Previous studies have concluded that there are significant differences in travelers’ preferences depending on the trip type. The problem of extracting users’ preferences from a corpus of text can be ...solved by using traditional clustering algorithms, which work quite well when there is no predefined data structure. However, in this paper, we consider the problem of extracting users’ preferences when they belong to a finite number of classes represented by the trip type. In this paper, we propose an encoding method based on a Convolutional Neural Networks (CNNs), trained as a classifier for the classes that predefine data structure. The intuition behind convolutional neural encoding is its ability to maximize the distance between documents belonging to different classes in the new, derived feature space. Findings reveal that CNNs encoding has better discriminative properties than alternative encoding methods such as Latent Dirichlet Allocation or average word2vec encoding. Moreover, we demonstrate that CNNs encoding can be used to identify the unique topics associated with the predefined data structure determined, in this case, by the four trip types.
•A systematic approach for deriving customer preferences from online hotel reviews is proposed.•A specific triple is defined to represent the sentiment elements of a review comment.•An algorithm is ...developed for sentiment analysis of implicit hotels’ attributes.•Word embedding, co-occurrence and dependency parsing are combined.•Online hotel reviews crawled from Ctrip.com are used to verify the proposed approach.
An increasing number of travelers like to share their experience and feelings about hotel stays through social media, generating a sheer volume of online hotel reviews. The user-generated comments contain their preferences for different aspects of hotels, which are helpful for hoteliers to improve hotels’ services. The key of deriving customer preferences from online hotel reviews is to identify fine-grained sentiment towards hotel attributes. However, the existing fine-grained sentiment analysis approaches cannot address the implicit aspect-level terms extraction very well, which is necessary to deal with the common situation that some aspects are omitted in the online reviews. To better understand customer preferences, we propose an unsupervised approach for aspect-level sentiment analysis with the implicit hotel attributes into consideration by integrating word embedding, co-occurrence and dependency parsing. A method based on overall sentiment values of hotel attributes is used to measure the customer preferences to support the hotel services analysis. Finally, online hotel reviews crawled from Ctrip.com are used to verify the proposed approach, and the results show that the hybrid approach outperforms the individual included techniques with respect to the sentiment classification performance. The analysis of customer preference for Dalian Bayshore Hotel suggests that the hotel’s facility should be upgraded urgently, and different types of customers pay different attention to hotel attributes, such as price, hygiene, and location.
•A gradual covering location problem with different facility types is examined.•Customers visit facilities based on their preferences represented as probabilities.•A fuzzy c-means clustering ...algorithm is employed in computing customer preferences.•Two binary-integer linear programming models are introduced.•Two Lagrangean heuristics and a local search procedure are developed.
In this paper, we address a discrete facility location problem where a retailer aims at locating new facilities with possibly different characteristics. Customers visit the facilities based on their preferences which are represented as probabilities. These probabilities are determined in a novel way by using a fuzzy clustering algorithm. It is assumed that the sum of the probabilities with which customers at a given demand zone patronize different types of facilities is equal to one. However, among the same type of facilities they choose the closest facility, and the strength at which this facility covers the customer is based on two distances referred to as full coverage distance and gradual (partial) coverage distance. If the distance between the customer location and the closest facility is smaller (larger) than the full (partial) coverage distance, this customer is fully (not) covered, whereas for all distance values between full and partial coverage, the customer is partially covered. Both distance values depend on both the customer attributes and the type of the facility. Furthermore, facilities can only be opened if their revenue exceeds a certain threshold value. A final restriction is incorporated into the model by defining a minimum separation distance between the same facility types. This restriction is also extended to the case where a minimum threshold distance exists among facilities of different types. The objective of the retailer is to find the optimal locations and types of the new facilities in order to maximize its profit. Two versions of the problem are formulated using integer linear programming, which differ according to whether the minimum separation distance applies to the same facility type or different facility types. The resulting integer linear programming models are solved by three approaches: commercial solver CPLEX, heuristics based on Lagrangean relaxation, and local search implemented with 1-Add and 1-Swap moves. Apart from experimentally assessing the accuracy and the efficiency of the solution methods on a set of randomly generated test instances, we also carry out sensitivity analysis using a real-world problem instance.
