Integration of Analytic Hierarchy Process with Regression Analysis to Identify Attractive Locations for Market Expansion

Selection of new geographies in which to expand is a key decision for businesses aspiring to go beyond the opportunities in the existing markets. The conventional approaches of market selection can only provide a set of systematic steps for problem solving without considering the relationships between the decision factors. Decision models based on statistical techniques are able to examine the relationship between decision factors but are unable to effectively assist decision makers in identifying the most promising market, particularly in terms of prioritizing across decision factors. Analytic Hierarchy Process (AHP) is a commonly used approach for choosing alternatives by prioritizing across multiple decision factors. The typical AHP modelling requires knowledge of criteria and/or alternatives along with their relative weights, generally elicited from field experts. Quite often, firms encounter situations where decision makers are aware of only the overall objective and a set of earmarked geographies for setting up market locations while being relatively unaware of decision criteria and relative weights. This precludes using AHP to identify promising market locations. This paper conceptualizes a market selection decision model that integrates AHP with statistical modelling techniques to identify the attractive market locations for the purpose of expansion. The model first uses principal component analysis and multiple regression to determine significant decision criteria and their weights. Thereafter, it applies AHP to prioritize the market locations across the decision criteria. This integrative approach is illustrated for identifying the attractive locations in rural markets for a steel firm in India. The major advantage of this approach is that unlike the existing models, it works in situations when firms have not enough knowledge about factors for evaluating alternative market locations. Another key advantage of the proposed model is that of economizing resources for data collection on variables representing decision factors. Copyright © 2012 John Wiley & Sons, Ltd.     

The Situational Specificity of Validities: Correcting for Statistical Artifacts Does Not Always Reduce the Trans-Situational Variability of Correlation Coefficients

Validity generalization research suggests that much of the variability in validity coefficients is due to statistical artifacts, such as sampling error. Correcting for these artifacts usually reduces the trans-situational variability in validity coefficients, but the reverse can happen. Conditions under which corrections for statistical artifacts can increase the variability in validity coefficients are described, examples of meta-analyses reporting larger corrected than observed variance in validity are cited, and implications for the situational specificity of validity coefficients are discussed. In general, the true variability in validity coefficients based on large samples is often larger than the observed variability in test validities.     

The general model and the particular decision: decision makers' awareness of their cue weightings

This paper examines the accuracy with which decision makers can predict the effects of the characteristics of a research study on their evaluations of its usefulness to their work. It draws on survey data from a sample of 155 decision makers in mental health: each of them predicted the effects of 26 research characteristics on usefulness and rated each of two research studies on usefulness and the extent to which each characteristic described the study. The principal finding is that decision makers' predictions of the effects of characteristics on usefulness correspond much more closely to the effects actually observed when these effects are calculated across individuals than when they are taken for a single individual rating one (or two) studies. After examining several alternative explanations, it is suggested that an individual decision maker uses multiple evaluation functions to judge research, weighting the characteristics differently in each, and that investigators should consider the possibility of such variation whenever they attempt to model individuals' evaluation functions.     

Functional measurement approach to self-estimation in multiattribute evaluation

Multiattribute analysis depends on measurement of values and weights. Unless these measures reflect the decision maker's true values and weights, the multiattribute formula may put a less-preferred alternative in first place. To avoid such disordinality requires stringent measurement conditions: First, the values and weights must be on linear (equal interval) or ratio (known zero) scales. Second, these scales must satisfy a condition of common unit across disparate attribute dimensions. Most methods of range adjustment beg both of these measurement questions. Functional measurement theory can solve both problems and so can be useful in multiattribute analysis. Past work has established the operation of a general cognitive algebra as an empirical reality. The averaging model, in particular, makes possible the definition and estimation of weights and values as distinct psychological parameters. It can also solve the problem of common unit. Cognitive algebra thus provides a grounded theoretical foundation on which to develop self-estimation methodology, in which decision makers provide direct estimates of their values and weights. The logic is straightforward. Functional measurement can analyze global judgments to obtain validated psychological scales. These scales may then be used as validational criteria for the self-estimates. Procedures to eliminate biases in the self-estimates can thus be tested and refined in well-learned multiattribute tasks, such as judgments of meals, in which global judgments are trustworthy. Once developed, such self-estimation procedures may be used with some confidence for general multiattribute analysis. A number of studies from 20-odd years of work on the theory of information integration are summarized to show good, although not unmixed promise for self-estimation.     

