He Who Knows Most Says Least, But Says Best: an Information Overload Perspective

ABSTRACT - Previous studies on information overload assumed that information quantity alone constitutes information load. Results from this study, however, suggest that information quality is another vital component. Specifically, it found that high quality information, compared to low quality information, encouraged consumers to use more information and increased their confidence in purchase decisions. Further, when a high quantity of information is provided, high quality information raised confidence in purchase decisions, compared to low quality information. These findings suggest that policy makers and marketers should consider not only the amount of information to provide, but also the form in which information is provided.


Ai Ching Lim and Swee Hoon Ang (2001) ,"He Who Knows Most Says Least, But Says Best: an Information Overload Perspective", in AP - Asia Pacific Advances in Consumer Research Volume 4, eds. Paula M. Tidwell and Thomas E. Muller, Provo, UT : Association for Consumer Research, Pages: 60-66.

Asia Pacific Advances in Consumer Research Volume 4, 2001      Pages 60-66


Ai Ching Lim, National University of Singapore, Singapore

Swee Hoon Ang, National University of Singapore, Singapore


Previous studies on information overload assumed that information quantity alone constitutes information load. Results from this study, however, suggest that information quality is another vital component. Specifically, it found that high quality information, compared to low quality information, encouraged consumers to use more information and increased their confidence in purchase decisions. Further, when a high quantity of information is provided, high quality information raised confidence in purchase decisions, compared to low quality information. These findings suggest that policy makers and marketers should consider not only the amount of information to provide, but also the form in which information is provided.

One issue that has generated much interest in the field of consumer information processing is information overload. However, while some findings provide empirical support (e.g., Jacoby, Speller, and Berning 194; Jacoby, Speller, and Kohn 1974), other research evidence testifies against its occurrence (e.g., Scammon 1977). Such disagreement may have risen from "the absence of a precise definition as to how much information constitute overload" (Schiffman and Kanuk 1994, p. 217).

The introduction of the NLEA in the U.S. spawned much research on consumer use of nutrition information. Many researchers expressed concern regarding the effectiveness of providing nutrition information to consumers (Brucks, Mitchell, and Staelin 1984; Burton and Andrews 1996; Jacoby, Chestnut, and Silberman 1977) while others focused on finding the best label format (Bettman and Kakkar 1977; Burton, Biswas, and Netemeyer 1994; Muller 1985; Russo et al. 1986; Viswanathan 1994). However, one fundamental issue is that it is unclear whether consumers are capable of using the information given to them (Jacoby, Chestnut, and Silberman 1977). Therefore, the current study uses nutrition information provision as the context in which information overload is studied. The issue of information overload is highly relevant to the study of nutrition information provision as an understanding of when overload occurs will assist marketers in improving the label’s communication effectiveness.

Past research implicitly assumed that information overload resulted only from excess information provided, but recent research has explicitly examined the role of information quality in determining information load. For instance, Keller and Staelin (1987) used both the quantitative and qualitative dimensions of information to investigate the occurrence of information overload. Further, the intuitive interaction between information quantity and quality in moderating the effects of information overload has not been examined.

The effects of both information quantity and quality, which constitute information load, will be investigated in this study. Information quantity is operationalized as the number of nutrition attributes provided on the label while information quality is the extent to which the provided information is ambiguous. By information ambiguity, we mean the extent to which the provided information is made useful to the consumer. For instance, different people can rate a product containing 3.0g of fat as high, moderate, or low in fat content when no interpretation is provided. Therefore, since consumers are unlikely to be able to interpret the value of absolute amounts of nutrients in a meaningful way, such information is ambiguous and offers little help for consumer decision making (Viswanathan 1994).

Specifically, effects of information quantity and quality on consumer recall, confidence about purchase decision, and use of information will be investigated. In so doing, we aim to obtain findings that support the occurrence of information overload. This study also aims to extend research on information overload by examining the interaction effects due to information quantity and quality.

The following section provides the literature review and the hypotheses. Next, the research method is presented, followed by results of the study. The theoretical, methodological, managerial, and policy implications derived from the findings are discussed at the end of this paper, together with directions for future research.


Information Overload

Information overload occurs during the transition from sensory to short-term store, when the amount of information exceeds the capacity of the short-term store and the mind fails to rehearse all the sensory stimuli entering the short-term store (Atkinson and Shiffrin 1968). Information overload thus occurs due to consumers’ limited cognitive ability to process. This understanding is effectively captured in Malhotra’s (1982) postuation that the existence of information overload is due to consumers’ "finite limits to absorb and process information during any given unit of time" (p. 419).

