
Expected Count
.6
.9
.2
.6
.7
.7
.3
9.0
% within Go to a movie
.0%
.0%
.0%
22.2%
33.3%
1.1%
33.3%
00.0%
% of Total
.0%
.0%
.0%
.4%
.6%
.2%
.6%
.9%
Daily
Count
0
0
0
0
0
2
Expected Count
.1
.2
.3
.4
.4
.4
.3
2.0
% within Go to a movie
.0%
50.0%
50.0%
.0%
.0%
.0%
.0%
00.0%
% of Total
.0%
.2%
.2%
.0%
.0%
.0%
.0%
.4%
2
Count
0
0
0
0
0
0
Expected Count
.1
.1
.1
.2
.2
.2
.1
.0
% within Go to a movie
00.0%
.0%
.0%
.0%
.0%
.0%
.0%
00.0%
% of Total
.2%
.0%
.0%
.0%
.0%
.0%
.0%
.2%
Total
Count
31
47
63
86
91
88
71
477
Expected Count
31.0
47.0
63.0
86.0
91.0
88.0
71.0
477.0
% within Go to a movie
6.5%
9.9%
3.2%
8.0%
9.1%
8.4%
4.9%
00.0%
% of Total
6.5%
9.9%
3.2%
8.0%
9.1%
8.4%
4.9%
00.0%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
45.650(a)
36
.130
Likelihood Ratio
39.149
36
.330
Linear-by-Linear Association
.880
.348
N of Valid Cases
477
a 25 cells (51.0%) have expected count less than 5. The minimum expected count is .06.
Appendix L
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Gender * The activity 'is high quality'
483
99.6%
2
.4%
485
00.0%
Gender * The activity 'is high quality' Crosstabulation
The activity 'is high quality'
Total
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
6
42
Gender
Female
Count
4
6
58
19
58
0
256
Expected Count
2.7
2.7
63.1
14.5
61.5
.1
.5
256.0
% within Gender
.6%
6.3%
22.7%
46.5%
22.7%
.0%
.4%
00.0%
% of Total
.8%
3.3%
2.0%
24.6%
2.0%
.0%
.2%
53.0%
Male
Count
8
61
95
58
2
0
225
Expected Count
2.3
1.2
55.4
00.6
54.0
.9
.5
225.0
% within Gender
.4%
3.6%
27.1%
42.2%
25.8%
.9%
.0%
00.0%
% of Total
.2%
.7%
2.6%
9.7%
2.0%
.4%
.0%
46.6%
2
Count
0
0
0
2
0
0
0
2
Expected Count
.0
.1
.5
.9
.5
.0
.0
2.0
% within Gender
.0%
.0%
.0%
00.0%
.0%
.0%
.0%
00.0%
% of Total
.0%
.0%
.0%
.4%
.0%
.0%
.0%
.4%
Total
Count
5
24
19
216
16
2
483
Expected Count
5.0
24.0
19.0
216.0
16.0
2.0
.0
483.0
% within Gender
.0%
5.0%
24.6%
44.7%
24.0%
.4%
.2%
00.0%
% of Total
.0%
5.0%
24.6%
44.7%
24.0%
.4%
.2%
00.0%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
0.783(a)
2
.548
Likelihood Ratio
2.808
2
.383
Linear-by-Linear Association
.093
.760
N of Valid Cases
483
a 13 cells (61.9%) have expected count less than 5. The minimum expected count is .00.