A MULTIDIMENSIONAL APPROACH FOR EVALUATING VARIABLES IN ORGANIZATIONAL RESEARCH AND PRACTICE

One of the most difficult tasks facing industrial-organizational psychologists is evaluating the importance of variables, especially new variables, to be included in the prediction of some outcome. When multiple regression is used, common practices suggest evaluating the usefulness of new variables by showing incremental validity beyond the set of existing variables. This approach assures that the new variables are not statistically redundant with this existing set, but this approach attributes any shared criterion-related validity to the existing set of variables and none to the new variables. More importantly, incremental validity alone fails to answer the question directly about the importance of variables included in a regression model—arguably the more important statistical concern for practitioners. To that end, the current article reviews 2 indices of relative importance, general dominance weights and relative weights, which may be used to complement incremental validity evidence and permit organizational decision makers to make more precise and informed decisions concerning the usefulness of predictor variables. We illustrate our approach by reanalyzing the correlation matrices from 2 published studies.     

Revisiting absolute and relative judgments in the WITNESS model

The WITNESS model (Clark in Applied Cognitive Psychology 17:629–654, 2003) provides a theoretical framework with which to investigate the factors that contribute to eyewitness identification decisions. One key factor involves the contributions of absolute versus relative judgments. An absolute contribution is determined by the degree of match between an individual lineup member and memory for the perpetrator; a relative contribution involves the degree to which the best-matching lineup member is a better match to memory than the remaining lineup members. In WITNESS, the proportional contributions of relative versus absolute judgments are governed by the values of the decision weight parameters. We conducted an exploration of the WITNESS model’s parameter space to determine the identifiability of these relative/absolute decision weight parameters, and compared the results to a restricted version of the model that does not vary the decision weight parameters. This exploration revealed that the decision weights in WITNESS are difficult to identify: Data often can be fit equally well by setting the decision weights to nearly any value and compensating with a criterion adjustment. Clark, Erickson, and Breneman (Law and Human Behavior 35:364–380, 2011) claimed to demonstrate a theoretical basis for the superiority of lineup decisions that are based on absolute contributions, but the relationship between the decision weights and the criterion weakens this claim. These findings necessitate reconsidering the role of the relative/absolute judgment distinction in eyewitness decision making.     

A WITHIN SETTING EMPIRICAL TEST OF THE SITUATIONAL SPECIFICITY HYPOTHESIS IN PERSONNEL SELECTION

The situational specificity hypothesis of selection procedure validity makes two predictions. The first is that variation in observed validities across settings is caused by real differences in what constitutes job performance. Validity generalization studies to date have provided disconfirming evidence for this prediction by showing that the observed variation is due to statistical and measurement artifacts. The second prediction is that if the situation (i.e., the organization, the setting, the job, the test and the criterion) is held constant, then validity findings will not vary from study to study. This article tests this prediction empirically using data from a unique series of studies reported by Bender and Loveless (1958). The results are contrary to the situational specificity hypothesis and consistent with the hypothesis that variation in observed validities is due to statistical and measurement artifacts that are unrelated to situations and settings. It is concluded that both major predictions of the situational specificity hypothesis have now been empirically disconfirmed.     

THE VALIDITY OF THE JOB CHARACTERISTICS MODEL: A REVIEW AND META-ANALYSIS

The validity of Hackman and Oldham's Job Characteristics Model was assessed by conducting a comprehensive review of nearly 200 relevant studies on the model as well as by applying meta-analytic procedures to a large portion of the data. The evidence indicated that the available correlational results are reasonably valid in light of the issues examined. Results tended to support the multidimensionality of job characteristics, but there was less agreement on the exact number of dimensions. The corrected correlational results of the meta-analysis indicated that job characteristics related both to psychological and behavioral outcomes. Concerning psychological states, the results tended to support their mediating (e.g., intervening) role between job characteristics and personal outcomes. The pattern of correlations between the job characteristics and psychological states was less supportive of the model. Meta-analytic results demonstrated that most of the cross-study variance was due to statistical artifacts. True variance across studies was found for the job characteristics-performance relationship, however, and subsequent analyses suggested that growth-need strength moderates this relationship. Implications for potential revisions of the model and for practice are discussed.     

From individual preference construction to group decisions: Framing effects and group processes

Two choice tasks known to produce framing effects in individual decisions were used to test group sensitivity to framing, relative to that of individuals, and to examine the effect of prior, individual consideration of a decision on group choice. Written post-decision reasons and pre-decision group discussions were analyzed to investigate process explanations of choices made by preexisting, naturalistic groups. For a risky choice problem, a similar framing effect was observed for groups and individuals. For an intertemporal choice task where consumption was either delayed or accelerated, naïve groups (whose members had not preconsidered the decision) showed a framing effect, less discounting in the delay frame, opposite to that observed in individuals. Predecided groups showed a non-significant effect in the other, expected direction. In all cases, process measures better explained variability in choices across conditions than frame alone. Implications for group decision research and design considerations for committee decisions are addressed.     

