• Asian-Australasian Journal of Animal Sciences
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• v.14 no.6
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• pp.837-843
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• 2001

Two experiments were conducted to compare the feed intake patterns and growth performance of Meishan and Yorkshire growing pigs. Experiment 1 was carried out over a 6-wk period and used 48 barrows with equal numbers of purebred Meishan (M) and Yorkshire (Y). Pigs were allocated to four groups of 12 pigs consisting of equal numbers of M and Y. Initial BW were $36.4{\pm}0.32kg$ and $42.1{\pm}1.41kg$ for M and Y, respectively. Experiment 2 was carried out over a 5-week period and used 48 pigs consisting of equal numbers of both barrows and gilts and of crossbred Meishan$\times$Yorkshire (MY) and purebred Yorkshire (Y) animals. Pigs were allotted to 6 pens of 8 pigs, with 4 single- and 2 mixed-genotype groups (initial $BW=28.5{\pm}0.99kg$). In both experiments, pigs were given ad libitum access to a grower diet (17% crude protein, 0.9% lysine, 3365 kcal/kg ME) via feed intake recording equipment (F.I.R.E.). Pigs carried an ear-tag transponder with an unique identification which allowed the time, duration, and size of individual meals to be recorded. In Exp. 1, Y had higher ADG (721 vs 353 g, p<0.01), daily feed intake (DFI; 2.338 vs 1.363 kg, p<0.01), made more frequent visits to the feeder per day (NFV; 18.5 vs 7.7, p<0.01), had a shorter feeder occupation time per visit (FOV; 7.4 vs 12.9 min, p<0.01), and ate less feed per visit (FIV; 130 vs 177 g, p<0.01) than M pigs. Feed consumption rates (CR) were greater for Y compared to M (19.3 vs 14.8 g/min, p<0.01). Feeder occupation time per day (FOD) was longer for Y than M (114.3 vs 82.8 min/pig, p<0.01). Yorkshire pigs visited the feeder more frequently between 0800 and 1100 h. Meishan pigs showed more frequent feeder visits between 0600 and 0800 h, and between 1600 and 2100 h when feeding competition with Y was reduced. In Exp. 2, there was no effect of genotype or group composition on DFI, ADG or gain:feed ratio. Crossbred pigs (MY) made fewer feeder visits (12.6 vs 17.7, p<0.01), and had greater FIV (124 vs 98 g/visit, p<0.01), and longer FOV (8.11 vs 7.24 min/visit, p<0.01) and FOD (112 vs 100 min, p<0.05) than Y pigs. Results of this study suggest substantial genetic variation in feeding patterns as well as in growth performance.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.12
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• pp.1717-1725
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• 2000

The objective was to determine the relationship between feed intake levels and patterns, and growth performance and body composition of barrows and gilts using automatic feed intake recording equipment (F.I.R.E.). This system records the time of visits to the feeder and the duration and size of meals for individual animals housed in groups. Ninety-six crossbred pigs were grown from $33.4{\pm}0.51$ to $109.7{\pm}1.39kg$ live weight over a 13-week period. Eight mixed-sex groups of 12 pigs were used and 4 dietary treatments were compared giving 2 pens per treatment. The dietary treatments consisted of corn-soybean meal diets with differing protein levels which ranged from 14.7% to 19% between 30 to 55 kg, from 13.3% to 16.9% between 56 and 85 kg, and from 12.3% to 16.8% for the remainder of the study. Animals were ultrasonically scanned to measure loin-eye area and backfat thickness to estimate carcass fat-free lean content at the beginning and end of the study. Barrows had higher daily feed intake than gilts (2.67 vs. 2.46 kg resp. p<0.05) which was the result of a longer feeder occupation time per visit (4.77 vs. 4.54 min, resp. p<0.05), higher feed consumption rates (30.4 vs. 29.0 g/min, resp. p<0.05), and higher feed intakes per visit (136.9 vs. 126.8 g, resp. p<0.01). Gilts had less backfat and greater loin-eye area than barrows (p<0.05). Diet had no significant effect on growth performance and had limited impact on feeding patterns. Body weight showed high correlations with ADG (r=0.74), feed intake per visit (r=0.51) and feed consumption rate (r=0.69). Positive correlation were also found between daily feed intake and feed intake per visit (r=0.45), feeder occupation time per day (r=0.56), and feed consumption rate (r=0.55), and between daily feed intake and backfat thickness (r=0.32) and feed consumption rate and loin-eye area (r=0.32). There were negative correlations between number of feeder visit per day and daily feed intake (r=-0.54), and between feed intake per visit and number of feeder visits per day (r=-0.43). However, correlations between feed intake traits and carcass traits were generally low. Visits to the feeder were greatest during the morning (0700 to 1100 h) and lowest during the evening and nighttime. These results highlight limited variation among the sexes in feeding patterns and suggest important relationships between feeding behavior and feed intake.

• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.1-29
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• 2007

The flow theory becomes one of the most important frameworks in the internet research arena. Hoffman and Novak proposed a hierarchical flow model showing the antecedents and outcomes of flow and the relationship among these variables in the hyper-media computer circumstances (Hoffman and Novak 1996). This model was further tested after their initial research (Novak, Hoffman, and Yung 2000). At their paper, Hoffman and Novak explained that the balance of challenge and skill leads to flow which means the positive optimal state of mind (Hoffman and Novak 1996). An imbalance between challenge and skill, leads to negative states of mind like anxiety, boredom, apathy (Csikszentmihalyi and Csikszentmihalyi 1988). Almost all research on the flow 4-channel model have been focusingon flow, the positive state of mind (Ellis, Voelkl, and Morris 1994 Mathwick and Rigdon 2004). However, it also needs to examine the formation of the negative states of minds and their outcomes. Flow researchers explain play or playfulness as antecedents or the early state of flow. However, play has been regarded as a distinct concept from flow in the flow literatures (Hoffman and Novak 1996; Novak, Hoffman, and Yung 2000). Mathwick and Rigdon discovered the influences of challenge and skill on play; they also observed the influence of play on web-loyalty and brand loyalty (Mathwick and Rigdon 2004). Unfortunately, they did not go so far as to test the influences of play on state of mind. This study focuses on the relationships between state of mind in the flow 4-channel model and play. Early research has attempted to hypothetically explain state of mind in flow theory, but has not been tested except flow until now. Also the importance of play has been emphasized in the flow theory, but has not been tested in the flow 4-channel model context. This researcher attempts to analyze the relationships among state of mind, skill of play, challenge, state of mind and web loyalty. For this objective, I developed a measure for state of mind and defined the concept of play as a trait. Then, the influences of challenge and skill on the state of mind and play under on-line shopping conditions were tested. Also the influences of play on state of mind were tested and those of flow and play on web loyalty were highlighted. 294 undergraduate students participated in this research survey. They were asked to respond about their perceptions of challenge, skill, state of mind, play, and web-loyalty to on-line shopping mall. Respondents were restricted to students who bought products on-line in a month. In case of buying products at two or more on-line shopping malls, they asked to respond about the shopping mall where they bought the most important one. Construct validity, discriminant validity, and convergent validity were used to check the measurement validations. Also, Cronbach's alpha was used to check scale reliability. A series of exploratory factor analyses was conducted. This researcher conducted confirmatory factor analyses to assess the validity of measurements. All items loaded significantly on their respective constructs. Also, all reliabilities were greater than.70. Chi-square difference tests and goodness of fit tests supported discriminant and convergent validity. The results of clustering and ANOVA showed that high challenge and high skill leaded to flow, low challenge and high skill leaded to boredom, and low challenge and low skill leaded to apathy. But, it was different from my expectation that high challenge and low skill didnot lead to anxiety but leaded to apathy. The results also showed that high challenge and high skill, and high challenge and low skill leaded to the highest play. Low challenge leaded to low play. 4 Structural Equation Models were built by flow, anxiety, boredom, apathy for analyzing not only the impact of play on state of mind and web-loyalty, but also that of state of mind on web-loyalty. According the analyses results of these models, play impacted flow and web-loyalty positively, but impacted anxiety, boredom, and apathy negatively. Results also showed that flow impacted web-loyalty positively, but anxiety, boredom, and apathy impacted web-loyalty negatively. The interpretations and implications of the test results of the hypotheses are as follows. First, respondents belonging to different clusters based on challenge and skill level experienced different states of mind such as flow, anxiety, boredom, apathy. The low challenge and low skill group felt the highest anxiety and apathy. It could be interpreted that this group feeling high anxiety or fear, then avoided attempts to shop on-line. Second, it was found that higher challenge leads to higher levels of play. Test results show that the play level of the high challenge and low skill group (anxiety group) was higher than that of the high challenge and high skill group (flow group). However, this was not significant. Third, play positively impacted flow and negatively impacted boredom. The negative impacts on anxiety and apathy were not significant. This means that the combination of challenge and skill creates different results. Forth, play and flow positively impacted web-loyalty, but anxiety, boredom, apathy had negative impacts. The effect of play on web-loyalty was stronger in case of anxiety, boredom, apathy group than fl ow group. These results show that challenge and skill influences state of mind and play. Results also demonstrate how play and flow influence web-loyalty. It implies that state of mind and play should be the core marketing variables in internet marketing. The flow theory has been focusing on flow and on the positive outcomes of flow experiences. But, this research shows that lots of consumers experience the negative state of mind rather than flow state in the internet shopping circumstance. Results show that the negative state of mind leads to low or negative web-loyalty. Play can have an important role with the web-loyalty when consumers have the negative state of mind. Results of structural equation model analyses show that play influences web-loyalty positively, even though consumers may be in the negative state of mind. This research found the impacts of challenge and skill on state of mind in the flow 4-channel model, not only flow but also anxiety, boredom, apathy. Also, it highlighted the role of play in the flow 4-channel model context and impacts on web-loyalty. However, tests show a few different results from hypothetical expectations such as the highest anxiety level of apathy group and insignificant impacts of play on anxiety and apathy. Further research needs to replicate this research and/or to compare 3-channel model with 4-channel model.