Appendix M
Rank Importance: Quality of facility
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
3
9.1
9.1
9.1
2
4
2.1
2.1
21.2
3
7
21.2
21.2
42.4
4
5
5.2
5.2
57.6
5
5
5.2
5.2
72.7
6
6
8.2
8.2
90.9
7
3
9.1
9.1
00.0
Total
33
00.0
00.0
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * Rank Importance: Quality of facility
33
00.0%
0
.0%
33
00.0%
Go to a movie * Rank Importance: Quality of facility Crosstabulation
Rank Importance: Quality of facility
Total
2
3
4
5
6
7
Go to a movie
Once a month or less
Count
3
5
5
3
3
0
20
Expected Count
.8
2.4
4.2
3.0
3.0
3.6
.8
20.0
% within Go to a movie
5.0%
5.0%
25.0%
25.0%
5.0%
5.0%
.0%
00.0%
Fortnighly
Count
0
2
7
Expected Count
.6
.8
.5
.1
.1
.3
.6
7.0
% within Go to a movie
4.3%
4.3%
4.3%
.0%
4.3%
28.6%
4.3%
00.0%
Once a week
Count
0
0
0
2
5
Expected Count
.5
.6
.1
.8
.8
.9
.5
5.0
% within Go to a movie
20.0%
.0%
20.0%
.0%
20.0%
.0%
40.0%
00.0%
A few times a week
Count
0
0
0
0
0
0
Expected Count
.1
.1
.2
.2
.2
.2
.1
.0
% within Go to a movie
.0%
.0%
.0%
.0%
.0%
00.0%
.0%
00.0%
Total
Count
3
4
7
5
5
6
3
33
Expected Count
3.0
4.0
7.0
5.0
5.0
6.0
3.0
33.0
% within Go to a movie
9.1%
2.1%
21.2%
5.2%
5.2%
8.2%
9.1%
00.0%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
8.694(a)
8
.411
Likelihood Ratio
9.906
8
.338
Linear-by-Linear Association
2.070
.150
N of Valid Cases
33
a 28 cells (100.0%) have expected count less than 5. The minimum expected count is .09.
Appendix N
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Gender * is high quality
33
00.0%
0
.0%
33
00.0%
Gender * is high quality Crosstabulation
is high quality
Total
disagree
neutral
agree
Strongly agree
Gender
Male
Count
5
6
2
4
Expected Count
3.0
6.4
4.2
.4
4.0
% within Gender
35.7%
42.9%
4.3%
7.1%
00.0%
Female
Count
2
9
8
0
9
Expected Count
4.0
8.6
5.8
.6
9.0
% within Gender
0.5%
47.4%
42.1%
.0%
00.0%
Total
Count
7
5
0
33
Expected Count
7.0
5.0
0.0
.0
33.0
% within Gender
21.2%
45.5%
30.3%
3.0%
00.0%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
5.863(a)
3
.118
Likelihood Ratio
6.413
3
.093
Linear-by-Linear Association
.911
.167
N of Valid Cases
33
a 5 cells (62.5%) have expected count less than 5. The minimum expected count is .42.
Appendix O
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * Age
484
99.8%
.2%
485
00.0%
Age
Total
<42
>42
Go to a movie
Never
Count
% Within Go to a movie
33
68.75%
5
31.25%
48
00%
Once a month or less
Count
% Within Go to a movie
263
87.9%
36
2.1%
299
00%
Fortnightly
Count
% Within Go to a movie
78
94.8%
4
5.2%
82
00%
Once a week
Count
% Within Go to a movie
41
95.3%
2
4.7%
43
00%
A few times a week
Count
% Within Go to a movie
9
00%
0
0%
9
00%
Daily
Count
% Within Go to a movie
2
00%
0
0%
2
00%
2
Count
% Within Go to a movie
2100%
0
0%
00%
Total
Count
% Within Go to a movie
428
88.4%
56
1.6%
484
00%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
262.190(a)
276
.715
Likelihood Ratio
91.198
276
.000
Linear-by-Linear Association
3.743
.000
N of Valid Cases
484
a 309 cells (93.9%) have expected count less than 5. The minimum expected count is .00
Appendix P
Correlations
Go to a movie
Age
Go to a movie
Pearson Correlation
-.169(**)
Sig. (2-tailed)
.000
N
484
484
Age
Pearson Correlation
-.169(**)
Sig. (2-tailed)