Sampling Methods for Identifying Differences in Source Reliability

This study explored observer performance in using across-trial and within-trial variability information to weight sources based on reliability. Three trained observers performed a multi-element, visual signal-detection task under 3 block-type conditions: a fixed block condition and 2 random conditions. In the fixed block condition, the observers had both within- and across-trial variability information to identify differences in source reliability. The random conditions eliminated the across-trial information, leaving only within-trial variability information in 1 case and neither within- nor across-trial variability information in another case. There was a significant block-type effect. Observers could use differences in across-trial variability of individual sources to assign weights. Although there was a difference in the weights assigned to reliable and unreliable sources in the partial-random condition (in which within-trial variability information was available), only 1 participant showed a significant difference in the weight assignment relative to the full-random condition. Thus, altogether, observers were not very efficient at using within-trial variability to weight sources accordingly.     

Sampling methods for identifying differences in source reliability

This study explored observer performance in using across-trial and within-trial variability information to weight sources based on reliability. Three trained observers performed a multi-element, visual signal-detection task under 3 block-type conditions: a fixed block condition and 2 random conditions. In the fixed block condition, the observers had both within- and across-trial variability information to identify differences in source reliability. The random conditions eliminated the across-trial information, leaving only within-trial variability information in 1 case and neither within- nor across-trial variability information in another case. There was a significant block-type effect. Observers could use differences in across-trial variability of individual sources to assign weights. Although there was a difference in the weights assigned to reliable and unreliable sources in the partial-random condition (in which within-trial variability information was available), only 1 participant showed a significant difference in the weight assignment relative to the full-random condition. Thus, altogether, observers were not very efficient at using within-trial variability to weight sources accordingly.     

Assessment of Multiple Membership Multilevel Models: An Application to Interviewer Effects on Nonresponse

Multilevel multiple membership models account for situations where lower level units are nested within multiple higher level units from the same classification. Not accounting correctly for such multiple membership structures leads to biased results. The use of a multiple membership model requires selection of weights reflecting the hypothesized contribution of each level two unit and their relationship to the level one outcome. The Deviance Information Criterion (DIC) has been proposed to identify such weights. For the case of logistic regression, this study assesses, through simulation, the model identification rates of the DIC to detect the correct multiple membership weights, and the properties of model variance estimators for different weight specifications across a range of scenarios. The study is motivated by analyzing interviewer effects across waves in a longitudinal study. Interviewers can substantially influence the behavior of sample survey respondents, including their decision to participate in the survey. In the case of a longitudinal survey several interviewers may contact sample members to participate across different waves. Multilevel multiple membership models are suitable to account for the inclusion of higher-level random effects for interviewers at various waves, and to assess, for example, the relative importance of previous and current wave interviewers on current wave nonresponse. To illustrate the application, multiple membership models are applied to the UK Family and Children Survey to identify interviewer effects in a longitudinal study. The paper takes a critical view on the substantive interpretation of the model weights and provides practical guidance to statistical modelers. The main recommendation is that it is best to specify the weights in a multiple membership model by exploring different weight specifications based on the DIC, rather than prespecifying the weights.     

Effect of individual thinking styles on item selection during study time allocation

The influence of individual differences on learners' study time allocation has been emphasised in recent studies; however, little is known about the role of individual thinking styles (analytical versus intuitive). In the present study, we explored the influence of individual thinking styles on learners' application of agenda‐based and habitual processes when selecting the first item during a study‐time allocation task. A 3‐item cognitive reflection test (CRT) was used to determine individuals' degree of cognitive reliance on intuitive versus analytical cognitive processing. Significant correlations between CRT scores and the choices of first item selection were observed in both Experiment 1a (study time was 5 seconds per triplet) and Experiment 1b (study time was 20 seconds per triplet). Furthermore, analytical decision makers constructed a value‐based agenda (prioritised high‐reward items), whereas intuitive decision makers relied more upon habitual responding (selected items from the leftmost of the array). The findings of Experiment 1a were replicated in Experiment 2 notwithstanding ruling out the possible effects from individual intelligence and working memory capacity. Overall, the individual thinking style plays an important role on learners' study time allocation and the predictive ability of CRT is reliable in learners' item selection strategy.     