Although information overload has been conceptually and theoretically accepted, past research yielded ambiguous evidence of its occurrence in experiments conducted. On the one hand, advocates like Jacoby, Speller, and Berning (1974) found that consumers spend less time acquiring information if the amount of information increases beyond a certain point. This finding supports Jacoby, Speller, and Kohn’s (1974) contention that poorer decisions could result from the provision of substantial amounts of information. Therefore, they argued that there is a limit to the amount of information that an individual can effectively process before he becomes "overloaded" with information.

However, Scammon (1977) refuted the occurrence of information overload and concluded that the reduction in recall was more likely due to the division of a fixed amount of available processing time as the individual receives more information, rather than the occurrence of information overload as she previously assumed. Further, critics argued that information overload is unlikely to take place in an actual consumption setting since consumers are unlikely to allow themselves to be overloaded, but will quickly employ strategies to avoid expending too much mental resources (Muller 1985).

In summary, there has been little agreement concerning the occurrence of information overload both in experimental laboratories and actual consumption settings. Perhaps the lack of agreement is due to the shortage of agreed-upon definitions for factors crucial to the study of information overload, one of which has been noted by Schiffman and Kanuk (1994). Another factor, the dimensions constituting information load, is considered in this study.

Information Load: Information Quantity and Information Quality

While it is traditionally assumed that overload only results from an excess amount of information, some evidence exists to suggest that the qualitative aspect of information is also important. For instance, researchers have studied the differences between simple versus complex information (Russo, Krieser, and Miyashita 1975), and processed versus unprocessed information (Scammon 1977). Generally, it was found that consumers’ information processing capabilities improved when information of higher quality (i.e., information that is simpler, processed, and more easily understood) was used. More recently, research has suggested that information load has two dimensionsBinformation quantity and information quality (Ford et al. 1996; Keller and Staelin 1987). Therefore, this study explicitly considers both dimensions of information as determinants of information load.


Information Quantity: Number of Attributes

One measure used in past research to gauge an individual’s cognitive capacity is the maximum number of attributes that can be processed before dysfunctional behavior occurs. Streufert and his associates (e.g., Streufert 1970; Streufert, Driver, and Huan 1967) found that information processing levels decreased when the number of attributes exceeded 10. Malhotra (1982) remarked that the consistency of the results obtained by Streufert suggested that "individuals cannot handle more than 10 items of information simultaneously" (p. 427). Scammon (1977) observed that recall first increases when more information is initially provided, then decreases when the cognitive limit is exceeded. This curvilinear relationship between recall and the amount of information provided is consistent with past research that found a limit to the numbe of attributes that can be processed. Taken together, we expect recall to first increase as more nutritional attributes are provided, then decrease as the consumer becomes overloaded.

In terms of consumers’ perceived confidence about their product choices, Keller and Staelin (1987) found that as more information was provided, the confidence that consumers held about their choice decreased. Thus, as information quantity increases beyond the amount that a consumer can handle, confidence is expected to decrease.

Several studies have focused on consumers’ comprehension and utilization of information (Burton and Andrews 1996; Christine Moorman 1990; Jacoby, Chestnut, and Silberman 1977; Keller and Staelin 1987). Jacoby and his associates concluded that while consumers had a tendency to ask for more information, "most consumers neither acquire such information when making a purchase decision nor comprehend most nutrition information once they receive it" (p. 119). Levy, Fein, and Schucker (1996) further refuted the implicit theory that more information means that such information is more useful. Hence, as the information quantity increases, we expect the use of information to decrease. Based on the above discussion, H1 predicts that as more information becomes available, recall, purchase decision confidence, use of information, and confusion will decrease. Hence H1 states:

H1: When the number of attributes provided is increased from low to high,

a) recall of nutrition information decreases,

b) confidence about purchase decision decreases, and

c) use of information decreases.

Information Quality: Information Ambiguity

Keller and Staelin (1987) defined information quality as "the usefulness of the available attribute information in aiding a decision maker to evaluate his/her true utility associated with an alternative" (p. 202). In this study, information quality is operationalized by controlling the level of information ambiguity, which has been defined as the potential for multiple interpretations (Hoch and Ha 1986; Lambert and Wedell 1991).