Research Summary
What strategies do firms use to change their customers' preferences? This paper addresses this question by developing a conceptual model that combines the representation of customer ...preferences as a demand landscape with research on marketing and psychology. I suggest that in addition to changing their products to accommodate customer preferences, firms also change the distribution of customer preferences to accommodate the firms' products. Specifically, I argue that firms alter customer preferences by adding, removing, and transforming the dimensions of the demand landscape. I illustrate the model with an historical case study of the U.S. market for mortgage‐backed securities (MBS) between 1968 and 1987, a period during which MBS issuers succeeded in reshaping the bond demand landscape to promote the acceptance of MBS as bonds.
Managerial Summary
Managers often assume that customer tastes are fixed and that the only way to improve a product's appeal to customers is to change the product's attributes to better accommodate the customers' preferences. In this paper, I consider two approaches firms can take to changing customer preferences to better accommodate their products. One approach is to convince the customers that the combination of attributes offered by the firm's product is more valuable than that in a product a customer is used to buying. An alternative approach is to manipulate the customer's perception of similarity between a product she is used to and the firm's product.
Transitions from a linear (take-make-dispose) to a circular product system (considering reuse/remanufacturing/recycling) require changes of business models through new value propositions. Therefore, ...the focus for industrial businesses shifts from selling physical products to, for example, providing access to functionality through business innovation. In this context it is particularly challenging to understand what complexity a new concept like circular economy (CE) brings to established businesses where the success and the failure of the business is dependent on customer’s acceptance of new value propositions.
The objective of this paper is to develop an algorithm based on gathered survey data to “learn” choice behavior of a small customer group and then replicate that choice behavior on a larger population level.
This paper explores the opportunities of different circular business offers in the city of Stockholm by embedding support vector machine classifiers, which are trained on CE survey data, in a simulation model to quantify and study choice behavior on city level.
Stated choices from CE surveys including unique demographic data from the respondents, i.e. age, income, gender and education, are used for algorithm training. Based on the survey data, support vector machine algorithms are trained to replicate the decision-making process of a small sample of respondents. The example of a washing machine is used as a case study with the attributes price and payment scheme, environmental friendliness as well as service level. The trained support vector machines are then implemented in a simulation model to simulate choice behavior on population level (Stockholm city).
This paper is the first of its kind to use both machine learning and simulation approaches in a CE market acceptance context.
Based on the washing machine-specific survey and Stockholm-specific customer data, results indicate that larger share of the Stockholm population would be willing to opt for circular washing machine offers compared to the existing linear sales model. Given the data-driven nature of machine learning algorithms and the process-oriented structure of simulations programs allows for generating large amounts of data from small samples. This supports exploration of new emerging areas like CE in addition to saving time and expenses.
In industrial practice a transition from a linear (take-make-dispose) to a circular product system (considering reuse/remanufacturing/recycling) requires the change of business models through new ...value propositions. In doing so the focus of the value proposition shifts from selling a physical product to providing access to functionality through business innovation. One key factor related to circular business transitions is market acceptance. It is particularly challenging to understand what complexity a new concept like circular economy (CE) brings to established businesses where the success and the failure of the business is dependent on customer's acceptance of new value propositions.
This paper empirically explores the opportunities of a circular business approach for washing machines in the city of Stockholm by quantifying and assessing customer preferences for CE value propositions for a business to customer (B2C) scenario.
This study uses the method of choice-based conjoint analysis to investigate preferences based on the attributes price and payment scheme, environmental friendliness as well as service level.
This paper is the first of its kind to assess customer preferences from the CE market acceptance point of view using a conjoint approach and provides insight to what extent new CE value propositions may be adopted.
Results indicate that there is general interest in paying for access rather than for ownership. Service levels have the strongest impact on customer utility of a washing machine offer. If associated with reduction in CO2 emissions the number of remanufacturing cycles can increase purchase probability. As a method choice-based conjoint analysis is highlighted as beneficial to break down CE value propositions and to identify to what extent particular service-related attributes and product-related attributes contribute to overall customer utility.