Individual Differences in Categorization Gradience As Predicted by Online Processing of Phonetic Cues During Spoken Word Recognition: Evidence From Eye Movements

Recent studies have documented substantial variability among typical listeners in how gradiently they categorize speech sounds, and this variability in categorization gradience may link to how listeners weight different cues in the incoming signal. The present study tested the relationship between categorization gradience and cue weighting across two sets of English contrasts, each varying orthogonally in two acoustic dimensions. Participants performed a four-alternative forced-choice identification task in a visual world paradigm while their eye movements were monitored. We found that (a) greater categorization gradience derived from behavioral identification responses corresponds to larger secondary cue weights derived from eye movements; (b) the relationship between categorization gradience and secondary cue weighting is observed across cues and contrasts, suggesting that categorization gradience may be a consistent within-individual property in speech perception; and (c) listeners who showed greater categorization gradience tend to adopt a buffered processing strategy, especially when cues arrive asynchronously in time.     

A dynamic, stochastic, computational model of preference reversal phenomena

Preference orderings among a set of options may depend on the elicitation method (e.g., choice or pricing); these preference reversals challenge traditional decision theories. Previous attempts to explain these reversals have relied on allowing utility of the options to change across elicitation methods by changing the decision weights, the attribute values, or the combination of this information--still, no theory has successfully accounted for all the phenomena. In this article, the authors present a new computational model that accounts for the empirical trends without changing decision weights, values, or combination rules. Rather, the current model specifies a dynamic evaluation and response process that correctly predicts preference orderings across 6 elicitation methods, retains stable evaluations across methods, and makes novel predictions regarding response distributions and response times.     

Toward disentangling sources of individual differences in appraisal and anger

ABSTRACT A theoretical framework is presented to explain individual differences in situation-specific emotional experience in terms of three different sources of variance: (a) individual differences in how one appraises one's circumstances, (b) individual differences in how appraisals are related to the experience of emotion, and (c) individual differences independent from situation and appraisal. The relative contribution and nature of these sources was examined empirically for the experience of anger based on data from two directed imagery studies (total N =1,192). Consistent results across the two studies demonstrated that variability in anger experience primarily stems from variability in how a situation is appraised and to a smaller extent from individual differences in the relations between the appraisals and anger and individual differences independent of appraisal. The findings further identified frustration as the central appraisal involved in anger. Implications for emotion theories and anger management programs are discussed.     

The influence of cue unreliability on judgment in a multiple cue probability learning task

The effect of cue reliability was tested in a two-cue multiple-cue probability learning (MCPL) task. Subjects were 42 undergraduates. The cue validities were.78 and.37. Unreliability in a cue was defined as variability in multiple observations of that cue on a given trial. That variability was provided by adding random errors to the true value of the cue. One group was given consistent cues (i.e., the more valid cue was the more reliable cue), one group was given inconsistent cues, and the control group was given true scores. Cue reliability did not affect subjects' consistency or achievement, but did interact with the presence of outcome feedback. The subjective weights showed that subjects thought they weighted the more reliable cue more heavily regardless of the cue validities or their actual cue weightings. There were wide individual differences in performance, illustrating the need for an idiographic-statistical approach in studying decision making.     

Who emerges as a leader? Meta-analyses of individual differences as predictors of leadership emergence

For more than sixty years, the leaderless group discussion (LGD) has been commonly used to assess leadership emergence and potential. This research focuses on individual differences as predictors of leader emergence in LGDs. Meta-analytic methods allowed us to combine and compare results from 45 studies, producing 196 effect sizes, and make statistically guided decisions about the strength of relationships between individual difference and personality variables and leadership emergence in LGDs. Consistent with more general research on leader emergence and effectiveness, we found a number of individual differences predictive of leader emergence in LGDs, most notably extraversion and authoritarian personality. This research augments previous meta-analytic research on personality and leadership with a focus specifically on the leaderless group discussion.     

Dominance Measuring Method Performance under Incomplete Information about Weights

In multi‐attribute utility theory, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. A very widespread approach is to gather incomplete information. A recent approach for dealing with such situations is to use information about each alternative's intensity of dominance, known as dominance measuring methods. Different dominance measuring methods have been proposed, and simulation studies have been carried out to compare these methods with each other and with other approaches but only when ordinal information about weights is available. In this paper, we use Monte Carlo simulation techniques to analyse the performance of and adapt such methods to deal with weight intervals, weights fitting independent normal probability distributions or weights represented by fuzzy numbers. Moreover, dominance measuring method performance is also compared with a widely used methodology dealing with incomplete information on weights, the stochastic multicriteria acceptability analysis (SMAA). SMAA is based on exploring the weight space to describe the evaluations that would make each alternative the preferred one. Copyright © 2012 John Wiley & Sons, Ltd.