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The purpose of this paper is to identify the impact of analytical customer relationship management (CRM) on salesperson information use behavior.

To achieve the aim of this paper, a vignette experiment was undertaken. The data used for the final analysis included 125 professional salespeople across multiple industries.

This paper focuses on the personal use of competitive intelligence. The authors find that to maximize the effectiveness of using competitive intelligence, the salesperson must become adept at both choosing the correct pa`rtners to trust and properly valuing information. Properly valuing information can be accomplished through the use of analytical CRM.

The managerial implications of this paper are straightforward yet important. CRM providers have improved the tools available to salespeople (e.g., heat maps) and have partnered with other large scale providers of customer and market information (e.g., global marketing research firms) to provide a analytical tool that is user friendly to salespeople. Yet, many firms still use simplified CRM platforms, which do little more for the salesperson than offer an opportunity to document notes. Sales firms should move toward this analytical CRM system because it improves the salesperson’s ability to value information and increases the salesperson’s ability to use intelligence to link products to buyer needs.

This paper contributes to theory through confirming the importance of analytical CRM on salesperson’s information use behavior by using a motivation, opportunity and ability framework. Additionally, a methodological contribution was made through the development of an information value scale.

The debate over intuitive vs analytical decision-making styles began almost 40 years ago and had yet to deliver definite answers. The debate – however – has led to divergent theoretical stances and empirical results. The purpose of this paper is to investigate the role of these information processing styles in customer-related decision-making in the context of mobile technologies.

The hypotheses are derived from the contrasting theoretical propositions and empirical evidence present in the debate around decision-making styles. The study also introduces and investigates the moderating role of environmental dynamism (ED). Analyses and results are based on survey research that involves 251 managers with responsibility for organizational decision-making processes.

The study’s findings suggest that both intuitive and analytical styles are relevant in the actual context characterized by mobile technologies. Intuition still plays a central role in managers’ decision-making processes, but when the industry environment is highly dynamic analytical information processing also plays an essential role in supporting organizational responsiveness and performance.

This study can help managers in reconsidering the way in which they employ analytical or intuitive information processing activities inside their decision making at different levels of ED.

The novelty of this paper relies on testing hypothesis simultaneously developed by both the theoretical stances favorable to intuitive and to analytical information processing. Besides, it tests these hypotheses in the actual empirical context characterized by a transformed scenario in terms of data availability.

To describe consumers’ heuristic and analytical searches for a pre‐purchase information acquisition, and to assess the correspondence of flexibility of information task and the information found with a search.

Propositions based on current research in web use and consumer studies. Tracked records of searches are used for descriptive analysis of transitional patterns in the data. Regression is used for statistical verification of the information provided by searches.

Consumer searches center on chaining events, indicating heavy reliance on hyperlink navigation between web sites. Formal searches are seldom used, although when employed, tend to have a high level of diagnosticity. The emphasis on heuristic behavior is logical, as the way consumer information is currently presented on the internet rewards for this type of behavior. Use of heuristic search increases the likelihood of access to flexibly presented information.

Consumers favor heuristic trial‐and‐error searches even in focused fact‐finding search tasks, which are typically considered the domain of analytical seeking. Consumers seem to benefit most from apparently inefficient, reactive and heuristic searches, because these are more likely to provide information in a format that the consumer can adapt. Convenience sample limits generalizability of findings.

While there is an increasing body of knowledge concerning internet use for finding information, fewer studies have focused on consumer uses of the web in search. This paper provides new information of online consumers, an increasingly important topic.

The purpose of the paper is to provide a thorough analysis of the concepts of business intelligence (BI), knowledge management (KM) and analytical CRM (aCRM) and to establish a framework for integrating all the three to each other. The paper also seeks to establish a KM and aCRM based framework using data mining (DM) techniques, which helps in the enterprise decision‐making. The objective is to share how KM and aCRM can be integrated into this seamless analytics framework to sustain excellence in decision making using effective data mining techniques and to explore how working on such aCRM system can be effective for enabling organizations delivering complete solutions.

This paper is based on focused and dedicated study of the literature present on the aCRM, KM and data mining techniques. The paper considered how to develop a strategy and operational framework that would build aCRM on the foundation of existing DM techniques and KM approach to meet the business challenges. Based on this research, a customized, integrated framework, to match the needs of business was designed.

KM focuses on managing knowledge within the organization and aCRM focuses on gaining analytical information from the customer data. Both KM and aCRM help in the decision making process and understanding. This knowledge is difficult to uncover. Hence, this paper explains the importance of data mining tools and techniques to uncover knowledge by the integration between KM and aCRM. This paper presents an integrated KM and aCRM based framework using DM techniques.

All the firms may not be in favor of adopting KM while implementing aCRM. The KM requires a convalesce of organizational culture, technology innovations, effective work force in culminating knowledge dissemination in all business domains.

The organizations implementing this knowledge enabled aCRM framework would be easily able to convert their business knowledge via the analytical CRM to solve many business issues, such as increase response rates from direct mail, telephone, e‐mail, and internet delivered marketing campaigns, increased sales and increased services. With aCRM, firms can identify their most profitable customers and use this knowledge for promotional schemes for those customers as well as identify future customers with prediction on ROI.