•Empirical study of circular business approaches using washing machines in Stockholm.•Evaluates customer preferences for circular economy value propositions.•Includes price and payment schemes, environmental friendliness and service levels.•Quantifies utility and choice probability of various washing machine offers.•Identifies general interest in service-oriented offers with environmental benefits.
Last-mile delivery plays a crucial role in improving the service level of express delivery, as it involves direct contact with customers. Providing personalized lastmile delivery services is an ...important means of improving customer satisfaction. Massive consumer data makes it possible to mine customers' personalized logistics preferences. The paper studies the vehicle routing problem in last-mile delivery considering customers' preferences. The paper first quantifies customers' preferences for delivery time, location, and mode, and obtains preference probabilities based on historical data. Then, an optimization considering customer satisfaction and enterprise delivery costs is established, and a vehicle routing problem model considering customer preferences is proposed. To solve the problem, we designed an adaptive large neighborhood search (ALNS) algorithm with virtual delivery points to solve the problem and proposed specific destroy and repair operators. Through the case analysis of an express delivery company, this article provides the optimal routs and analyzes the customer preferences on each route. In addition, this article explores the impact of the customer preference constraint and complaint constraint on cost and gives the appropriate customer preference constraint and complaint rate constraint from the perspective of cost-saving.
•We present the results of a conjoint experiment on innovative heavy-duty trucks.•Maximum driving range is the most important attribute.•Tank-to-wheel emissions is the least important ...attribute.•Freight companies are generally open to switching to innovative heavy-duty trucks.
Germany is by far the largest contributor of greenhouse gas emissions in the European Union but adopted its own climate action plan to achieve greenhouse gas neutrality by 2050. The country’s third-largest emitter of greenhouse gas emissions is the transportation sector. As of January 2019, 99.7% of heavy-duty trucks registered in Germany run on diesel while the share of alternative fuel-powered passenger cars increases steadily. Apart from rising emissions, the industry faces a growing shortage of qualified truck drivers. A solution to increasing emissions and the shortage of drivers are autonomous and alternative fuel-powered heavy-duty trucks. We employed a choice-based conjoint analysis with employees from freight companies in Germany to find out how they assess the main attributes of innovative trucks. Our results reveal that the maximum driving range is the most important attribute followed by the refueling/recharging time. Tank-to-wheel emissions, on the other hand, was ranked as the least relevant attribute. Moreover, we present customers’ preference shares for future heavy-duty trucks until 2035. According to our results, freight companies are generally open to switching from conventional to low emission and (conditionally-) automated heavy-duty trucks, however, a close collaboration between truck manufacturers, customers, infrastructure companies, and policymakers is essential to spur the penetration of autonomous and alternative fuel-powered heavy-duty trucks.
Research background:
Corporate competitiveness is closely connected with the competitive advantage. Contemporary developed European countries are characterised by being highly competitive. Thus, the ...competitive advantage is particularly bound by fulfilling the customer preferences and needs. However, there is a necessity to preserve and strengthen customer satisfaction and loyalty, in the long-run.
Purpose of the article:
In the current globalised world, there is apparent high pressure on the sustainable development of the whole society. This trend was shifted from the mere legislation scope to the society-wide discussion. However, the previous researches prove that this trend is not reflected in the real purchasing behaviour in Czechia. Thus, the main aim of this article is to explore the role of customer behaviour in the corporate competitiveness paradigm concerning the current trend of corporate social responsibility.
Methods:
Primary quantitative research is based on a questionnaire survey with Czech managers at the top and middle level. The data collection was conducted from April to June 2021 via personal interviewing. The questionnaire contained mainly close-ended questions. However, if it was relevant there were also used additional open-ended questions for a closer explanation of the question point.
Findings & Value added:
The results indicate that the Czech business mostly perceives as a key factor of corporate competitiveness innovation potential, not direct relationships with customers. However, this factor has a significant indirect connection with customer satisfaction. CSR has the lowest impact on corporate competitiveness according to the respondents. However, they predicate the upward trend of this factor, similar to the global environment.
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