Ford et al. (1996) operationalized ambiguity through the use of absolute numbers with or without adjectival descriptors in the presentation of nutrition information to represent the ambiguous and unambiguous conditions respectively. It was found that subjects were better able to distinguish the healthier brand of frozen dinner when information provided was unambiguous (i.e., containing absolute numbers and adjectival descriptors), compared to when it was ambiguous (i.e., containing absolute numbers only). Levy, Fein, and Schucker (1996) also found that the provision of adjectival descriptors next to absolute nutrient amounts helped subjects understand the relative magnitude of the food content. Further support came from Scammon (1977), who observed that subjects could more accurately identify the "more nutritious" brand when adjectival descriptions were used (e.g., "excellent," "good," or "fair") instead of left unprocessed in the percentage form. She suggested that adjectival descriptions "made the respondents’ evaluation process easier since some of the information-processing was already done" (p. 54).

On the effects of ambiguity on recall, Scammon (1977) found that subjects could recall better when the provided information was processed rather than unprocessed. Since processed information effectively removes the ambiguity, recall is expected to improve when unambiguous, rather than ambiguous information is provided.

Ghosh and Ray (1997) suggested that the presence of ambiguity reduces the confidence level, which is intuitively appealing since ambiguous information creates doubt, which results in a lower confidence level. Hence, we hypothesize that confidence decreases when ambiguous information is provided.

On consumer use of information, Keller and Staelin (1987) observed that as the quality level information increased, consumers used more information. Hence, consumers use information when it is meaningful to them. Together, the above discussion suggests that as ambiguity increases, recall, purchase decision confidence, and use of information will decrease. Formally, H2 is formally proposed as:

H2: When ambiguous, rather than unambiguous information, is provided:

a) recall of nutrition information decreases,

b) confidence about purchase decision decreases, and

c) use of information decreases.

Interaction Effects

Although Scammon (1977) did not test for interaction effects between information amount and format on recall, it seems intuitive that such interaction exists. When information quantity is low, the consumer has sufficient mental resources to engage deeply so as to think about what the ambiguous information means to him/her. This deeper level of information processing and higher level of rehearsal involved helps him/her to better remember the information. Thus, the presence of ambiguity improves recall for a low number of attributes, resulting in similar recall scores between the two conditions of ambiguity. However, when information quantity is high, ambiguity inflates the information load and the consumer is discouraged from engaging in deep processing. Past research have found that when faced with huge amounts of information, consumers resort to employing simplifying strategies for decision making so as to reduce the amount of cognitive effort needed (Helgeson and Ursic 1993; Jacoby, Speller, and Berning 1974; Mueller 1984). Hence, at a high level of information quantity, ambiguous information will elicit a higher recall (of familiar items) since selective attention is likely to take place. Formally, H3 states:

H3a: There will be an interaction between number of attributes and ambiguity resulting in:

a) a similar level of recall when ambiguous, rather than unambiguous, information is provided with a low number of attributes, and

b) a higher recall when ambiguous, rather than unambiguous, information is provided with a high number of attributes.

Ambiguity is also expected to produce a compounding effect on confidence when a high number of attributes is provided. First, having to process more attributes with ambiguous information requires huge cognitive effort and hence, it is expected that one’s confidence of choice will be reduced compared to when a small number of attributes is used. This is consistent with the predicted main effects on onfidence due to information quantity.

However, as information quantity increases, the provision of unambiguous information is likely to be better accepted than if ambiguous information is provided. While each piece of unambiguous information adds explicit product knowledge to the subject’s evaluation of the product, hence building up confidence in his/her choice, ambiguous information merely inflates the apparent information load. Such expectations are captured under H3b:

H3b: There will be an interaction between number of attributes and ambiguity resulting in:

a) a similar level of confidence about purchase decision when ambiguous, rather than unambiguous information is provided with a low number of attributes, and

b) a higher level of confidence about purchase decision when unambiguous, rather than unambiguous, information is provided with a high number of attributes.


Experimental Design

A 2x2 between-subjects factorial design was used. Subjects were 87 undergraduate students randomly allocated to four treatment cells.