.000
N
484
485
** Correlation is significant at the 0.01 level (2-tailed).
Appendix Q
age
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
5
3.0
3.0
3.0
6
3.0
3.0
6.1
7
2
6.1
6.1
2.1
8
6
8.2
8.2
30.3
9
6
8.2
8.2
48.5
20
0
30.3
30.3
78.8
21
2
6.1
6.1
84.8
22
3
9.1
9.1
93.9
23
3.0
3.0
97.0
26
3.0
3.0
00.0
Total
33
00.0
00.0
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * age
33
00.0%
0
.0%
33
00.0%
Go to a movie * age Crosstabulation
age
Total
5
6
7
8
9
20
21
22
23
26
Go to a movie
Once a month or less
Count
0
2
4
4
6
0
20
% within Go to a movie
.0%
5.0%
0.0%
20.0%
20.0%
30.0%
.0%
5.0%
5.0%
5.0%
00.0%
Fortnighly
Count
0
0
0
3
0
2
0
0
7
% within Go to a movie
4.3%
.0%
.0%
.0%
4.3%
42.9%
.0%
28.6%
.0%
.0%
00.0%
Once a week
Count
0
0
0
2
0
2
0
0
0
5
% within Go to a movie
.0%
.0%
.0%
40.0%
.0%
20.0%
40.0%
.0%
.0%
.0%
00.0%
A few times a week
Count
0
0
0
0
0
0
0
0
0
% within Go to a movie
.0%
.0%
.0%
.0%
00.0%
.0%
.0%
.0%
.0%
.0%
00.0%
Total
Count
2
6
6
0
2
3
33
% within Go to a movie
3.0%
3.0%
6.1%
8.2%
8.2%
30.3%
6.1%
9.1%
3.0%
3.0%
00.0%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
30.329(a)
27
.300
Likelihood Ratio
27.776
27
.423
Linear-by-Linear Association
.005
.946
N of Valid Cases
33
a 39 cells (97.5%) have expected count less than 5. The minimum expected count is .03
Appendix R
Correlations
Go to a movie
age
Go to a movie
Pearson Correlation
.012
Sig. (2-tailed)
.947
N
33
33
age
Pearson Correlation
.012
Sig. (2-tailed)
.947
N
33
33
Appendix S
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * I am 'careful about money'
484
99.8%
.2%
485
00.0%
Go to a movie * I am 'careful about money' Crosstabulation
I am 'careful about money'
Total
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
6
Go to a movie
Never
Count
3
6
2
20
7
0
48
% within Go to a movie
6.3%
2.5%
25.0%
41.7%
4.6%
.0%
00.0%
Once a month or less
Count
1
50
57
35
46
0
299
% within Go to a movie
3.7%
6.7%
9.1%
45.2%
5.4%
.0%
00.0%
Fortnighly
Count
5
7
5
36
9
0
82
% within Go to a movie
6.1%
20.7%
8.3%
43.9%
1.0%
.0%
00.0%
Once a week
Count
3
1
3
5
0
43
% within Go to a movie
7.0%
25.6%
30.2%
1.6%
23.3%
2.3%
00.0%
A few times a week
Count
0
2
3
2
9
% within Go to a movie
.0%
1.1%
22.2%
33.3%
22.2%
1.1%
00.0%
Daily
Count
0
0
0
0
2
% within Go to a movie
.0%
.0%
50.0%
.0%
50.0%
.0%
00.0%
2
Count
0
0
0
0
0
% within Go to a movie
.0%
.0%
00.0%
.0%
.0%
.0%
00.0%
Total
Count
22
85
01
99
75
2
484
% within Go to a movie
4.5%
7.6%
20.9%
41.1%
5.5%
.4%
00.0%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
59.747(a)
30
.001
Likelihood Ratio
42.499
30
.065
Linear-by-Linear Association
.408
.523
N of Valid Cases
484
a 25 cells (59.5%) have expected count less than 5. The minimum expected count is .00.