The need for the integration of KM and aCRM is clear. It is written for practitioners who are looking for approaches to improve business performance and maintain high profits for their business by incorporating knowledge‐enabled aCRM in their setup.

The survival and growth of organizations presently depend on managing processes and capabilities to effectively use large volumes of data from different sources to assist organizations’ strategic and operational goals. This paper aims to test the relationship between organizational analytical capabilities (OAC), the performance results in organizational resilience (OR) and the business process management maturity (BPMM).

Based on a survey of companies operating in the state of Espírito Santo, Brazil, a conceptual model was proposed and tested using the partial least squares algorithm.

The results confirm the proposed theoretical hypotheses that OAC and BPMM positively impact OR. In addition, the results show that OAC exert a moderating effect on the relationship between BPMM and OR.

It is understood that stimulating the practice of data and information analysis in the organizational routine translates into a relevant managerial behavior, as this attitude leverages the knowledge development and understanding about how to manage unexpected risk events, enabling companies to assess their ability to react to disruptions, even in terms of operational failures.

The purpose of this paper is to analyze the decision-making process and provide a decision support framework for deployment of an on-site analytical capacity (a fieldable laboratory (FL)) to contain an expanding outbreak and protect public health.

The FL mission cycle consists of five successive interlinked phases with a set of operational functions (OFs) performed during the mission. The list of phases, OFs and their contents were iteratively developed during and after FL missions and validated with operational partners.

The well-defined structure of the FL domain appears as the best functional basis for tracking the decision-making process across the whole mission cycle. Description of all the FL elements and information flows addresses the major issue of interoperability of resources used by similar international capacities (inter-)acting as operational partners in global response to the crisis.

The work presents the first attempt in this field to systematically describe and chronologically organize the decisions taken by a FL manager and staff during all phases of the FL mission cycle. Definition of OFs with all the related information flows allows for comparison of procedures, their better planning and refining, validation of protocols, mutual training and operational improvement between FLs from different geographical, organizational and cultural origins.

The purpose of this paper is to provide insight into whether GM‐labelling leads to different processing behaviour of food stimuli compared to when products are not labelled.

A task was designed to investigate people's categorization behaviour as a function of information provided. In two studies each participant was randomly allocated to either the experimental “GM‐labelled condition”, or the control “non‐labelled condition”.

Different processing strategies and different characteristics are used to judge products that are labelled as genetically modified or not. GM labelling of foods is interpreted to induce analytical processing of information and therefore the products are classified relatively more often on the basis of verifiable categorization criteria compared to when they were not labelled as GM. When products are not labelled as GM, information is more likely to be automatically processed and non‐verifiable categorization criteria are used.

This is the first study to examine the processes that labelling as GM brings about.

Activity-based costing (ABC) is presented in accounting textbooks as a costing system that can be used to make valuable managerial decisions. Little experimental or empirical evidence, however, has demonstrated the benefits of ABC under controlled conditions. Similarly, although case studies and business surveys often comment on business environments that appear to favor ABC methods, experimental studies of actual behavioral issues affecting ABCs usage are limited.

This study used an interactive computer simulation, under controlled, laboratory conditions, to test the decision usefulness of ABC information. The effects of presentation format (theory of cognitive fit and decision framing), decision commitment (cognitive dissonance), and their interactions were also examined. ABC information yielded better profitability decisions, requiring no additional decision time. Graphic presentations required less decision time, however, presentation formats did not significantly affect decision quality (simulation profits). Decision commitment beneficially affected profitability decisions, requiring no additional time. Decision commitment was especially influential (helpful) in non-ABC decision environments.

The purpose of this paper is twofold. First, in order to understand mobile customer relationship management (mCRM) and data mining application in the mCRM, this paper aims to present a conceptualization of mCRM in respect of data mining. Second, the paper also aims to develop the empirically grounded framework of the mCRM from data mining perspective.

The empirical paper is used to gain a conceptual view of mCRM. Semi‐structured interviews and contact methodology is used to form the main data source through which the major concerns and issues of mCRM are identified. This lead to holistic framework of mCRM. The paper followed the paradigm of natural science research on information technology by March and Smith and Hervner et al.

The framework identified three critical issues that are categorized as customer care information center, data store and data access systems, and mobile services and technology. The paper on various existing literatures in mCRM strategies and data mining leads to the development of the mCRM framework. The applications of methodology in data mining helped in identifying and exploring mCRM processes. The data mining based framework identifies issues related to customer attrition, customer life time value analysis and customer churn analysis while moving towards mCRM.

The suggested framework would serve as a guideline to all mCRM product vendors and will be considered as a structured consistent procedure for applying mCRM using data mining tools and techniques. The paper explored various studies in the area of mCRM and data mining and shed light on emerging issues in mCRM area. The suggested framework would give an organization, product developers, and management thinker's valuable insights on application of data mining tools and techniques in mCRM application.