Stimulus Materials

This study examines information provision in a nutrition information provision setting, and hence nutrition information on nutrition labels was used as the stimulus. Following Malhotra’s (1982) observation that high involvement in the task was a necessary condition for possible information overload occurrence, subjects were instructed to imagine himself or herself being the principal of an elementary school who had to choose a brand of milk to adopt for the school’s milk-order scheme so as to ensure that subjects perceive the task to be important. Milk was chosen for its familiarity among subjects and because it bears some health consequences if consumed for long periods of time. A fictitious name, "Nutrimilk", was used to avoid eliciting biased evaluations. Four different nutrition labels were developed for the four treatment cells.

Experimental Procedure

At the start of the experiment, subjects were reminded to "answer the questions as best as they can by following the instructions carefully." After that, each student was given a questionnaire that contained a nutrition label manipulated in accordance to one of the four treatment cells. Most subjects completed the questionnaire in less than ten minutes, and the whole experiment lasted only about 15 minutes per session.

Independent Variables

Information Quantity: Number of attributes

In order to create up to 20 nutritional attributes, nutrients and vitamins not normally present in milk were included. Conditions of low and high information load were created by using eight and 20 nutritional attributes. However, none of the subjects indicated any suspicion regarding the fictitious attributes used for the study. Therefore, the use of fictitious attributes should not affect the responses of the subjects in any way.

Four items were used to measure information quantity. Subjects rated the information on a seven-point numerical scale (too little/too much, needs less information/needs more information). They were also asked what they thought about the quantity of nutrition information provided on the label and whether they would like to see more information on the label. With a Cronbach apha of 0.91 the scores were averaged. Consistent with pretest results, subjects rated the low information quantity treatment as providing less information than the high information quantity treatment (x=3.60 vs. 4.54, t=-4.46, p<0.01). Hence the manipulation for information quantity was successful.

Information Quality: Information Ambiguity

Two levels of information ambiguity were used for this study: ambiguity and no ambiguity. An ambiguous condition was one that offered only absolute information while an unambiguous condition contained comparative information explicitly stating how Nutrimilk fared against fresh milk for each attribute.

Four items using seven-point numerical scales were used to measure ambiguity. Subjects were asked to compare Nutrimilk against fresh milk using anchors of ambiguous/specific, vague/precise, clear/not clear, and useful/not useful. The latter two were reverse scored. A high Cronbach alpha value was obtained (?=0.93), confirming that the scale used to measure ambiguity was reliable. Hence an average score across the four items was used. A manipulation check showed that subjects rated the information in the ambiguous condition as less clear than the unambiguous condition (x=4.00 vs. 5.25, t=4.15, p<0.01).

Importance of decision

To ensure that the decision task is involving, subjects rated the task on three seven-point items (not important at all/very important, not significant at all/very significant, can be made easily/needs to be deliberated). Following a high Cronbach alpha value of 0.86, an average score was used for analysis. Results showed high levels of involvement (x>5.57) with no significant main and interaction effects across conditions (x’s>5.20, F’s<1.07, p>0.10).

Dependent Variables


Five items asking about the amounts of nutrition attributes found in the brand of milk were constructed to measure recall. For each item, three options were provided, one of which was the correct answer. Subjects were then scored on the number of correct answers, thus recall scores vary from zero to five. All five nutrition attributes used were common and familiar to the subjects.

Confidence about Purchase Decision

A single seven-point item was used to measure subjects’ confidence about their purchase decision.

Use of information

Two seven-point items were used to measure subjects’ ability to use information from the nutrition label. These were questions asking subjects how easy it was to select and use information from the label provided, and how much nutritional information they were able to use. With a Cronbach’s alpha of 0.73, an average score was used in the analysis.




Hypotheses Testing

Data collected was analyzed using MANOVA followed by univariate ANOVA. The descriptive statistics are furnished in Table 1.

H1: Main effect due to Information Quantity

From Table 1, only use of information (i.e. H1c) yielded significant results. Specifically, subjects found it more difficult to use the information when 20 attributes were provided than when eight were given (x=3.78 vs 3.30, p<0.10, where 1=a lot and 7=none of it). H1c is therefore marginally supported. Overall, there isa significant main effect due to information quantity (p<0.05).

H2: Main effect due to Information Quality

Univariate ANOVA test results show that only H2a is not supported. H2b, and H2c are supported. In particular, subjects were less confident when ambiguous information was provided (x=4.00 vs 5.31, p<0.01), thus supporting H2b. Subjects also found it more difficult to use information when it was ambiguous (x=3.96 vs 3.16, p<0.01, where 1=a lot and 7=none of it). Overall, there is a significant main effect due to information quality (p<0.01).