Appendix T
Correlations
Go to a movie
I am 'careful about money'
Go to a movie
Pearson Correlation
-.029
Sig. (2-tailed)
.524
N
484
484
I am 'careful about money'
Pearson Correlation
-.029
Sig. (2-tailed)
.524
N
484
485
Appendix U
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * careful about money
33
00.0%
0
.0%
33
00.0%
Go to a movie * careful about money Crosstabulation
careful about money
Total
disagree
neutral
agree
Strongly agree
Go to a movie
Once a month or less
Count
2
8
8
2
20
% within careful about money
66.7%
66.7%
50.0%
00.0%
60.6%
Fortnighly
Count
0
2
5
0
7
% within careful about money
.0%
6.7%
31.3%
.0%
21.2%
Once a week
Count
2
2
0
5
% within careful about money
33.3%
6.7%
2.5%
.0%
5.2%
A few times a week
Count
0
0
0
% within careful about money
.0%
.0%
6.3%
.0%
3.0%
Total
Count
3
2
6
2
33
% within careful about money
00.0%
00.0%
00.0%
00.0%
00.0%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
4.950(a)
9
.839
Likelihood Ratio
6.378
9
.702
Linear-by-Linear Association
.007
.935
N of Valid Cases
33
a 14 cells (87.5%) have expected count less than 5. The minimum expected count is .06.
Appendix V
Correlations
Go to a movie
careful about money
Go to a movie
Pearson Correlation
-.015
Sig. (2-tailed)
.936
N
33
33
careful about money
Pearson Correlation
-.015
Sig. (2-tailed)
.936
N
33
33
Appendix W
Go to a movie
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Never
48
9.9
9.9
9.9
Once a month or less
299
61.6
61.8
71.7
Fortnightly
82
6.9
6.9
88.6
Once a week
43
8.9
8.9
97.5
A few times a week
9
.9
.9
99.4
Daily
2
.4
.4
99.8
2
.2
.2
00.0
Total
484
99.8
00.0
Missing
System
.2
Total
485
00.0
Table 4.1
Diagram 4.1
Go to a movie* I am 'always pressed for time' Crosstabulation
I am 'always pressed for time'
Total
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
6
Go to a movie
Never
Count
5
0
7
4
2
0
48
% within Go to a movie
0.4%
20.8%
4.6%
29.2%
25.0%
.0%
00.0%
Once a month or less
Count
1
52
97
91
48
0
299
% within Go to a movie
3.7%
7.4%
32.4%
30.4%
6.1%
.0%
00.0%
Fortnightly
Count
5
21
22
8
5
0
81
% within Go to a movie
6.2%
25.9%
27.2%
22.2%
8.5%
.0%
00.0%
Once a week
Count
6
9
5
0
2
43
% within Go to a movie
2.3%
4.0%
20.9%
34.9%
23.3%
4.7%
00.0%
A few times a week
Count
3
3
0
9
% within Go to a movie
1.1%
1.1%
1.1%
33.3%
33.3%
.0%
00.0%
Daily
Count
0
0
0
0
2
% within Go to a movie
.0%
50.0%
.0%
50.0%
.0%
.0%
00.0%
2
Count
0
0
0
0
0
% within Go to a movie
.0%
.0%
.0%
00.0%
.0%
.0%
00.0%
Total
Count
23
91
36
43
88
2
483
% within Go to a movie
4.8%
8.8%
28.2%
29.6%
8.2%
.4%
00.0%
Table 4.2
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * I am 'always pressed from time'
483
99.6%
2
.4%
485
00.0%
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
46.760(a)
30
.026
Likelihood Ratio
36.374
30
.196
Linear-by-Linear Association
.436
.231
N of Valid Cases
483
a 25 cells (59.5%) have expected count less than 5. The minimum expected count is .00.