H3: Interaction Effect

From Table A, there is an interaction effect on confidence but not on recall. From Table B, there is no difference in the mean scores for confidence when the quantity of information provided is low (x=4.39 vs 5.05, t=1.53, p<0.05). However, when the information quantity is high, the means are significantly different (x=3.47 vs 5.52, t=6.36, p<0.05). Collectively, these results show that H3c is supported.


The first objective of this study was to examine the impact of information quantity and information quality on recall, confidence about purchase decision, and use of information. The impact due to information quantity is first discussed, followed by that of information quality.

Results showed that as the information quantity increased from low to high, subjects’ use of information decreased. This finding is consistent with past research findings that consumers do not use information even when more is provided (Jacoby, Chestnut, and Silberman 1977; Levy, Fein, and Schucker 1996).

However, there was no difference in the mean recall levels. Besides, no significant relationship was found for confidence about purchase decision, in contrast to Keller and Staelin’s (1987) finding that consumers’ perceived confidence decreased as the amount of information provided increased. While the number of attributes used by Keller and Staelin (1987) ranged from four to 12, the present study used eight or 20 attributes. Consumers may be more sensitive at lower levels of information quantity and any increase in information was perceived as more significant.

Information quality had significant effects on confidence and use of information. In particular, confidence about purchase decision and use of information improved when unambiguous information was provided, consistent with that observed by Keller and Staelin (1987). However, recall was indifferent between the two levels of ambiguity as it was between the two levels of information quantity.

The second objective of this study involved investigating the interaction between information quantity and quality. Only H3b was supported. Specifically, when a large amount of information was provided, unambiguous information produced higher confidence in purchase decisions. Although a significant effect is found only for one dependent variable, there are still some important theoretical implications. First, the finding of a significant interaction between number of attributes and ambiguity suggests that information quality may be able to offset some of the overloading effects due to sheer information quantity. Second, acknowledging information quality as another dimension of information derives a better definition of information load. As discussed in the following section, this finding also has some implications for marketers.

This study also found that information quality influences the dependent variables more than information quantity. For instance, while varying the number of attributes had no effects on confidence, providing unambiguous information raised subjects’ confidence. Thus, information quality may have a bigger hand in determining the occurrence of infrmation overload, contrary to what is commonly accepted. The implications for both marketers and policy makers are discussed in the next section.

An interesting observation is that while information quantity and information quality were expected to have the largest impact on recall, no significant relationships between these two factors and recall were found. This occurred despite the fact that the items selected for the recall questions were those that were familiar and important to subjects. A possible reason for the low recall is that subjects may be ignorant of the nutritional benefits or disbenefits offered by particular nutrients or vitamins. Subjects may not have been conditioned or educated to read, understand or evaluate nutrition labels and consumer awareness about nutrition and healthy living is only beginning to take form. As such, the nutrients or vitamins provided on labels do not mean very much to them. In fact, the importance of having prior knowledge is acknowledged by Jacoby, Chestnut, and Silberman (1977), who noted: "A necessary prerequisite for effectively interpreting and using information is prior relevant education." (p. 127).






For Marketers

The findings from the present study provide directions for marketers to improve the communication effectiveness of the nutrition label. Firstly, instead of trying to provide the least amount of information possible, they may be able to achieve better effects by providing clear and unambiguous information. Especially in the case in which a large amount of information is to be provided, marketers should ensure that the information is clear and unambiguous. The present study showed that this increases consumers’ confidence about the purchase decision. This builds brand equity, which in turn leads to repeat purchases.

There are also implications for more effective planning of the advertising dollar. This is especially true in countries where no laws exist as yet to govern information provision on labels. Given the freedom of information provision, marketers should exercise discretion in the provision of information to better position their products. For instance, if the product has very strong benefits, they should be communicated in an unambiguous manner. If not, marketers may choose to de-emphasize the product benefits or disbenefits. Thus, it is in the best interest of marketers to provide what consumers want to know or what is important to them so as to avoid overloading consumers with information that is not meaningful to them.

For policy makers

Policy makers have the power to decide what information consumers see. Hence, they should not merely be concerned about making information available, they also have a duty to ensure that it is of use to the consumers. The implication for policy makers from the findings of this study is that they should take into consideration both the quantitative and qualitative aspects of information in evaluating the effectiveness of certain formats of information disclosure. Policy makers should also look into the possibility of cultivating nutrition knowledge among consumers since consumers’ ability to use nutrition information also depends on their prior knowledge. However, before making any commitment, more research should be conducted regarding the benefits and feasibility of nutrition information provision and nutrition education since their implementation will involve huge capital investments.


Firstly, this study used milk a the stimulus to create a high-involving decision making situation. To increase generalizability of research findings, future research may study label effectiveness across food products (e.g., instant noodles, canned foods). Information provision and utilization for highly involving consumer products (e.g., cars and houses) should also be examined so as to further improve generalizability.

Future studies should investigate the sensitivity of consumer responses when varying amounts of information is provided. Specifically, employment of manipulation levels of between eight and 20 nutrition attributes will help uncover the point at which information overload (i.e., the maximum number of nutrition attributes that can be effectively processed before dysfunctional behaviour takes place) occurs. Changes in recall, confidence about purchase decision, and use of information can then be charted.

In future studies, other variables may be included. On the side of dependent variables, other responses previously associated with information overload can be examined. For instance, although it is intuitively accepted that confusion accompanies increases in information quantity, past research did not yield clear findings. In addition, how information overload affects brand choices should also be examined. For independent variables, future research can examine the determinants of information overload. For example, it will be interesting to examine how consumer behaviour varies with different amounts of time allowances, and the optimal time allowance to be given for a certain information load.

Another variable that should be considered in future studies is the level of nutritional knowledge among consumers, which may vary from country to country, depending largely on the educational level of consumers. This may be captured either as a covariate or as an independent variable, depending on the way the study is designed. Also, in countries where consumers are more nutritionally educated, other measures for the variables examined in this study can be used. While this study tested subjects on their ability to recall amounts of nutritional attributes present in the brand of milk used because subjects were less educated about nutritional information usage, free or cued recall may be used. This would yield recall values that better reflect the extent to which consumers use nutritional information. For use of information, consumers can be asked to perform simple calculations (e.g., calculate the number of servings required to meet the recommended daily requirements for a healthy diet) as an objective measure of the extent to which nutritional information is used.

The underlying processes of consumer information overload should also be examined. In particular, it will be intriguing to know which stage of the buying process it is that information overload occurs. Hence, future research should be directed at developing a model of consumer information overload to help marketers better understand the processes that consumers undergo when making a purchase decision.

Lastly, since almost all past research was performed on Western consumers, future researchers should consider using Asian consumers to test the validity of the findings. There may be differences in what constitute information overload for Western and Asian subjects, and a cross-cultural comparison will be both interesting and revealing.


Bettman, James R., and Pradeep Kakkar (1977), "Effects of Information Presentation Format on Consumer Acquisition Strategies," Journal of Consumer Research, 3, 233-240.

Broadbent, Donald E. (1975), "The Magic Number Seven After Fifteen Years," in Studies in Long Term Memory, eds., Alan Kenned and Alan Wilkes, London: John Wiley & Sons, 1-18.

Brucks, Merrie, Andrew A. Mitchell and Richard Staelin (1984), "The effect of Nutrition Information Disclosure in Advertising: An Information Processing Apporach," Journal of Public Policy and Marketing, 3, 1-25.

Burton, Scot, and Andrews, J. Craig (1996), "Age, Product Nutrition, and Label Format Effects on Consumer Perceptions and Product Evaluations," The Journal of Consumer Affairs, 30 (1), 68-89.

Business Week (1989), "Can Cornflakes Cure Cancer?" (October 9), cover page.

Ford, Gary T., Manoj Hastak, Anusree Mitra, and Debra Jones Ringold (1996), "Can Consumers Interpret Nutrition Information in the Presence of a Health Claim? A Laboratory Investigation," Journal of Public Policy & Marketing, 15 (1), 16-27.

Ghosh, Dipankar, and Manash R. Ray (1997), "Risk, Ambiguity, and Decision Choice: Some Additional Evidence," Decision Sciences, 28(1), 81-104.

Ha, Young-Won, and Stephen J. Hoch (1989), "Ambiguity, Processing Strategy, and Advertising-Evidence Interactions," Journal of Consumer Research, 16, 354-360.

Helgeson, James G., and Michael L. Ursic (1993), "Information Load, Cost/Benefit Assessment and Decision Strategy Variability," Journal of the Academy of Marketing Science, 21, 13-20.

Hoch, Stephen J., and Young-Won Ha (1986), "Consumer Learning: Advertising and the Ambiguity of Product Experience," Journal of Consumer Research, 13, 221-233.