Appendix X
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * The activity 'requires to much time'
480
99.0%
5
.0%
485
00.0%
Go to a movie* The activity 'requires too much time' Crosstabulation
The activity 'requires too much time'
Total
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Go to a movie
Never
Count
9
8
0
7
2
46
% within Go to a movie
9.6%
39.1%
21.7%
5.2%
4.3%
00.0%
Once a month or less
Count
70
32
62
26
8
298
% within Go to a movie
23.5%
44.3%
20.8%
8.7%
2.7%
00.0%
Fortnightly
Count
6
35
4
2
4
81
% within Go to a movie
9.8%
43.2%
7.3%
4.8%
4.9%
00.0%
Once a week
Count
4
5
4
7
3
43
% within Go to a movie
9.3%
34.9%
32.6%
6.3%
7.0%
00.0%
A few times a week
Count
2
4
9
% within Go to a movie
22.2%
1.1%
44.4%
1.1%
1.1%
00.0%
Daily
Count
0
0
0
2
% within Go to a movie
50.0%
.0%
.0%
50.0%
.0%
00.0%
2
Count
0
0
0
0
% within Go to a movie
00.0%
.0%
.0%
.0%
.0%
00.0%
Total
Count
03
201
04
54
8
480
% within Go to a movie
21.5%
41.9%
21.7%
1.3%
3.8%
00.0%
Table 4.3
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
28.517(a)
24
.239
Likelihood Ratio
27.984
24
.261
Linear-by-Linear Association
2.817
.093
N of Valid Cases
480
a 19 cells (54.3%) have expected count less than 5. The minimum expected count is .04.
Appendix Y
Go to a movie
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Once a month or less
20
60.6
60.6
60.6
Fortnightly
7
21.2
21.2
81.8
Once a week
5
5.2
5.2
97.0
A few times a week
3.0
3.0
00.0
Total
33
00.0
00.0
Table 4.1A
Diagram 4.1A
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * always pressed for time
33
00.0%
0
.0%
33
00.0%
Go to a movie* I am 'always pressed for time' Crosstabulation
always pressed for time
Total
disagree
neutral
agree
Strongly agree
Go to a movie
Once a month or less
Count
3
0
6
20
% within Go to a movie
5.0%
50.0%
30.0%
5.0%
00.0%
Fortnightly
Count
2
3
7
% within Go to a movie
4.3%
28.6%
42.9%
4.3%
00.0%
Once a week
Count
2
2
0
5
% within Go to a movie
40.0%
40.0%
.0%
20.0%
00.0%
A few times a week
Count
0
0
0
% within Go to a movie
.0%
00.0%
.0%
.0%
00.0%
Total
Count
6
5
9
3
33
% within Go to a movie
8.2%
45.5%
27.3%
9.1%
00.0%
Table 4.2A
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
6.514(a)
9
.688
Likelihood Ratio
7.773
9
.557
Linear-by-Linear Association
.116
.734
N of Valid Cases
33
a 14 cells (87.5%) have expected count less than 5. The minimum expected count is .09.
Appendix Z
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Go to a movie * requires too much time
33
00.0%
0
.0%
33
00.0%
Go to a movie* The activity 'requires too much time' Crosstabulation
requires too much time
Total
Strongly disagree
disagree
neutral
agree
Strongly agree
Go to a movie
Once a month or less
Count
3
6
7
3
20
% within Go to a movie
5.0%
30.0%
35.0%
5.0%
5.0%
00.0%
Fortnightly
Count
5
0
0
7
% within Go to a movie
4.3%
71.4%
4.3%
.0%
.0%
00.0%
Once a week
Count
3
0
0
5
% within Go to a movie
20.0%
60.0%
20.0%
.0%
.0%
00.0%
A few times a week
Count
0
0
0
0
% within Go to a movie
00.0%
.0%
.0%
.0%
.0%
00.0%
Total
Count
6
4
9
3
33
% within Go to a movie
8.2%
42.4%
27.3%
9.1%
3.0%
00.0%
Table 4.3A
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
0.605(a)
2
.563
Likelihood Ratio
0.680
2
.557
Linear-by-Linear Association
4.470
.034
N of Valid Cases
33
a 18 cells (90.0%) have expected count less than 5. The minimum expected count is .03.
MKTG 2010 ?Major Project
Evelyn, Stella, Emy, Rena Page 1 of 57
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