Jacoby, Jacob, Robert W. Chestnut, and William Silberman (1977), "Consumer Use and Comprehension of Nutrition Information," Journal of Consumer Research, 4, 119-128.

Jacoby, Jacob, Donald E. Speller, and Carol K. Berning (1974), "Brand Choice as a Function of Information Load: Replication and Extension," Journal of Consumer Research, 1, 33-42.

Jacoby, Jacob, Donald E. Speller, and Carol A. Kohn (1974), "Brand Choice as a Function of Information Load," Journal of Marketing Research, 11, 63-9.

Keller, Kevin Lane, and Richard Staelin (1987), "Effects of Quality and Quantity of Information on Decision Effectiveness," Journal of Consumer Research, 14, 200-213.

Lambert, Alan J. and Douglas H. Wedell (1991), "The self and Social Judgment: Effects of Affective Reaction and #Own Position’ on Judgments of Unambiguous and Ambiguous Information About Others," Journal of Personality and Social Psychology, 61 (6), 884-97.

Levy, Alan S., Sara B. Fein, and Raymond E. Schucker (1996), "Performance Characteristics of Seven Nutrition Label Formats," Journal of Public Policy and Marketing, 15(1), 1-15.

Malhotra, Naresh K. (1982), "Information Load and Consumer Decision Making," Journal of Consumer Research, 8, 419-430.

Malhotra, Naresh K., Arun K. Jain, and Stephen W. Lagakos (1982a), "The Information Overload Controversy: An Alternative Viewpoint," Journal of Marketing, 46, 27-37.

Miller, George A. (1956), "The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information," Psychological Review, 63, 81-97.

Moorman, Christine (1990), "The Effects of Stimulus and Consumer Characteristics on the Utilisation of Nutrition Information," Journal of Consumer Research, 17, 362-374.

Muller, E. Thomas (1985), "Structural Information Factors which Stimulate the Use of Nutrition Information: A Field Experiment," Journal of Marketing Research, 22, 143-157.

Russo, J. Edward, J. Krieser, and S. Miyashita (1975), "An Effective Display of Unit Price Information," Journal of Marketing, 39(April), 11-19.

Scammon, Debra L. (1977), "#Information Load’ and Consumers," Journal of Consumer Research, 4, 148-55.

Schiffman, LeonG., and Leslie Lazar Kanuk (1994), Consumer Behaviour, Prentice Hall Inc., 5th Edition.

Simon, Herbert A. (1974), "How Big is a Chunk?" Science, 183, 482-8.

Streufert, Siegfried (1970), "Complexity and Complex Decision Making: Convergences Between Differentiation and Integration Approaches to the Prediction of Task Performance," Journal of Experimental Social Psychology, 6,494-509.

Streufert, Siegfried, Michael J. Driver, and Kenneth H. Haun (1967), "Components of Response Rate in Complex Decision Making," Journal of Experimental Social Psychology, 3, 286-95.

The Singapore Straits Times (16th August, 1995), "Nutrition Label Scheme Planned".

Venkatesan, M., Wade Lancester, and Kenneth W. Kendall (1986), "An Empirical Study of Alternate Formats for Nutrition Information Disclosure in Advertising," Journal of Public Policy and Marketing, 5, 29-43.

Viswanathan, Madhubalan (1994), "The Influence of Summary Information on the Usage of Nutrition Information," Journal of Public Policy and Marketing, 13(1), 48-60.



Ai Ching Lim, National University of Singapore, Singapore
Swee Hoon Ang, National University of Singapore, Singapore


AP - Asia Pacific Advances in Consumer Research Volume 4 | 2001

Share Proceeding

Featured papers

See More


F5. Alternative Food Consumption (AFC) Adoption and Low SES Youth Food Well-Being: From Precontemplation to Maintenance

Wided Batat, American University Beirut

Read More


People Overpredict the Benefit of Using Expensive Items and Appearing Rich in Friend-Making

Xilin Li, University of Chicago, USA
Christopher Hsee, University of Chicago, USA

Read More


Brand Fan(atic)s: When Excessive Brand Loyalty Sends the Wrong Signal

Isabelle Engeler, IESE Business School
Kate Barasz, IESE Business School

Read More

Engage with Us

Becoming an Association for Consumer Research member is simple. Membership in ACR is relatively inexpensive, but brings significant benefits to its members.